The following investigators are involved in Bioinformatics projects, four examples of which are given below: Pierre Bushel(http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/bushel/index.cfm), David Dunson, Leping Li(http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/li/index.cfm), Shyamal Peddada(http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/peddada/index.cfm), William Quattlebaum(http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/quattlebaum/index.cfm), David Umbach(http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/umbach/index.cfm), Clarice Weinberg(http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/weinberg/index.cfm).
Order-restricted inference for gene expression patterns: The Branch developed methods based on order-restricted inference for classifying response profiles for genes over time or over doses, to aid in identifying families of genes that are differentially-expressed and possibly co-regulated. Downloadable software, ORIOGEN(http://www.niehs.nih.gov/research/resources/software/oriogen/index.cfm), is available without charge.
Gene set enrichment analysis for nonmonotone dose-response: The Branch proposed a three-step procedure for identifying biological pathways/processes, using pre-defined gene sets that are associated with a continuous phenotypic endpoint through gene expression data.
Promoter sequence analysis: The Branch is developing and implementing methods for detecting and discovering functional elements such as the cis-regulatory motifs in the promoter regions of genes using Markov models and Expectation Maximization (EM) methods.
Phenotypic anchoring: The Branch developed a modified k-prototypes semi-supervised clustering algorithm, which integrates and analyzes phenotypic observations, end-point measurements and associated biological information with gene expression data. The purpose of the algorithm is to identify biological mechanisms and pathways that are perturbed by environmental stressors. This approach allows for construction of phenotypic prototypes using key histopathologic severity scores, clinical chemistry measurements and significantly differentially expressed genes, which prototypes can group biological samples according to pathophysiological states.