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Finding new diseases for known cures. By Joseph Hall. Toronto Star (August 21, 2007). "The cure for an emerging outbreak may already be in your medicine cabinet. And with an artificial intelligence computer program he's creating, a Canadian researcher is honing in on that link. ... [Artem Cherkasov, a University of British Columbia chemist] says there are untold numbers of drugs on the market that may have more than one pharmaceutical function. His program aims to identify those additional medical capabilities and match them up against emerging infectious ailments. 'My program predicts in a virtual world ... what the chances are for all those tens of thousands of substances to be antibiotic,' he says. ... Cherkasov, who has no connection to any drug manufacturer, says his software has been programmed to 'learn' which substances may look like antimicrobial drugs. Testing a set of drugs with known antibiotic properties against a set of other substances, Cherkasov has 'trained' his program to distinguish, with 95 per cent accuracy, which agents might fight infectious ailments and which will not. 'And once you're happy with your training accuracy, then you can just pour in unknown compounds,' says Cherkasov, an assistant professor in UBC's infectious disease department. 'And your pre-trained artificial intelligence system will assign probabilities for the unknown compound to be either antibiotic or not.'"
Brains, cancer and computers. By Daniel Winterstein. The Register (August 16, 2005). "The race is on to apply machine learning to biology. The starting gun was fired in 2002 when research company Correlogic stunned the medical world with the announcement of a vastly improved test for detecting ovarian cancer. The new test was simple - a few drops of blood are all that's required - yet reliable. What made it truly remarkable was that the test was discovered by machine. This formed a key theme at this month's International Joint Conference in AI (IJCAI) at Edinburgh. The computer program BLAST, which searches genetics databases looking for similar gene sequences, is now ubiquitous in genetics research. ... This is the new mechanised biology, created by a combination of developments. Modern biology - especially genetics, molecular biology and medicine -- throws up vast amounts of data. These are now available in various vast international databases. Put this together with advances in statistical artificial intelligence (AI), and the conditions are ripe for the creation of a new subject. Known as bio-informatics (the word has become ubiquitous in AI project proposals), it is the application of computers to biology." Data Mining in Bioinformatics. Guest Editors' Introduction by Jinyan Li, Limsoon Wong, and Qiang Yang. IEEE Intelligent Systems 20(6): 16 -18 (November / December 2005). "Bioinformatics and data mining provide exciting and challenging research and application areas for computational science. Bioinformatics is the science of managing, mining, and interpreting information from biological sequences and structures. Advances such as genome-sequencing initiatives, microarrays, proteomics, and functional and structural genomics have pushed the frontiers of human knowledge. In addition, data mining and machine learning have been advancing in strides in recent years,with high-impact applications from marketing to science. Although researchers have spent much effort on data mining for bioinformatics (see the sidebar), the two areas have largely been developing separately This special issue aims to bridge the gap between bioinformatics and data mining by presenting research integrating the two." Biomedical Informatics: The Nature of the Discipline. By Edward H. Shortliffe, MD, PhD, Professor of Biomedical Informatics, Arizona State University, Phoenix. "Biomedical informatics is intrinsically entwined with the substance of biomedical science. It determines and analyzes the structure of biomedical information and knowledge, whereas biomedical science is constrained by that structure. Biomedical informatics melds the study of biomedical computer science with analyses of biomedical information and knowledge, thereby addressing specifically the interface between computer science and biomedical science. ... Work in biomedical informatics is inherently motivated by problems encountered in a set of applied domains in biomedicine. The first of these historically has been clinical care (including medicine, nursing, dentistry, and veterinary care), areas of activity that demand patient-oriented informatics applications. I refer to this area as clinical informatics. Closely tied to clinical informatics is public health informatics, in which similar methods are generalized for application to populations of patients rather than to single individuals. Thus clinical informatics and public health informatics share many of the same methods and techniques. Two other large areas of application overlap in some ways with clinical informatics and public health informatics. These include imaging informatics ['also called structural informatics at some institutions'] (and the set of issues developed around both radiology and other image-management and image-analysis domains such as pathology, dermatology, and molecular visualization). Finally, there is the burgeoning area of bioinformatics [see footnote], which at the molecular and cellular level is offering challenges that draw on many of the same informatics methods as well. As is shown in the diagram, there is a spectrum as one moves from left to right across these application domains. In bioinformatics, workers deal with molecular and cellular processes in the application of informatics methods. ..."
