{bio,medical} informatics
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Wired News
Fingering Cancer Genes"Genes have fingerprints just like fingers, which got one cancer researcher thinking.
Since the FBI uses neural networks -- a type of artificial intelligence built to imitate neuron function in the brain -- to sift through masses of computerized fingerprint data to solve crimes, why not do the same for genetic fingerprint data?"
""We trained (the neural networks) to recognize this is one cancer and this is another and this is not a cancer," Kahn said. "Eventually it learned to recognize particular features that were particular for cancer.""
"In this study, for the first time, a gene expression microarray was used to tell the difference between four unique types of cancer: neuroblastoma, rhabdomyosarcoma, non-Hodgkin lymphoma (Burkitt's lymphoma) and Ewing's sarcoma. As a group, these cancers are referred to as the small, round blue cell tumors of childhood because of the way they look under the microscope.
"This is the first time anyone has taken several different kinds of cancer, and used their gene expression patterns for diagnostic classification," Meltzer says. The data form a complex pattern of signal intensities that represent the fingerprint for each tumor type."
"The power of this method, Khan says, "is not only that we can diagnose these cancers, but in the very near future, we will be able to predict which patients will respond to treatment and which will not, and will therefore need stronger treatment.""
"The automated interpretation of gene expression data may play a crucial role in the classification and treatment of human cancers. In this paper a new computational approach to the discovery and analysis of gene expression patterns is illustrated and applied to the recognition of B-cell malignancies. Using cDNA microarrays data obtained from a previous study, an unsupervised and self-adaptive neural network model known as Growing Cell Structures is able to identify normal and diffuse large B-cell lymphoma (DLBCL) patients. Furthermore, it distinguishes patients with molecularly distinct types of DLBCL without previous knowledge of those subclasses."
"This document contains references to neural network applications in medicine. It contains some hypertext links to author information and to a few papers. Most of these links are to other WWW, gopher, and ftp sites around the world, and these links may not always be accessable. If a link is repetitively down, please send me a message. Additional links and references are welcome!"
"We seem to forget, sometimes, that the first researchers in AI that chosen medicine as a problem domain did so, not because of an interest in medicine, but because of an interest in diagnosis as an example of intelligent behavior. Medical diagnosis was one example (perhaps a poor one given that there are much simpler and easier models in other physical systems). Automated diagnosis has rarely interested the medical community, not because of a fear of removing the human element (we've already done that with our reimbursement system) or of replacing humans with machines but, more simply, because diagnosis (as most people view it), is not really the problem. Most clinicians manage some form of diagnosis and most patients are treated appropriately. What is needed is better information on the utility of information and the means to obtain it which least stresses the system. The best source of this information may, in fact, be pooled knowledge of real patients, not compiled knowledge of some particular problem domain.
Of course there are probably not 10 people in NIH who have read Krakauer's article so I don't expect to see any sorely needed policy shifts in NIH funding in the next few years."
"Medical researchers who perform prognostic modeling usually oversimplify the problem by choosing a single point in time to predict outcomes (e.g., death in five years). This approach not only fails to differentiate patterns of disease progression, but also wastes important information that is usually available in time-oriented research data bases. The adequate use of time-oriented data bases can improve the performance of prognostic systems if the interdependencies among prognoses at different intervals of time are explicitly modeled. In such models, predictions for a certain interval of time (e.g., death within one year) are influenced by predictions made for other intervals, and prognostic survival curves that provide consistent estimates for several points in time can be produced. We developed a system of neural network models that makes use of time- oriented data to predict development of coronary heart disease (CHD), using a set of 2594 patients. The output of the neural network system was a prognostic curve representing survival without CHD, and the inputs were the values of demographic, clinical, and laboratory variables. The system of neural networks was trained by backprogation and its results were evaluated in test sets of previously unseen cases. We showed that, by explicitly modeling time in the neural net work architecture, the performance of the prognostic index, measured by the area under the receiver operating characteristic (ROC) curve, was significantly improved (p<0.05)."
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Quicken.Com
IBM, Others Are Financing A Public Database on Proteins"A not-for-profit company being launched today, with backing from IBM and others, hopes to become the repository for a coming wave of biological information. But unlike leading firms, such as Celera Genomics Group in the gene-sequencing area, it plans to give away its data, Tuesday's Wall Street Journal reported.
The new venture, called Blueprint Worldwide Inc., plans to create and maintain a vast public database of information about the proteins of humans and other organisms."
"The company won't be generating the data itself, but will be consolidating the wealth of public data scattered in various databases and publications. For example Blueprint plans to enter results from some 200,000 scientific papers into the database."
"The public database, which will be accessible through the Internet, could pose a threat to for-profit companies that are trying to sell similar data."
redux [03.10.01]
"With information on the genome now rapidly becoming available, the business models for companies that sell information about the genome, such as Celera and Incyte, may soon be outmoded. Biotech companies will then have to earn their stripes the old-fashioned way: by developing blockbuster drugs. Of course, proteomics companies could arise to sell information about proteins to other drug companies, but Strosberg thinks this is a flawed approach. Given his history, he should know. "Incyte's business model," he recalls, "was originally to be an information provider. That period is over. People will not pay as much for information as they used to because so much of it is now publicly available. Information is becoming a commodity." Instead of selling information about proteins, he is focusing Hybrigenics on using its proteomics information to develop drugs, either alone or in partnership with larger pharmaceutical companies."
redux [05.19.01]
"It may seem surprising, considering the amount of publicity the Human Genome Project has garnered over the past year, but a recent Wellcome Trust survey indicates that only half of biomedical researchers using genome databases are familiar with the services provided by Ensembl and other freely available options.
Although the number of hits on the Ensembl website has doubled since the publication of the Human Genome Project’s findings in Nature in February, a questionnaire sent to 777 individuals funded by the Wellcome Trust found that only 82 used Ensembl regularly, 189 used it occasionally, and only 50 percent of those who used DNA databases regularly used Ensembl at all.
