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{bio,medical} informatics

Friday, September 28, 2001

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find related articles. powered by google. USAToday Genetic test shows which AIDS drugs will fail

"The government has approved the first gene-based test to tell quickly whether an HIV patient's virus is mutating to make a particular drug therapy fail, important to know so the person can switch AIDS medications."

"With Trugene, a doctor sends a patient's blood sample to one of 130 labs where Visible Genetics so far has trained personnel. A computer decodes the HIV genes in that blood, identifying all the genetic mutations present. Then a software program matches those mutations to a list of more than 70 mutations currently linked to resistance in specific drugs."

redux [01.18.01]
find related articles. powered by google. The New York Times When Gene Sequencing Becomes a Fact of Life
[requires 'free' registration]

"The gene-sequencing machines that unraveled the human genome were nearly the size of refrigerators and cost $300,000 apiece. Richard T. Daly's sequencers fit easily on a desk ? and he is giving them away.

The reason is that Mr. Daly wants to move gene sequencing from research laboratories into daily medical practice. The company he heads, Visible Genetics, has developed a test to sequence genes of the virus that causes AIDS, providing information to help doctors choose which of the 15 or so available drugs will work best against a particular patient's infection.

"You've got a huge medicine cabinet to pick from and no good way to pick," said Mr. Daly, whose company is publicly traded and based in Toronto."

redux [07.06.00]
find related articles. powered by google. Proceedings of the National Academy of Sciences Production of resistant HIV mutants during antiretroviral therapy

"HIV drug therapy often fails because of the appearance of multidrug-resistant virus. There are two possible scenarios for the outgrowth of multidrug-resistant virus in response to therapy. Resistant virus may preexist at low frequencies in drug-naïve patients and is rapidly selected in the presence of drugs. Alternatively, resistant virus is absent at the start of therapy but is generated by residual viral replication during therapy. Currently available experimental methods are generally too insensitive to distinguish between these two scenarios. Here we use deterministic and stochastic models to investigate the origin of multidrug resistance. We quantify the probabilities that resistant mutants preexist, and that resistant mutants are generated during therapy. The models suggest that under a wide range of conditions, treatment failure is most likely caused by the preexistence of resistant mutants."

redux [08.01.01]
find related articles. powered by google. Stanford Medical Informatics Preprint Archive Challenges for Biomedical Informatics and Pharmacogenomics

"Pharmacogenomics requires the integration and analysis of genomic, molecular, cellular, and clinical data, and thus offers a remarkable set of challenges to biomedical informatics. These include infrastructural challenges such as the creation of data models and data bases for storing this data, the integration of these data with external databases, the extraction of information from natural language text, and the protection of databases with sensitive information. There are also scientific challenge in creating tools to support gene expression analysis, three-dimensional structural analysis, and comparative genomic analysis. In this review, we summarize the current uses of informatics within pharmacogenomics, and show how the technical challenges that remain for biomedical informatics are typical of those that will be confronted in the post-genomic era."

redux [08.06.01]
find related articles. powered by google. Science Defining Disease in the Genomics Era
[ summary can be viewed for free once registered]

"The human genome sequence will dramatically alter how we define, prevent, and treat disease. As more and more genetic variations among individuals are discovered, there will be a rush to label many of these variations as disease-associated. We need to define the term disease so that it incorporates our expanding genetic knowledge, taking into account the possible risks and adverse consequences associated with certain genetic variations, while acknowledging that a definition of disease cannot be based solely on one genetic abnormality."

"In thinking about how clinicians use the term disease, we think that three elements should be considered: disease is a state that places individuals at increased risk of adverse consequences. Treatment is given to those with a disease to prevent or ameliorate adverse consequences. The key element in this definition is risk: deviations from normal that are not associated with risk should not be considered synonymous with disease. Our definition has three definable elements and should serve clinicians well. Of course, its success will depend on whether it becomes clinically useful."

redux [05.31.01]
find related articles. powered by google. Family Physicians' Electronic Network Diagnostic Algorithms: results at last!

"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."

[ 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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