The use of computational techniques for making biological predictions of gene attributes or interactions serves an important role in the scientific community. Due to the rapid growth of available data, it is not feasible to perform every desired experiment using wet-lab techniques. Computational predictions can thus be used to prioritize hypotheses, permitting more efficient use of experimental resources. Existing prediction techniques often make use of similar information to the variable being predicted (e.g. when predicting a gene attribute, use other known attributes of that gene; or more commonly the guilt-by-association approach with other genes, such as sequence homology). A larger probabilistic model that makes use of more information is to be extended during this research, with the goal of utilizing as much available information as possible to heighten the accuracy of the predictive methods. In particular, the incorporation of multi-organism data, the analysis of """"""""missing data"""""""" (i.e. unperformed experiments), and alternative modeling approaches will be studied. Furthermore, predictions that are interesting and novel will be pursued using traditional wet-lab experiments (either in-house or through collaborators) in an attempt to make discoveries of practical value to biologists (following the precedent set by the Roth Lab, e.g. the rRNA processing collaboration with Tim Hughes). ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Postdoctoral Individual National Research Service Award (F32)
Project #
1F32HG004098-01
Application #
7158235
Study Section
Special Emphasis Panel (ZRG1-F14-A (20))
Program Officer
Graham, Bettie
Project Start
2006-12-01
Project End
2008-11-30
Budget Start
2006-12-01
Budget End
2007-11-30
Support Year
1
Fiscal Year
2006
Total Cost
$43,996
Indirect Cost
Name
Harvard University
Department
Biochemistry
Type
Schools of Medicine
DUNS #
047006379
City
Boston
State
MA
Country
United States
Zip Code
02115
Ta?an, Murat; Musso, Gabriel; Hao, Tong et al. (2015) Selecting causal genes from genome-wide association studies via functionally coherent subnetworks. Nat Methods 12:154-9
Musso, Gabriel; Tasan, Murat; Mosimann, Christian et al. (2014) Novel cardiovascular gene functions revealed via systematic phenotype prediction in zebrafish. Development 141:224-35
Cokol, Murat; Weinstein, Zohar B; Yilancioglu, Kaan et al. (2014) Large-scale identification and analysis of suppressive drug interactions. Chem Biol 21:541-551
Ta?an, Murat; Drabkin, Harold J; Beaver, John E et al. (2012) A Resource of Quantitative Functional Annotation for Homo sapiens Genes. G3 (Bethesda) 2:223-33
Cokol, Murat; Chua, Hon Nian; Tasan, Murat et al. (2011) Systematic exploration of synergistic drug pairs. Mol Syst Biol 7:544
Arabidopsis Interactome Mapping Consortium (2011) Evidence for network evolution in an Arabidopsis interactome map. Science 333:601-7
Simonis, Nicolas; Rual, Jean-Fran├žois; Carvunis, Anne-Ruxandra et al. (2009) Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network. Nat Methods 6:47-54
Braun, Pascal; Tasan, Murat; Dreze, Matija et al. (2009) An experimentally derived confidence score for binary protein-protein interactions. Nat Methods 6:91-7
Pena-Castillo, Lourdes; Tasan, Murat; Myers, Chad L et al. (2008) A critical assessment of Mus musculus gene function prediction using integrated genomic evidence. Genome Biol 9 Suppl 1:S2
Tian, Weidong; Zhang, Lan V; Tasan, Murat et al. (2008) Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function. Genome Biol 9 Suppl 1:S7

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