Even more surprising was the finding that of those who didn’t use Ensembl, 50 percent had never heard of it."
redux [05.09.01]
"The Internet could turnout to become the equaliser in the brave new world of research into human genetics - up to a point.
Following a fierce dispute, the data on the reading of the human genetic code has been published on the Internet to make it accessible to scientists anywhere. The result has been a flood of research projects in the developing world into data that would otherwise not have been accessible."
"During the past couple of months, the public genome databases have ben used by scientists 160,000 times in India, 61,000 times in Mexico and about 50,000 times each in China and in Brazil. The data is being accessed daily by about 10,000 organisations around the world."redux [06.29.00]
" The toolbox of biochemistry, the parts list--"the kernel," to stretch the software analogy--is shared by all organisms on the planet. In general, organisms differ from one another because of their order of gene expression or because of relatively subtle perturbations to protein structures common to all forms of terrestrial life. That is, innovation in the natural world in some sense has always followed the idea of a service and flow economy. If the environment is static, only when an organism figures out how to use the old toolbox to provide itself, or another organism, with a new service is advantage conferred.
The analogy to future industrial applications of biology is clear: When molecular biologists figure out the kernel of biology, innovation by humans will consist of tweaking the parts to provide new services. Because of the sheer amount of information, it is unlikely that a single corporate entity could maintain a monopoly on the kernel. Eventually, as design tasks increase in number and sophistication, corporations will have to share techniques and this information will inevitably spread widely, reaching all levels of technical ability--the currency of the day will be innovation and design. As with every other technology developed by humans, biological technology will be broadly disseminated."
Technology based on intentional, open-source biology is on its way, whether we like it or not, and the opportunity it represents will just begin to emerge in the next 50 years."
"Great discoveries do not necessarily make great businesses. Businesses have to sell something. Celera Genomics doesn't sell or make anything tangible. It hawks service and information. It sells access to lists of genes and computers that can sort through those messy lists. Samuel Broder, the company's executive vice president and chief medical officer, makes Celera sound like some kind of consulting company, or perhaps a library."
"In a market filled with companies that acquire knowledge and then use it to produce chemicals and drugs with immediate importance, Celera is charging an arm and a leg for a library with really nifty computers.
But the Human Genome Project, like the public library, is offering similar services for free. Certainly, its computers are less nifty. But it has a relatively good draft of the genome. A lot of companies and universities may pay for Celera's cleaner, clearer books, its faster computers, and its richer catalogs of where the genes are and what they do. But this all seems speculative. It would certainly be nice if they had an exclusive human genome to sell."
"Venter's quest could be a fable, with all sorts of morals about the power of capitalism and the importance of a single, brilliant, willful individual who used the market to shake the ivory towers of science. But those morals only hold if Celera succeeds, if business and science blend to propel the company into the future with breathtaking speed without rocketing it into the realities of the marketplace. Celera could become one of the great business success stories. It could also be a financial train wreck."
Right now, that makes it a very volatile stock."
redux [01.11.01]
"Biotech deals announced in the last few days highlight the hottest debate in the biotech world: If and how bioinformatics companies -- the likes of Gene Logic (Nasdaq: GLGC), Incyte Genomics (Nasdaq: INCY), and Celera Genomics (NYSE: CRA) -- can sustain long-term business success. Can they sell their information alone, obtain future milestone payments and royalties on drugs or diagnostics produced from their data, or must they become drug development companies themselves?"
"Currently, big pharma subscribers are paying several million dollars per year, for several years, on a non-exclusive basis. Now if we assume you are correct in that unvalidated targets are worth MUCH less than validated targets (and I'm correct in assuming that first generation genomics targets are evn more likely to be "low margin") then, considering the time it will take to bring an unvalidated target to market, Celera is ripping these guys off (well, that's a little extreme). There's absolutely no guarantee any of this data will yield blockbuster drugs and yet they can charge tens of millions of dollars for it on a non-exclusive basis. Why does pharma buy it? Because several million a year is pocket change for a lot of these companies. Why risk missing out? Suckers.;-)
I realize I'm taking an extreme stance, but it's only to make a point: Celera is making a lot of money from their subscribers. The big question is can they continue to sign companies up at a rate sufficient to fund expansion of an internal drug discovery platform. Here we may have a problem. The subscription rate has been, for most of us, disappointing. Either Celera is going to have to start validating targets (or annotating or value-adding) to attract more customers, or they are going to have to enter into significant collaborations. I think they're going to do both."
""This is the next phase for us," said Celera president J. Craig Venter, surrounded by the protein sequencers. "This is equipment that didn't exist before - it's not cutting-edge, it's bleeding-edge technology."
It was Celera's radical approach to biology that put Venter and Celera in the spotlight as the company deciphered the human genetic code at breakneck speed.
Venter plans to use the same bold methods that made Celera a biotech maverick to challenge the pharmaceutical world.
Celera, which has until now focused on selling access to its gene database, is starting to read that genetic library for clues to finding new drugs and treatments - and license those discoveries to big drug companies for potentially huge royalties."
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ZDNet
Supercomputing: Virtually fighting disease"Juno Online Services burns through US$9.6 million per quarter. But the free Internet service provider hopes its Virtual Supercomputer Project will help reverse its cash flow and keep email free, while helping scientists search the human genome for disease-fighting proteins."
"Juno already collects responses to the ads it serves up to subscribers. Using the same technology, Juno will download the mathematical tasks to subscribers' computers. The processing will be done offline, while the subscribers still have their computers on but are taking breaks. The next time the user signs onto the network, the results of the tasks will be uploaded."
redux [04.05.01]
"There's never been a better reason to ditch the flying windows or gyrating text drifting across your computer screen. Cancer researchers are offering a new computer screen saver that will use spare processor time on your computer to look for drugs to fight the disease.
Pinching the idea from the search for extraterrestrial intelligence (SETI), the researchers hope that drafting in PC users across the globe will speed up drugs discovery. "We're going to need perhaps three or four million hours of computing time," says Graham Richards of Oxford University. "Using our own computers, we'd be dead before we finished."