Bioinformatics - some definitions:
Bioinformatics Predecessors. From Columbia University Biomedical Informatics. "The field that is currently called Bioinformatics (see link for alternative definitions of this research area) emerged recently as a merger of several distinct scientific disciplines: Structural biology... Statistical genetics... Statistical evolutionary biology... Genomics-oriented computer science... Genomics-oriented statistics... Biochemical kinetics analysis... Medical informatics ..." Center for Biomolecular Science and Engineering, University of California, Santa Cruz. "The Mission of the CBSE is to foster research and education intended to meet the challenges of the post-genomic era resulting from completion of the Human Genome Project and the sequencing of model organisms. The revolutionary technologies that have recently been developed to gather and analyze genomic information will help to forge a new understanding of biology, with widespread applications to medicine, agriculture, and ecology. These technologies have been made possible by developments in structural biology, engineering, and computer science, and their further advancement requires a new blend of computational analysis, micromechanical robotics, microfluidics, bioelectronic chips, imaging, and new laboratory methods for functional genomics."
Bioinformatics moves into the mainstream - An explosion of data is being tamed with new systems. By Jennifer Ouellette. The Industrial Physicist (December-January 2003/2004; Volume 9, Issue 6). "[G]enome mappings, those completed and those in progress, have generated a vast amount of biological data, and now more than ever, scientists need sophisticated computational techniques to make sense of it. To meet those ever-increasing needs, bioinformatics is shifting from software designed for a specific project in academic laboratories to the commercial mainstream. Bioinformatics is an interdisciplinary research area loosely defined as the interface between the biological and computational sciences. In practice, the definition is narrower, according to Michael Zuker, a professor of mathematical sciences at Rensselaer Polytechnic Institute (RPI) in Troy, New York. For Zuker and many others, the term applies to the use of computers to store, retrieve, analyze, or predict the composition or structure of biomolecules. These include genetic materials such as nucleic acids, as well as proteins, the end products of genes. ... The need to manage and analyze this data largely drives the current bioinformatics boom. 'Biology is awash in data,' says [Eric] Jakobsson. 'We cannot exploit the body of data that is currently out there -- we cannot mine it -- without computers, and now we cannot even handle the data in our own individual labs without sophisticated computation.'" Diverse Sciences Propel Bioinformatics. By Jessica D. Tenenbaum. eWeek (August 20, 2004). "At conferences in computational biology, speakers generally start with questions: 'How many people in the room are biologists? Computer scientists? Other?' It can be hard to predict what kinds of experts will show up in the audience. This year's Computational Systems Bioinformatics Conference, the third of its kind, was no exception. The CSB 2002 Web site described the conference's goal as bringing together 'biology and computer science' experts. This year, the conference organizers hope to 'promote a systems biology approach that links biology, computer science, mathematics, chemistry, physics, medicine and engineering.' That's five new disciplines in two years. Even so, we've left out statistics. ... One is struck both by how far the field has come in a relatively short period of time, and also by how far it has yet to go. In the past 10 years, the numbers of sequences stored in public databases such as GenBank, SwissProt and even the Protein Data Bank all have increased exponentially. ... The conference agenda itself highlighted how interdisciplinary this field is. ... Other presentations included methods from high-throughput microscopy, text processing, data mining, artificial intelligence and more. Fusions of fields are not just expected but required. Stephen Wong of Harvard University explained how to use robotic automation and digital microscopy to screen thousands of cells simultaneously for, among other tasks, high-throughput drug screening." Iridescent Software Illuminates Research Data. By Mike Martin. Sci-Tech Today (January 27, 2004). "Bioinformatics researchers at the University of Texas (UT) Southwestern Medical Center have developed Iridescent, a software program that helps scientists easily identify obscure commonalities in research data and directly relate them to their own work, saving money and speeding the process of discovery. 'This work is about teaching computers to 'read' the literature and make relevant associations so they can be summarized and scored for their potential relevance,' said Dr. Jonathan Wren, a researcher in the department of botany and microbiology at the University of Oklahoma. 'For humans to answer the same questions objectively and comprehensively could entail reading tens of thousands of papers.' ... Iridescent is unveiled in the current issue of the journal Bioinformatics"
A Machine With a Mind of Its Own - Ross King wanted a research assistant who would work 24/7 without sleep or food. So he built one. By Oliver Morton. Wired Magazine (August 2004, Issue 12.08). "The 'robot scientist' (King has resisted the temptation of a jazzy acronym) may look like a mere labor-saving gizmo, shuttling back and forth ad nauseam, but it's much more than that. Biology is full of tools with which to make discoveries. Here's a tool that can make discoveries on its own."
