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{bio,medical} informatics

Friday, April 07, 2006

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find related articles. powered by google. Bio-IT World Spearheading the Semantic Web

"Sharing their passion for the Semantic Web at the Bio-IT World Life Sciences Conference + Expo session on Tuesday were several prominent leaders of the movement.

Providing a clinical perspective was Tonya Hongsermeier of Partners Healthcare, who said the Semantic Web is necessary for knowledge management because “the market is balking at healthcare inflation.” She said eight datapoints are now kept for each patient (age, height, weight, etc.), but with new tests and personalized medicine, the number could balloon to 3000. The Semantic Web can reduce the cost and liability of knowledge management, said Hongsermeier, and is a critical component of translational medicine going forward."

redux [09.29.05]
find related articles. powered by google. Nature Biotechnology Are the current ontologies in biology good ontologies?

"The research histories of both biology and ontologies originate with the philosopher Aristotle. For 2,400 years, the two subjects have taken separate paths. Now, with the rise of bioinformatics, they are reunited in the 'hot' research topic of biological ontologies. So why are biological ontologies important? As more and more biological data are stored on computers, the problem of efficient retrieval and analysis of these data becomes the most important scientific bottleneck, and the problem is particularly acute in biology because biological data are notorious for their complex form and semantics. Ontologies can help because they embody the abstract knowledge required for data integration and analysis. The utility of ontologies has been clearly demonstrated in several biological domains (e.g., Gene Ontology1). However, within biology, the enthusiasm for ontologies has been accompanied by a general lack of awareness of what exactly ontologies are and how to use them. This lack of awareness is reflected in the fact that many, perhaps all, 'bio-ontologies' fail to follow international standards for ontology design and description. This failure is important because it places a serious restriction on their applicability to knowledge sharing, reuse and inference.

In this article, we analyze the current state of application of ontologies in biology, try to reveal the reasons for the existing difficulties, recommend a possible solution to the current problems and describe prospects and future challenges for the application of ontological engineering in biological domains."

[ via nodalpoint ]
redux [03.17.02]
find related articles. powered by google. The Scientist Life Sentences
[requires 'free' registration]

"The great challenge in biological research today is how to turn data into knowledge. I have met people who think data is knowledge but these people are then striving for a means of turning knowledge into understanding. Knowledge and science are related words and to know, I believe, is to understand. Before rushing to convert genomics to 'genamics' and finding that it is another dead end, we should consider evacuating the Tower of Babel. We need a theoretical framework in which to embed biological data so that the endless stream of data, filled with the flotsam and jetsam of evolution, can be sifted and abstracted.

Very simply, the network we should be interested is not the network of names but the network of the objects themselves. The language of these objects is not the Oxford Dictionary of Molecular Biology--the Ontology Consortium's main source--but that of molecular recognition, the language of molecular biology itself."

redux [01.08.02]
find related articles. powered by google. Stanford Medical Informatics Preprint Archive Ontology Development for a Pharmacogenetics Knowledge Base

"Research directed toward discovering how genetic factors influence a patient's response to drugs requires coordination of data produced from laboratory experiments, computational methods, and clinical studies. A public respository of pharmacogenetic data to which investigators from different centers can contribute will facilitate hypothesis generation for further research. We are developing a pharmacogenetics knowledge base (PharmGKB) that will support storage and retrieval of experimental data and conceptual knowledge. We are confronted with the challenge of designing an Internet-based resource that integrates complex biological, pharmacological, and clinical data in such a way that researchers can submit their data and users can retrieve information that supports genotype phenotype correlations. Successful management of the names, meaning, and organization of concepts used within the system is crucial. We have selected a frame-based knowledge-representation system for development of an ontology of concepts and relationships that represent the domain and that will permit storage of experimental data. Preliminary experience shows that the ontology we have developed for gene-sequence data submissions is appropriate for experimental data that researchers will enter."

find related articles. powered by google. The Molecular Biology Ontology Working Group An Evaluation of Ontology Exchange Languages for Bioinformatics

"Ontologies are specifications of the concepts in a given field and the relationships among those concepts. The development of ontologies for molecular-biology information and the sharing of those ontologies within the bioinformatics community are central problems in bioinformatics. If the bioinformatics community is to share ontologies effectively, ontologies must be exchanged in a form that uses standardized syntax and semantics. This paper reports on an effort among the authors to evaluate a number of alternative ontology-exchange languages, and to recommend one or more languages for use within the larger bioinformatics community. The study selected a set of candidate languages, and defined a set of capabilities that the ideal ontology-exchange language should satisfy. The study scored the languages according to the degree to which they provided each capability. In addition, the authors performed several ontology-exchange experiments with the two languages that received the highest scores: OML and Ontolingua. The result of those experiments, and the main conclusions of this study, was that the frame-based semantic model of Ontolingua is preferable to the conceptual graph model of OML, but that the XML-based syntax of OML is preferable to the Lisp-based syntax of Ontolingua."

redux [06.27.01]
find related articles. powered by google. Wired News Making a Language Out of ATCG

"Suppose that in order to use the appliances in your kitchen, an electrical engineer needs to tear down the wall and splice in wiring for the appropriate voltage converter."

Then suppose you're running a restaurant out of that kitchen.

That, according to those in the know, is what it's like to work with life-science information today, where the absence of a common programming language makes coordination between researchers a dicey prospect."

Enter IBM, which is spearheading an effort to write that common language using XML.

find related articles. powered by google. GenomeWeb Informatics Infrastructure Consortium Unveils Demo Protocol

"The Interoperable Informatics Infrastructure Consortium (I3C) unveiled its first demonstration of a working protocol Tuesday at the BIO 2001 Conference."

The XML-driven format allowed the exchange and analysis of sequence data across 10 different organizations? products. I3C views the demonstration as a bridge to the next step to defining components needed for a more general open architecture."

redux [05.23.01]
find related articles. powered by google. The Washington Post Biotech Industry Developing Worldwide Standard for Data

"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]
find related articles. powered by google. GenomeWeb Sun Forms Industry-Wide Collaboration to Develop Open Platform for Life Sciences

"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."

redux [03.15.01]
find related articles. powered by google. MIT Technology Review Gene Babel

"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.

"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]
find related articles. powered by google. Science The Babel of Bioinformatics
[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]
find related articles. powered by google. The Scientist The Language of Bioinformatics
[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]
find related articles. powered by google. The XML Cover Pages XML and Semantic Transparency

"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 [05.10.00]
find related articles. powered by google. The Rand Corporation : Scaffolding the New Web: Standards and Standards Policy for the Digital Economy The Emerging Challenge of Common Semantics

"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?"

find related articles. powered by google. SemanticWeb.Org Tutorial on Knowledge Markup Techniques

"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."

[ rhetoric ]

Bioinformatics will be at the core of biology in the 21st century. In fields ranging from structural biology to genomics to biomedical imaging, ready access to data and analytical tools are fundamentally changing the way investigators in the life sciences conduct research and approach problems. Complex, computationally intensive biological problems are now being addressed and promise to significantly advance our understanding of biology and medicine. No biological discipline will be unaffected by these technological breakthroughs.


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