To look for new drugs, the team takes 3D computer models of four key proteins that seem to promote cancer - by encouraging the growth of blood vessels to supply tumours, for example. The models include all the active binding sites of the proteins. "We'd like to design little molecules that would get into these sites and block them," says Richards."
redux [02.18.01]
"A new distributed computing project is comparing gene data with protein structures to determine their genome sequences.
"Genome@home is the second project from Stanford University's chemistry department, which also runs the Folding@Home project.
"Whereas Folding@Home is designed to learn how genomes fold into proteins, Genome@Home was launched this week to try and reverse engineer known proteins by guessing the genome sequence of their structures."
redux [02.02.01]
"Facing shrinking ad revenues, ISP Juno Online is jumping into a new field that to date has enjoyed its greatest fame from the search for extraterrestrial life.
The New York-based company announced Thursday the creation of Juno Virtual Supercomputer Project, a distributed computing effort that would tap the computing power of Juno's 4 million subscribers. Juno hopes to sell that vast power to large-scale research projects, initially focusing on bioinformatics and pharmaceutical work, said Juno President and CEO Charles E. Ardai."
redux [10.09.00]
"The SETI@Home problem can be thought of as a special case of the distributed computation verification problem: "given a large amount of computation divided among many computers, how can malicious participating computers be prevented from doing damage?" This is not a new problem. Distributed computation is a venerable research topic, and the idea of "selling spare CPU cycles" has been a science fiction fixture for years.
In real life, distributed computation has been used since at least the late 1980's to create "farms" of machines for rendering 3-D images. Farms allow graphic artists to create large images without needing to buy a supercomputer. More recently, the needs of scientific computation have led to the creation of frameworks such as Parallel Virtual Machine (PVM) and Beowulf, which make it easier to distribute computations across many machines. The machines involved are usually owned by the same entity and a machine is either "good" or "bad" if it is operating or malfunctioning. There are no blatantly malicious machines.
The Internet makes it possible for computation to be distributed to many more machines. However, distributing computing around the internet requires developers to consider the possibility of malicious clients."
"The general study of secure multiparty computation has produced much interesting work over the last two decades. Less well studied, unfortunately, are the tools and techniques required to move the theoretical results to the real world. The old dream of massively distributed computations is finally coming true, and yet our tools for building and analysing real systems still seem primitive. The challenge of the next few years will be to bridge this gap."
redux [08.09.00]
BBC Screensavers could save lives
"Your computer could be helping to save lives when you are not using it to play games or surf the internet.
Instead of it sitting idle, it could be taking part in scientific experiments being distributed across thousands of computers on the internet.
Drugs to beat cancer and flu are starting to be tested in simulations split up and run on personal computers that would otherwise be doing nothing useful." [via slashdot.org]PC Magazine New Apps Exploit Connectivityredux [07.22.00]
"A natural complement to distributed file-sharing capabilities is distributed computation. The idea behind distributed computation is that a really big problem gets split into discrete, independent chunks, which are then parceled out to individual computers whose owners have volunteered their idle processor time to the cause. In aggregate, the users' computers form a sort of distributed supercomputer. The concept was first popularized by U.C. Berkeley's SETI@Home project, a 1999 PC Magazine Technical Excellence finalist that's now been downloaded by more than 2 million users. Though SETI@Home is a single-purpose tool designed solely to scour radio-telescope signals for signs of extraterrestrial transmissions, you can expect to see general-purpose mechanisms for distributing all kinds of massive computations. United Devices, for example, is a company that will use distributed computing for projects in areas such as bioinformatics research, drug design, and climate studies."
The Standard Distributed Computing Goes Commercial
"The distributed-computing model could be one of those rare cases where capitalism and pure scientific research mesh. Not every lab can afford to pay $200,000 for an eight-processor Origin 2000 SGI supercomputer, much less $1 million for a 40-processor machine, says David Fenstermacher, director of scientific computing for the medical school at the University of North Carolina at Chapel Hill. (Fenstermacher is also acting director of the campus' Center for Bioinformatics and a United Devices adviser.) And even the most powerful supercomputers need time to process data.
A project that would take several months on a supercomputer – creating a 3D model of a protein's linear be accomplished in much less time using thousands of distributed computers"
redux [04.05.00]
egroups : Decentralization Description
"* Is decentralization ever a good idea? If so, when? Is there non-anecdotal evidence on costs and benefits?
* What protocol issues are there? Can we begin assembling a good protocol for decentralized messaging? To what degree do the protocols for Freenet, Gnutella or WorldOS meet the need? Do we need an application protocol or something lower level? Can HTTP do the job? Can we implement peer routing as an add-on to existing protocols? Is there a call to develop an IETF working group?
* Given that authoring and versioning are critical but hard in a decentralized environment, how can we approach the job? Is it possible to integrate WebDAV with peer networking?
* What are the business issues? Who are the players? Who else stands to win or lose, and why?
At present many people and groups are working on the issues in isolation, some for competitive reasons and some for lack of an alternative. My belief is that a communal approach will be more productive."
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Science
Can Genes Explain Biological Complexity?[summary - can be viewed for free once registered]
" When it comes to the complexity of organisms we immediately think of behavioral or morphological complexity or perhaps wish to count the number of cells in an organism or the number of genes in the organism's genome. As Szathmáry et al. explain in their Perspective, biological complexity is not that simple. With the completed sequences of yeast, worm, fly, and human at hand, it is now clear that the number of genes cannot account for the complexity of organisms (the fly genome has about 25,000 genes and we only have about 35,000). The Perspective authors discuss whether we should think about complexity in terms of interactions among gene-regulation networks, using equations similar to those used by ecologists to determine the multitudinous interactions within food webs.”
redux [02.20.01]
[requires 'free' registration]
"Human complexity cannot be generated by 30,000 genes under the old view of life embodied in what geneticists literally called (admittedly with a sense of whimsy) their "central dogma": DNA makes RNA makes protein — in other words, one direction of causal flow from code to message to assembly of substance, with one item of code (a gene) ultimately making one item of substance (a protein), and the congeries of proteins making a body. Those 142,000 messages no doubt exist, as they must to build our bodies' complexity, with our previous error now exposed as the assumption that each message came from a distinct gene."
