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Final Progress Reports: University of California-Berkeley: Quantitative Biology: Biostatistics, Bioinformatics, and Computation

Superfund Research Program

Quantitative Biology: Biostatistics, Bioinformatics, and Computation

Project Leader: Mark J. van der Laan
Co-Investigator: Alan E. Hubbard
Grant Number: P42ES004705
Funding Period: 2006-2017
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Final Progress Reports

Year:   2016  2010 

Members of the Quantitative Biology: Biostatistics, Bioinformatics and Computation Core have had a productive period, engaging in: 1) applied analysis of complex data for finding the impacts of exposure to toxicants on biomarkers of disease, 2) methods-development for getting more robust statistical results (fewer false positive and negative associations of exposure and such biomarkers), and 3) software development for dissemination of the methods both to other Program members, but also general scientific community. Their program, like similar research efforts, has analytical challenges due to very complex data, with thousands (sometimes millions) of variables measured on relatively few subjects. Standard methods, even ones still commonly used for such data, are theoretically known to fail in such circumstances. The researchers taken two complimentary approaches. First, they have derived, theoretically, results that give some indication of how bad things can get (how erroneous interpretations of the data can be based on use of standard methods). Second, they have developed novel adaptive ways of narrowing down the list of variables, so that the number of variables does not overwhelm the data. Both of these tracks are complicated and computationally difficult to implement. Thus, they have used some of their funded time to develop software packages, so that expertise in computing is not required to implement these methods. This general approach is creating models of reproducible research, trying to gain as much information from the data as possible, but still providing accurate measures of uncertainty in the results so that there is some context to interpret the results.

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