Artificial Intelligence and Heuristic Methods for Bioinformatics - A NATO Advanced Studies Institute. San Miniato, Italy (October 1-11, 2001). "Molecular biologists are currently engaged in some of the most impressive data collection projects. Recent genome-sequencing projects are generating an enormous amount of data related to the function and the structure of biological molecules and sequences. Other complementary high-throughput technologies, such as DNA micro-arrays, are rapidly generating large amounts of data that are too overwhelming for conventional approaches to biological data analysis. The interpretation of this wealth of data may deeply affect our understanding of life at the molecular level, but the elicitation and the representation of biological knowledge are extremely challenging tasks, which are increasingly demanding powerful and sophisticated computational tools. AI and heuristic methods (in particular machine learning and data mining, cluster analysis, pattern recognition, knowledge representation) can provide key solutions for the new challenges posed by the progressive transformation of biology into a data-massive science. Important problems where AI approaches are particularly promising (and often already very successful) include the prediction of protein's structure and function, semiautomatic drug design, the interpretation of nucleotide sequences, and knowledge acquisition from genetic data."
Two Computational Biology courses from the University of Wisconsin:
The Interactions Between Clinical Informatics and Bioinformatics - A Case Study. By Russ B. Altman, MD, PhD, Stanford Medical Informatics, Stanford University. Journal of the American Medical Informatics Association 7:439-443 (2000) / available from PubMed Central. "For the past decade, Stanford Medical Informatics has combined clinical informatics and bioinformatics research and training in an explicit way. The interest in applying informatics techniques to both clinical problems and problems in basic science can be traced to the Dendral project in the 1960s. Having bioinformatics and clinical informatics in the same academic unit is still somewhat unusual and can lead to clashes of clinical and basic science cultures. Nevertheless, the benefits of this organization have recently become clear, as the landscape of academic medicine in the next decades has begun to emerge. The author provides examples of technology transfer between clinical informatics and bioinformatics that illustrate how they complement each other." Proceedings of the AISB 2000 Sumposium on Artificial Intelligence in Bioinformatics. One of the many convention proceedings available from The Society for the Study of Artificial Intelligence and Simulation of Behaviour (SSAISB). A robot that likes to play with test tubes. By David Akin. The Globe & Mail (January 17, 2004). "[The Robot Scientist] probably will become a vital tool for researchers, particularly in biological fields, to advance human knowledge. That is because in many scientific areas, such as nanotechnology, molecular genetics and the exploration of space, information is being generated too fast for humans to analyze it effectively. 'Biology is in a great data-gathering phase at the moment, a bit like it was in the 19th century,' said Stephen Oliver, a professor and genomics researcher at the University of Manchester in England and another of the eight researchers. The Human Genome Project, the monster science project that identified and explained the function of the genes in a human being, made great use of computers and sophisticated software programs to automate the scientific discovery progress. Indeed, there is now a branch of artificial intelligence research devoted to scientific discovery." The race to computerise biology. The Economist Technology Quarterly (December 12, 2002). "It is in data mining, however, where bioinformatics hopes for its biggest pay-off. First applied in banking, data mining uses a variety of algorithms to sift through storehouses of data in search of 'noisy' patterns and relationships among the different silos of information. The promise for bioinformatics is that public genome data, mixed with proprietary sequence data, clinical data from previous drug efforts and other stores of information, could unearth clues about possible candidates for future drugs." Technologies in Genomic Research. By Peter Gwynne and Guy Page. Science (special advertising supplement;January 28, 2000). "Making the jump from raw sequencing data to a functional understanding of the way life works involves more than a few inspired ideas and back-of-the-envelope calculations. Because of the vast mountains of genetic data assembled by genome sequencers, the issue has taken on a needle-in-the-haystack coloration. Fortunately, information technologists have spent several years