"The collapse of the doctrine of one gene for one protein, and one direction of causal flow from basic codes to elaborate totality, marks the failure of reductionism for the complex system that we call biology..."redux [07.11.00]
"Biology is currently undergoing a shift from a mostly qualitative to an information rich, quantitative science. Using large-scale biological technologies, we are gaining global views of structural and dynamic information in the form of whole genome sequences and the corresponding gene activity patterns at the RNA and protein level. These data reflect the molecular workings of a complex information processing system. In many ways these systems can be effectively viewed from the perspective of genetic feedback networks, given that the fundamental step of biological information flow resides in gene activation and its control through the activity of regulatory genes."
"The following sets of nine papers deals with the modeling of molecular networks, inference of functional relationships from gene activity profiles, and networks approach to structural evolution. We begin with a review by Szallasi in which he explains shy integrative approaches have been ignored in the traditional search for "dominant" molecular genetic mechanisms, and why this is no longer tenable in light of the evidence for combinatorial molecular causes for e.g. complex human diseases."
"Through centuries biological theories have been molded to conform to the view of nature established in classical physics. An apparently infinite succession of deep-rooted controversies bear witness to the fact, that this was not at all an easy fit. Vitalism, teleology or finalism have perpetually been called upon to account for living systems. But the authority of physics was such, that in the end those deviations from the ideal was always defeated - to reappear, nevertheless, in new disguise in the next generation.
With the birth of molecular biology and especially molecular genetics in the fifties and sixties a strange thing happened. Suddenly a new and very foreign vocabulary was introduced into biology, that of cybernetics or information theory. Terms like 'program', 'genetic code', 'information', 'messenger-RNA', 'feedback' and the like became respectable or even indispensable notions. Such terms, however, clearly played no role in the world view of classical physics.
This contradiction disappears when it is recognized that these new terms did not mean the same thing in biology as they did in general language. Facilitated by a widespread indifference to epistemological problems among biologists the concept of genetic information became for all practical purposes identified as the sum of the genes which carried it."
"We highly suspect the fruitfulness of this paradigm."
"For most of us, formal biology education begins with complex systems--the traditional dissection of a frog in high school biology class is virtually a rite of passage in the U.S.
But the way many people learn about and invest in biotechnology is at the smallest end of the spectrum--the genome, now often described as the "periodic table" of biology. Genomics and all its related buzzwords have been responsible for much of the media attention, government grants, and investment capital heaped on the biotech industry over the past decade.
But just as there is a whole lot of chemistry that happens in between the periodic table and a birthday cake, there is a lot of biology in between the genome and a living organism. With the completion of biology's periodic table within sight, academics and industry players alike are pondering the best way to apply our hard won knowledge.
The only problem is, the path from genome to system seems to get harder the more we learn."
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The New York Times
'The Genomic Revolution': Human Genome Gets to Meet the Family[requires 'free' registration]
"Long-winded, repetitious, immensely hard to understand, the human genome would not obviously qualify as the ideal weekend guest. But for anyone who has been putting off that inevitable one-to-one meeting, the American Museum of Natural History has prepared a most pleasant surprise in a new exhibition entitled "The Genomic Revolution." The show, which opens tomorrow, highlights the genome's elegance, power and decided air of mystery while playing down the awkward guest's most tiresome features."
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OReilly.Com
An Interview with Cynthia Gibas and Per Jambeck"What we often wished for was a manual that would outline for students all of the things they needed to learn to work in the group. We sat down and made a list, which started with Unix, Web literacy, and the ability to intelligently organize large data sets on a computer, and we included topics as wide-ranging as fundamental database principles, data visualization methods, and elementary programming concepts. Many students needed to learn or be reminded of what DNA is, how it gets translated into protein, and why the order of the characters in a 1-dimensional sequence is important in the 3-dimensional world of biology. And that was before we even got to the main bioinformatics tools: sequence analysis, molecular structure analysis, and modeling based on sequence similarity. We thought we'd better point out that bioinformatics doesn't begin and end with sequence, and that the whole landscape of biology is changing to be more information-oriented.
So, this book is for anyone who wonders what bioinformatics is and where to start on learning the skills they might need to learn to work in a bioinformatics research environment. We hope it helps." [via bioinformatics.org]
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The Washington Post
Biotech Industry Developing Worldwide Standard for Data"Worried that modern biology is producing a confused Babel of computer data, a coalition of biotechnology companies and organizations is planning to develop a worldwide standard for storing and retrieving information about the molecular details of life."
"The new coalition, led by the Biotechnology Industry Organization (BIO), a Washington trade group, plans to spend the next year or so creating a detailed specification for biological data. This specification would be available without fee to any company or scientist that wanted to use it to help organize and mine information."
The project has been dubbed the Interoperable Informatics Infrastructure Consortium, or I3C."
redux [02.21.01]
"Sun Microsystems said Wednesday it would partner with the Biotechnology Industry Organization, the National Cancer Institute, and several commercial bioinformatics vendors to support a collaborative effort to develop an open platform for the life sciences based on Java and XML.
The proposed initiative, temporarily referred to as Life Force or LI4 (Lifescience Informatics Interoperability Infrastructure Initiative) aims to develop an open platform to support data integration and interoperability and to focus the growing number of standards efforts"
"Sun intends to contribute the underlying infrastructure for the open platform, which the company hopes will form the eventual hub for a broad variety of life science computing needs, including bioinformatics, cheminformatics, genomics, proteomics, pharmacogenomics, metabolomics, and clinical informatics."
"Sun's vision in Computational Biology is based on three factors: The Sun Platform, Open Standards, and Community Support."
