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Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|February 21, 2013
Protein-chemical interaction prediction via kernelized sparse learning SVMYi Shi, Xinhua Zhang, Xiaoping Liao, et al.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|February 21, 2013
Drug target predictions based on heterogeneous graph inferenceWenhui Wang, Sen Yang, Jing Li
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|February 21, 2013
Using DNase digestion data to accurately identify transcription factor binding sitesKaixuan Luo, Alexander J Hartemink
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|February 21, 2013
Identifying master regulators of cancer and their downstream targets by integrating genomic and epigenomic featuresOlivier Gevaert, Sylvia Plevritis
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|February 21, 2013
Interpreting personal transcriptomes: personalized mechanism-scale profiling of RNA-seq dataAlan Perez-Rathke, Haiquan Li, Yves A Lussier
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|February 21, 2013
Systematic identification of risk factors for Alzheimer's disease through shared genetic architecture and electronic medical recordsLi Li, David Ruau, Rong Chen, et al.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|February 21, 2013
Inferring optimal species trees under gene duplication and lossM S Bayzid, S Mirarab, T Warnow
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|February 21, 2013
Evaluating variations on the STAR algorithm for relative efficiency and sample sizes needed to reconstruct species treesJames H Degnan
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|February 21, 2013
Using BioBin to explore rare variant population stratificationCarrie B Moore, John R Wallace, Alex T Frase, et al.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|February 21, 2013
ATHENA: a tool for meta-dimensional analysis applied to genotypes and gene expression data to predict HDL cholesterol levelsEmily R Holzinger, Scott M Dudek, Alex T Frase, et al.
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