grappling with the problems of assembling data banks and distilling meaning from the huge amounts of data these repositories contain.Data mining programs use a variety of advanced technologies, including artificial intelligence and neural networks, to identify significant clusters of data from among the info-rubble." The Bioinformatics Gold Rush. By Ken Howard. Scientific American (July 2000). "A $300-million industry has emerged around turning raw genome data into knowledge for making new drugs. ... Researchers are generating gigantic databases containing the details of when and in which tissues of the body various genes are turned on, the shapes of the proteins the genes encode, how the proteins interact with one another and the role those interactions play in disease. Add to the mix the data pouring in about the genomes of so-called model organisms such as fruit flies and mice, and you have what Gene Myers, Jr., vice president of informatics research at Celera Genomics in Rockville, Md., calls 'a tsunami of information.' The new discipline of bioinformatics -- a marriage between computer science and biology --seeks to make sense of it all. In so doing, it is destined to change the face of biomedicine. 'For the next two to three years, the amount of information will be phenomenal, and everyone will be overwhelmed by it,' Myers predicts. 'The race and competition will be who can mine it best. There will be such a wealth of riches.'" Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models. By Shaun Mahony, James O. McInerney,Terry J. Smith, and Aaron Golden. BMC Bioinformatics. 2004; 5 (1): 23 / available from PubMed Central. . "This paper aims to show how the Self-Organizing Map neural network algorithm can be used to automatically identify the major trends in oligonucleotide variation in a genome, and in doing so provide multiple gene models for use in gene prediction." Questions and Answers: Proteomics and Cancer. Fact sheet from the National Cancer Institute (updated 09/29/2005). "The discovery of protein patterns that distinguished ovarian cancers patients from those without disease relied on a sophisticated artificial intelligence computer program developed by Correlogic Systems, Inc., Bethesda, Md. Scientists were able to 'train' the computer to identify a pattern of only a handful of small proteins from thousands of candidates found in the blood that could distinguish cancer patients vs. control samples. Once these patterns were found, they were tested on other blinded samples from patients with and without cancer. Fifty out of 50 cancers and 63 of 66 non-cancer samples were correctly identified. These results suggested that proteomic technology may help clinicians diagnose the disease much earlier than current methods."
A Map That Maps Gene Functions. By Kristen Philipkoski. Wired News (5/28/02). "The genetics revolution is generating such a gigantic glut of information that artificial intelligence may be the only way scientists will ever put it to practical use. Inspired by an AI effort to record all of the common-sense knowledge shared among humans called Cyc, scientists have come up with a technology that can gather all of the information scientists know about an organism. ... Working with Doug Lenat -- who started the Cyc common-sense project in 1985 -- inspired Karp to apply some of the Cyc artificial intelligence techniques; namely, using knowledge representation to map metabolic pathways in organisms. ... 'Artificial intelligence comes in when you can use the tool interactively and ask very advanced queries. Others just implement perl scripts that are, I'd say, dumb,' said Lukas Mueller, a researcher at the Carnegie Institution and a curator of the Arabidopsis Information Resource." "PubMed Central (PMC) is the U.S. National Library of Medicine's free digital archive of biomedical and life sciences journal literature." To get started, enter "artificial intelligence" in the seach box. The Computer Meets Medicine and Biology: Emergence of a Discipline. By Edward H. Shortliffe and Mardsen S. Blois. Chapter 1 of the Third Edition (May 2006) of Biomedical Informatics: Computer Applications in Health Care and Biomedicine, edited by Edward H. Shortliffe and James J. Cimino. Springer. "[T]he enormous technological advances of the last two decades -- personal computers and graphical workstations, new methods for human-computer interactions, innovations in mass storage of data, personal digital assistants, the Internet and the World Wide Web, wireless communications -- have all combined to make routine use of computers by all health workers and biomedical scientists inevitable. A new world is already with us, but its greatest influence is yet to come.This book will teach you both about our present resources and accomplishments andabout what we can expect in the years ahead."