"As part of our efforts to support both open standards and the computational community, as well as to keep the Sun Computational Biology Community up-to-date on Sun products, we have formed a Special Interest Group in Computational Biology."
"Discussion topics for the CB-SIG will come from topics of interest to the membership, as well as Sun's Informatics Advisory Council (IAC), and the open-standards Interoperable Informatics Infrastructure Consortium (I3C), and other initiatives."
redux [03.15.01]
"Small DNA-laden wafers have transformed biology. Using these DNA chips, geneticists can see which genes are turned on, or expressed, in a cell at a particular time. Such gene expression experiments allow bioscientists to diagnose different diseases, quickly screen thousands of drug candidates for efficacy and safety and even learn the functions of newly discovered genes.
Sharing this information over the Web could lead to an explosion in biological knowledge. But each experiment generates gigabytes of data written in one of several formats, depending on the type of chip used. And with dozens of chips on the market and hundreds of ways to analyze the data, the Web is in danger of becoming a genetic Tower of Babel."
"Companies and academics have begun creating uniform formats for representing gene expression data, designed to work on any computer."
redux [10.21.00]
[summary - can be viewed for free once registered]
"As more and more genomes are sequenced, it is becoming clear that deciphering the clues latent in these sequences is anything but trivial. In this Techview, Attwood analyzes the current state of the art in sequence-structure-function bioinformatics. She highlights the need for precise terminology, and argues that a holistic view of complex biological systems will be an essential next step for bioinformatics.”
redux [07.25.00]
[requires 'free' registration]
"Once the world had a single language and not too many words, but then clarity deteriorated into clamor. Today in the small but prolific world of bioinformatics, another Tower of Babel is rising up, with the miscommunication due as much to the rapid expansion of information as to basic changes in how it is processed. "Horrible problems" crop up as more information is computed on instead of read by a human researcher, according to Ewan Birney, a group leader in the Ensembl genome annotation project at the European Bioinformatics Institute (EBI) in Cambridge, England.
In the early days of bioinformatics, human-readable data exchange formats such as ASN.1, the format adopted for GenBank by the National Center for Biotechnology Information (NCBI) 10 years ago, were the norm. Easily editable with a text utility, ASN.1's syntactic looseness makes it congenial to the human user, but not to the machine, which likes its inputs defined with dictatorial rigidity."
redux [05.10.00]
"We may rehearse this fundamental axiom of descriptive markup in terms of a classical SGML polemic: the doubly-delimited information objects in an SGML/XML document are described by markup in a meaningful, self-documenting way through the use of names which are carefully selected by domain experts for element type names, attribute names, and attribute values. This is true of XML in 1998, was true of SGML in 1986, and was true of Brian Reid's Scribe system in 1976. However, of itself, descriptive markup proves to be of limited relevance as a mechanism to enable information interchange at the level of the machine.As enchanting as it is to contemplate the apparent 'semantic' clarity, flexibility, and extensibility of XML vis-à-vis HTML (e.g., how wonderfully perspicuous XML <bookTitle> seems when compared to HTML <i>), we must reckon with the cold fact that XML does not of itself enable blind interchange or information reuse. XML may help humans predict what information might lie "between the tags" in the case of <trunk> </trunk>, but XML can only help. For an XML processor, <trunk> and <i> and <booktitle> are all equally (and totally) meaningless. Yes, meaningless.
Just like its parent metalanguage (SGML), XML has no formal mechanism to support the declaration of semantic integrity constraints, and XML processors have no means of validating object semantics even if these are declared informally in an XML DTD. XML processors will have no inherent understanding of document object semantics because XML (meta-)markup languages have no predefined application-level processing semantics. XML thus formally governs syntax only - not semantics."
redux [10.13.00]
"Imagine that your co-worker in the next cubicle has some information you need for a report that's due soon. She e-mails it to you, but the data are from a spreadsheet program, and all you have is a word processor, so there's no possibility of your cutting and pasting it into your document. Instead you have to print it out and type it in all over again. That's roughly the situation facing biologists these days. Although databases of biological information abound--especially in this post-genome-sequencing era--many researchers are like sailors thirsting to death surrounded by an ocean: what they need is all around them, but it's not in a form they can readily use.
To solve the problem, various groups made up of academic scientists and researchers from biotechnology and pharmaceutical companies are coming together to try to devise computer standards for bioinformatics so that biologists can more easily share data and make the most of the glut of information resulting from the Human Genome Project. Their goal is to enable an investigator not only to float seamlessly between the enormous databases of DNA sequences and those of the three-dimensional protein structures encoded by that DNA. They also want a scientist to be able to search the databases more efficiently so that, to use an automobile metaphor, if someone typed in "Camaro," the results would include other cars as well because the system would be smart enough to know that a Camaro is another kind of car."
"Eric Neumann, a member of both the Bio-Ontologies and BioPathways consortia, is a neuroscientist who is now vice president for life science informatics at the consulting firm 3rd Millennium in Cambridge, Mass. (no relation to Millennium Pharmaceuticals). He says Extensible Markup Language (XML) is shaping up to be the standard computer language for bioinformatics."
redux [09.15.00]
"With XML has come a proliferation of consortia from every industry imagineable to populate structured material with standard terms (see Appendix B). By one estimate, a new industry consortium is founded every week, perhaps one in four of which can collect serious membership dues. Rising in concert are intermediary groups to provide a consistent dictionary in cyberspace, in which each consortium's words are registered and catalogued.
Having come so far with a syntactic standard, XML, will E-commerce and knowledge organization stall out in semantic confusion?"
"How are semantic standards to come about?"
"There is an increasing demand for formalized knowledge on the Web. Several communities (e.g. in bioinformatics and educational media) are getting ready to offer semiformal or formal Web content. XML-based markup languages provide a 'universal' storage and interchange format for such Web-distributed knowledge representation. This tutorial introduces techniques for knowledge markup: we show how to map AI representations (e.g., logics and frames) to XML (incl. RDF and RDF Schema), discuss how to specify XML DTDs and RDF (Schema) descriptions for various representations, survey existing XML extensions for knowledge bases/ontologies, deal with the acquisition and processing of such representations, and detail selected applications. After the tutorial, participants will have absorbed the theoretical foundation and practical use of knowledge markup and will be able to assess XML applications and extensions for AI. Besides bringing to bear existing AI techniques for a Web-based knowledge markup scenario, the tutorial will identify new AI research directions for further developing this scenario."
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Conference
BiO BoF 2001: BRIE'01 & OAP'01"Bioinformatics.org is announcing its first annual "Birds of a Feather" (BiO BoF) meeting. Every year we will host a conference on a topic of interest to our community. This year, we will be co-hosting the BRIE'01 & OAP'01 joint satellite conference on July 26, at ISMB'01, in Copenhagen, Denmark.
The "Biological Research with Information Extraction" (BRIE) 2001 conference will cover text mining for biology, while the "Open-Access Publications" (OAP) 2001 conference will cover the obstacles to information retrieval and extraction.
The joint conferences will require only one registration per person. A formal announcement with more details will follow shortly. If you're planning to attend, please note that the main ISMB'01 conference runs from July 21 to 25, and the satellite conferences are either before (as with BOSC'01) or after. BRIE'01 & OAP'01 will be held on the day AFTER the main conference (on Thursday)."
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Business 2.0
Tech Giants Court The Genome Crowd"According to scientists, decoding the human genome is the most complicated civilian computational problem ever tackled, and the data generated by genomics has been doubling every six months. Proteomics eventually will generate 100 times more data than genomics and require 1,000-times more computing power.
"We don't need an evolution in computing, we need a revolution . The normal increase in CPU power is just not enough," says Marshall Peterson, vice president of infrastructure development for Celera. "This is what we call Venter's law-it states that biology will outpace Moore's law. Fast makes the difference in the very beginning of a market, but we won't be at this stage for long.""
redux [04.20.01]
"Life science is the fastest-growing sector in the market for high-performance supercomputers already, and the real boom is yet to come. "DNA is digital information," says James Pierce, a professor at Philadelphia's University of the Sciences. "Life is an expression of information; that's why it's so beautifully adaptable to computers." The computer industry is adaptable, too. IBM is rushing to sell life-sciences companies everything from supercomputers to E-commerce tools, while the burgeoning market for biochips, tiny silicon wafers embedded with genetic material, has attracted high-tech powerhouses such as Motorola, Corning, and Agilent. In the new merger of infotech and biotech, biologists and drug companies will spur advances in computing power while computer geeks will play a pivotal role in the drive to treat disease."
redux [04.01.01]
"The reason for this sudden feeding frenzy? Sales growth. Although analysts estimate that bioinformatics will grow into a $2 billion dollar industry in the next five years, most IT companies believe the payoffs will be much higher. An internal study commissioned by IBM, for instance, predicts that when the markets for high-performance computing, storage, and e-commerce combine with that of data management, the worldwide market for IT products and services in the life-sciences sector will swell to $43 billion by 2004. Looking at these kinds of numbers, "now is not the time to think small,'' says Caroline A. Kovac, vice-president of IBM's Global Life Sciences Business Unit."
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Nature: Web Debates
Evolution and scientific literature: towards a decentralized adaptive web"As our systems grow more sophisticated, we will see applications that support not just links between authors and papers but relationships between users and information repositories, and users and communities. What is required is a mechanism to enable communication between these relationships that leads to information exchange, adaptation and recombination. A new generation of information-retrieval tools and applications are being designed that will support self-organizing knowledge on distributed networks driven by human interaction (see Active Recommendation Project at Los Alamos. For example, to support trans-disciplinary science we stimulate different databases to learn new terms and adapt existing keywords to the categories recognized by different communities. This capability would allow a physicist or chemist to collaborate with colleagues in the life sciences without having to learn an entirely new vocabulary.
Through the use of these new tools, we will derive a shared knowledge structure that is based on users and usage in addition to that provided by author citations. Thus, the aggregated connections that readers make between papers and concepts will provide an alternative conceptualization of a given knowledge space. Such techniques will be coupled with classical search and retrieval methods, and these capabilities have an obvious utility for discovering and supporting evolving knowledge from these networks."
[requires 'free' registration]
"To overcome his personal difficulties, Mr. Motl could only call upon what turned out to be a remarkable internal resiliency. But his scientific challenges had a more straightforward solution: an electronic, Web-based archive centered at Los Alamos National Laboratory in New Mexico.
The archive is transforming the quality of scientific research at institutions that are geographically isolated and, in many cases, small and financially precarious. It nurtures top-flight research in countries as disparate as Bulgaria, Colombia, Cuba, Ukraine, Iran, India, Romania, Russia, Israel, the Czech Republic and Zambia."
"The Open Archives Initiative develops and promotes interoperability standards that aim to facilitate the efficient dissemination of content. The Open Archives Initiative has its roots in an effort to enhance access to e-print archives as a means of increasing the availability of scholarly communication. Continued support of this work remains a cornerstone of the Open Archives program. The fundamental technological framework and standards that are developing to support this work are, however, independent of the both the type of content offered and the economic mechanisms surrounding that content, and promise to have much broader relevance in opening up access to a range of digital materials. As a result, the Open Archives Initiative is currently an organization and an effort explicitly in transition, and is committed to exploring and enabling this new and broader range of applications. As we gain greater knowledge of the scope of applicability of the underlying technology and standards being developed, and begin to understand the structure and culture of the various adopter communities, we expect that we will have to make continued evolutionary changes to both the mission and organization of the Open Archives Initiative."
redux [09.20.00]
"How should biomedical research be communicated? How should research be assessed and validated?"
"Below are abstracts, transcripts, and biographies from the conference. Some presentations did not lend themselves to transcription. Where possible we have supplemented them with editorials from the speakers.
We have also commissioned editorial articles from several speakers and delagates at the meeting.