Software tracks proteins inside living cells. By Tom Simonite. New Scientist Tech News (June 14, 2006). "A computer system that automatically tracks the movements of proteins within a living cell has been developed by a team of biologists and computer vision experts. It could save researchers the hours often spent analysing microscope images by hand, to determine the way a cell works. The system, called CellTracker, automatically analyses a series of still digital images captured through a microscope. Doug Kell at Manchester University in UK, the lead biologist involved with the project, believes the system could dramatically speed up studies of cells' function. ... The system uses image recognition algorithms to identify the membrane marking the edge of a cell as well as the one enclosing the nucleus, which contains the cell's DNA. ... The software has been publicly released for other researchers to use."
Bioinformatics at the NIH. "Welcome to the NIH Bioinformatics Web Site - Your Source of Information about Biomedical Computing at the National Institutes of Health.What is Bioinformatics? - Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data.What is Computational Biology? - The development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems. (Working Definition of Bioinformatics and Computational Biology - July 17, 2000). ... The Web site contains information on the BISTI Consortium, bioinformatics News and Events, a Calendar of related events, biomedical computing Symposia, Funding Opportunities in bioinformatics, and General Information about the field." Bioinformatics and Pattern Discovery Group at IBM's Computational Biology Center. You can even experiment online with TEIRESIAS, a pattern discovery tool. Bioinformatics Research at AIAI, the Artificial Intelligence Applications Institute at the University of Edinburgh's School of Informatics. "The increasing amount of biological data being made available on-line, combined with the already vast numbers of research papers available electronically, makes Bioinformatics an exciting area for the application of Artificial Intelligence techniques." Bioinformatics Research Group at SRI International. Biomedical Informatics at Columbia University. "Biomedical Informatics is the scientific field that deals with the storage, retrieval, sharing, and optimal use of biomedical information, data, and knowledge for problem solving and decision making. It touches on all basic and applied fields in biomedical science and is closely tied to modern information technologies, notably in the areas of computing and communication." Be sure to see:
Biomedical Literature (and text) Mining Publications, BLIMP, "covers all publications related to the fast-growing field of biomedical literature and text mining. It is a one-stop resource, letting researchers find out who-does-what in the area and where it is published, bridging across the many discipline-specific venues in which biomedical text-mining papers are published." "BioTools Incorporated possesses world class ability in bioinformatics: the marriage of the two most important sciences of today; biotechnology and computer science." Center for Computational Research (CCR), University at Buffalo - State University of New York. Their collection of resources for high school students and teachers includes:
Computational Bioinformatics Laboratory (CBL) at Imperial College.
Computational Biology Group and Wolfson Bioinformatics Unit at the University of Wales, Aberystwyth. GeneScene project at The University of Arizona Eller College of Management's Artificial Intelligence Lab. "The research goal of GeneScene is to develop novel machine learning and Natural Language Processing (NLP) techniques [including data mining and text mining techniques] to support efficient and effective data and text analysis in biomedical fields, particularly, the analysis of genetic regulatory pathways which is crucial for a thorough understanding of biological processes such as gene regulation and cancer development." Health Informatics World Wide. Maintained by Stefan Schulz and Stefan Schlachter of the Medical Informatics Department of the Freiburg University Hospital. Indexed links (by county and topic, including AI) as well as information about upcoming events. Journal of Biomedical Informatics (formerly Computers and Biomedical Research). National Centre for Text Mining (NaCTeM): "We provide text mining services in response to the requirements of the UK academic community. Our initial focus is on applications in the biological and medical domains, where the major successes in the mining of scientific texts have so far occurred. We also make significant contributions to the text mining research community, both nationally and internationally."