All thoughts, comments, and suggestions are welcome on our email discussion list"
"What if you could wave a wand, in this very Harry Potter decade, and make libraries - at least digital libraries - more open, more easy to manage, cheaper, and even more eclectic and democratic? What if content contributors could submit, catalog, index, manage, rate and rank materials in large collections themselves? I believe that, thanks to the innovations from the Open Source community and perhaps more importantly the Free Software community, that we can have a contributor-run library at this very moment.
In fact, there are several very successful examples from which we can draw not only best practices, but also - that grail of the programmer - working code. But better still, these projects are also examples of vibrant, lively, noisy, democratic communities. "
"Librarians are increasingly called upon not only to collect information in electronic form but also to organize it into digital libraries. The materials may be created and held locally, or they may be created and accessed in a distributed fashion as a virtual library. Digital libraries can provide material on a variety of topics, from children's games to high-energy physics. Their scope may be local, national, or even international; the audience may be a small group with specialized interests or the broader public. Essential to the successful implementation and use of any digital library is the organization of that library, either directly or indirectly, by one or more knowledge organization systems (KOS).
The term knowledge organization systems is intended to encompass all types of schemes for organizing information and promoting knowledge management. Knowledge organization systems include classification and categorization schemes that organize materials at a general level, subject headings that provide more detailed access, and authority files that control variant versions of key information such as geographic names and personal names. Knowledge organization systems also include highly structured vocabularies, such as thesauri, and less traditional schemes, such as semantic networks and ontologies. Because knowledge organization systems are mechanisms for organizing information, they are at the heart of every library, museum, and archive. "
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GenomeWeb
Survey Finds Only Half of Genome Database Users Aware of Free Resources"It may seem surprising, considering the amount of publicity the Human Genome Project has garnered over the past year, but a recent Wellcome Trust survey indicates that only half of biomedical researchers using genome databases are familiar with the services provided by Ensembl and other freely available options.
Although the number of hits on the Ensembl website has doubled since the publication of the Human Genome Project’s findings in Nature in February, a questionnaire sent to 777 individuals funded by the Wellcome Trust found that only 82 used Ensembl regularly, 189 used it occasionally, and only 50 percent of those who used DNA databases regularly used Ensembl at all.
Even more surprising was the finding that of those who didn’t use Ensembl, 50 percent had never heard of it."
redux [05.09.01]
"The Internet could turnout to become the equaliser in the brave new world of research into human genetics - up to a point.
Following a fierce dispute, the data on the reading of the human genetic code has been published on the Internet to make it accessible to scientists anywhere. The result has been a flood of research projects in the developing world into data that would otherwise not have been accessible."
"During the past couple of months, the public genome databases have ben used by scientists 160,000 times in India, 61,000 times in Mexico and about 50,000 times each in China and in Brazil. The data is being accessed daily by about 10,000 organisations around the world."
" The toolbox of biochemistry, the parts list--"the kernel," to stretch the software analogy--is shared by all organisms on the planet. In general, organisms differ from one another because of their order of gene expression or because of relatively subtle perturbations to protein structures common to all forms of terrestrial life. That is, innovation in the natural world in some sense has always followed the idea of a service and flow economy. If the environment is static, only when an organism figures out how to use the old toolbox to provide itself, or another organism, with a new service is advantage conferred.
The analogy to future industrial applications of biology is clear: When molecular biologists figure out the kernel of biology, innovation by humans will consist of tweaking the parts to provide new services. Because of the sheer amount of information, it is unlikely that a single corporate entity could maintain a monopoly on the kernel. Eventually, as design tasks increase in number and sophistication, corporations will have to share techniques and this information will inevitably spread widely, reaching all levels of technical ability--the currency of the day will be innovation and design. As with every other technology developed by humans, biological technology will be broadly disseminated."
Technology based on intentional, open-source biology is on its way, whether we like it or not, and the opportunity it represents will just begin to emerge in the next 50 years."
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The New York Times
Link Between Human Genes and Bacteria Is Hotly Debated[requires 'free' registration]
"In a fresh skirmish in the genome wars, a finding presented as a major discovery by the consortium of academic centers that decoded the human genome has come under attack from the camp of the consortium's rival, Celera Genomics.
The finding, one of the most surprising in the consortium's report on the human genome this February, was that 223 of the 30,000 human genes appear to have been acquired directly from bacteria. The implication was that the vertebrate and human genomes might have been shaped not just by inheritance but by weird accidents like bacterial infection of the egg or sperm cells."
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GenomeWeb
Proteomics Expert Says Technology Lags Corporate Promises"Many proteomics companies are promising more than they can deliver because they don’t yet have the technology to isolate and characterize low abundance proteins, Ian Humphery-Smith, a protein chemist at the University of Utrecht and the organizer of the Human Proteome Organization, said Thursday.
While companies, such as Large Scale Biology and Myriad, have touted the ability of their technology to pick out most of the proteins in certain cells, they haven’t identified more than five percent of the proteins in the human body, Humphery-Smith said."
"Humphery-Smith said protein microarray technology is the most likely to discover therapeutically important proteins because it will allow researchers to precisely study how the proteins in cells change with disease."
redux [04.28.01]
"Myriad Genetics made a splash earlier this month when it announced a collaboration with Hitachi and Oracle to "map the human proteome" by 2004.
The scope of this ambitious task could vary widely, depending on how the companies want to define "map" and how thorough they want to be. But regardless of how Myriad and its collaborators define the outer limits of the project, proteomics experts are questioning how much scientific value there is Myriad's basic approach.
These scientists say Myriad's two pronged attack on the proteome, an automated yeast two-hybrid strategy combined with MALDI-TOF mass mass spectroscopy analysis of protein complexes, won' t provide Myriad and its partners with a thorough understanding of how proteins play a role in disease."
redux [03.31.01]
[requires 'free' registration]
"A commonly expressed opinion is that a single Human Proteome Project can never match HGP's success. Eric S. Lander , director of the Whitehead Center for Genome Research in Cambridge, Mass., notes that biologists simply don't know how to characterize the proteome "from end to end, nailing every protein. The tools are not ready. And it's not clear that [such a project] makes sense." He contrasts proteomics to HGP where "there is a certain fixed number of base pairs--about three billion--and we were going to get them all. And so it had a beginning and an end to it."
redux [01.31.01]
"Can sequencing do for the proteome what it did for the genome?
On Wednesday, a number of world-renowned researchers in the field of proteomics issued a resounding " no."
Instead of devoting their efforts to decoding the human proteome, proteomics researchers should focus on developing a larger picture of protein structure, function, and pathways within cells and organisms, panelists said at a New York Academy of Sciences briefing entitled "The Promise of Proteomics."
"When a company has phenomenal success with strategy A, you want to do strategy A on the next subject," said John Richards, a professor of organic and biochemistry at California Institute of Technology, referring to current corporate attempts to map the proteome.
"This doesn't work," he said."
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CNN
Scientists question fruitfly genome accuracy"Scientists in the United States Wednesday questioned the accuracy of the fruitfly genome sequenced by Celera Genomics, saying there are numerous and significant discrepancies."
" "It turns out that more than 50 percent of the sequences that were previously known disagree substantially by more than one percent (with Celera's)."
Karlin and his colleagues' comparison showed that about 26 percent of the genes from Celera's fruitfly genome were a perfect match with the database information. Another 29 percent were nearly perfect but 45 percent had mismatches, insertions or deletions."
"Karlin claims there could be worse errors in both the public and private versions of the human genome. "Everyone was rushing," he says."
"Meanwhile, Karlin warns researchers to take care if they're studying any "new" genes revealed by Celera. "My advice is to do whatever part you're working on again," he says. His analysis reveals the perils of relying too much on computers, Karlin says. "People are trying to get away without doing experiments.""
"Gerald Rubin, the University of California at Berkeley geneticist who worked with Celera on the fruit-fly sequence, said the discrepancies are well known, and getting half of the proteins right is comparable to a baseball player hitting .500.
“I take that as a compliment. If you had said beforehand that this was the number, I would have said Celera should get a gold star,” Rubin said. He also said that nine out of 10 discrepancies found by Karlin stem from errors in the Swiss database, not in Celera’s work."
"Celera president J. Craig Venter has dismissed the criticism of his company's work.
"There's two ways to get ahead in science: One is to do something significant, and the other way is to criticize someone who has done something significant. We've chosen the former, some of our critics have chosen the latter," he said last week.
Larry Thompson, a spokesman for the National Human Genome Research Institute, which helped fund Celera's fruit fly work, said Karlin's findings were not surprising.
"They did a pretty good job if half of it is right the first time around," Thompson said."
"Karlin said he set out to indicate the dangers of rushing publication of sequenced genomes. “Since they knew about the errors, they should have spent maybe another six months resolving these differences, but they wanted to get the genome published,” he said.
Karlin added that he has spoken to a number of Drosophila researchers, “and they’re sort of split. Half of them think it’s a very good thing to have the genome as early as they have it and they’ll worry about how to use it, and the other half are saying it’s a very good thing to have these cautionary articles to make them aware they have to be more careful.”"
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BioMedNet
Cells in cyberspace promise biology real understanding[requires 'free' registration]
""We in physics are used to studying complex systems, but the level of complexity inherent in biological systems ... is way beyond what we have experience dealing with," said Rajagopal, assistant director of research at the Cavendish Laboratory in the University of Cambridge. "Biological systems are much harder to model as they are in highly non-equilibrium states and you not only have to take into account the flow of matter and energy, but also the flow of information!"
He added: "In biology, the whole is greater than the sum of its parts. We have to move on from the "reductionist" towards an "integrationist" approach.
"With viable computer models of biological systems, we can hope to evaluate hypotheses regarding the behaviour of these systems in their normal and diseased states," said Rajagopal."
"Our Mission is to develop an integrated, easy-to-use environment, the workbench , which will enable biologists to create, manipulate, display and analyze biological models at molecular, cellular and multicellular levels. We are focusing on biochemical networks including mass action kinetics, metabolic pathways, stochastic simulation, gene expression and regulation."
"One of the key aspects of out project is to facilitate collaboration among existing developers and users of system biology software. We aim to do this by providing an open-source software infrastructure which will enable collaborators to freely use and share each other's computational resources."
redux [02.16.01]
"Computers capable of mimicking life have long been the stuff of sci-fi nightmares—think The Terminator or 2001's HAL 9000. But for researchers struggling to make sense of vast amounts of new biological data, and for drug companies anxious to cut costs and speed development, having accurate computer simulations of living systems is still a dream. To make that dream come true, they are turning to "in silico biology," building computer models of the intricate processes that take place inside cells, organs, and even people. The ultimate goal: an entire organism modeled in silicon, allowing researchers to test new therapies much as engineers "fly" new airplane designs on supercomputers."
redux [11.27.00]
"What do a Boeing 777 and the human body have in common? Both are complex systems, dependent on millions of complex parts, whether they be a jet-propelled engine or a pumping organ such as the heart. The big difference: Engineers can design and build highly accurate computer models of the way a Boeing 777 will behave in flight. The human heart? Its complexity has long stymied efforts by researchers intent on turning drug development into a predictive science, much like building airplanes.
But that's changing. A handful of companies are developing software that can model single cells, whole organs, cellular metabolism and toxicology, diseases throughout a patient's body, and even an entire clinical trial."
redux [02.24.00]
[requires 'free' registration]
"For a brief period, supplying the data was enough. More genes meant more potential drug targets. But now the victims of the data flood are crying for help. Companies like Entelos, Inc. (Menlo Park, California) are coming to the rescue by building models that integrate all those data into a single, homeostatic, interconnected whole. The models allow researchers to run virtual drug trials to determine the best drug targets, treatment regimens, and patient populations."
Modelers feel that their time has come. "Leaders in the genomics field are all coming to this realization