"The probabilistic and statistical inference group at the University of Toronto develops new computational machine learning tools and theoretical frameworks for analyzing large-scale data sets, in the following application areas: Molecular Biology.... The research focus of members of the PSI-Group is on introducing principled algorithms that reveal hidden variables and efficiently take into account the structural knowledge that is critical in most real-world applications." Transinsight's GoPubMed, "an ontology-based search engine for the life sciences. In contrast to classical search engines it can answer questions using its background knowledge. ... GoPubMed can be configured for background knowledge in molecular biology, medicine, drug development, and food science. It can search literature repositories, web sites, your intranet and your desktop. GoPubMed is developed in collaboration with the Tecnical University of Dresden. We are a young, dynamic, international team of computer and life scientists with a mission to bring cutting-edge research in ontologies and text mining to the global market."
UK Bioinformatics Forum - promoting UK bioinformatics. What is Bioinformatics? "It is defined here as an interdisciplinary research area that applies computer and information science to solve biological problems. However, this is not the only definition. The field is being defined (and redefined) at present, and there are probably as many definitions as there are bioinformaticians (bioinformaticists?). The following references are a snapshot of the moving target named bioinformatics. ... " From the University of Minnesota Graduate Program in Bioinformatics. Other References Offline AI and Bioinformatics. AI Magazine 25(1); Spring 2004. Articles include: AI and Bioinformatics, by Janice Glasgow, Igor Jurisica, and Burkhard Rost; Life and Its Molecules: A Brief Introduction, by Lawrence Hunter; Using Machine Learning to Design and Interpret Gene-Expression Microarrays, by Michael Molla, Michael Waddell, David Page, and Jude Shavlik; Annotating Protein Function through Lexical Analysis, by Rajesh Nair and Burkhard Rost; Applying Inductive Logic Programming to Predicting Gene Function, by Ross D. King; Toward Automated Discovery in the Biological Sciences, by Bruce G. Buchanan and Gary R. Livingston; Applications of Case-Based Reasoning in Molecular Biology, by Igor Jurisica and Janice Glasgow; Representation of Protein-Sequence Information by Amino Acid Subalphabets, by Claus A. F. Andersen and Søren Brunak. Artificial Intelligence and Molecular Biology. Edited by Lawrence Hunter (1993). The entire book is now available online from AAAI's Classic Books in AI collection.
Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems. By Edward Keedwell, Ajit Narayanan. Wiley (2005). The table of contents can be viewed online. The Joshua Lederberg Papers, part of the National Library of Medicine's Profiles in Science archival collection, contains a wealth of information about DENDRAL, including:
Bioinformatics and Medical Informatics: Collaborations on the Road to Genomic Medicine? By Victor Maojo, MD, PhD and Casimir A. Kulikowski, PhD. J Am Med Inform Assoc. 2003 Nov; 10(6): 515-522 / available from PubMed Central. Molecular Treasure Hunt - A software tool elicits previously undiscovered gene or protein pathways by combing through hundreds of thousands of journal articles. By Gary Stix. Scientific American (May 2005; subscription req'd.). "When Andrey Rzhetsky arrived at Columbia University as a research scientist in 1996, the first project he collaborated on involved a literature search to try to understand why white blood cells called lymphocytes do not die in chronic lymphocytic leukemia. The mathematician-biologist found a few hundred articles on apoptosis (programmed cell death) and the cancer.... The experience led him to an idea that would have made his job on that first project much easier: an automated search tool that could supplant the mind-numbing task of finding and reading all the literature. But it also might do much more; it could even let a machine conduct research on its own, discovering the patterns among the data much as a human would do...." |
