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Many mammalian peripheral tissues have circadian clocks; endogenous oscillators that generate transcriptional rhythms thought to be important for the daily timing of physiological processes. The extent of circadian gene regulation in peripheral tissues is unclear, and to what degree circadian regulation in different tissues involves common or specialized(More)
mSin3A is a core component of a large multiprotein corepressor complex with associated histone deacetylase (HDAC) enzymatic activity. Physical interactions of mSin3A with many sequence-specific transcription factors has linked the mSin3A corepressor complex to the regulation of diverse signaling pathways and associated biological processes. To dissect the(More)
UNLABELLED The analysis of complex patterns of gene regulation is central to understanding the biology of cells, tissues and organisms. Patterns of gene regulation pertaining to specific biological processes can be revealed by a variety of experimental strategies, particularly microarrays and other highly parallel methods, which generate large datasets(More)
The structure of a genetic network is uncovered by studying its response to external stimuli (input signals). We present a theory of propagation of an input signal through a linear stochastic genetic network. We found that there are important advantages in using oscillatory signals over step or impulse signals and that the system may enter into a pure(More)
Learning the structure of Bayesian networks(BNs) is known to be NP-complete and most of the recent work in the field is based on heuristics. Many recent approaches to the problem trade correctness and exactness for faster computation and are still computationally infeasible, except for networks with few variables. In this paper we present a(More)
Complex interactions between genes or proteins contribute substantially to phenotypic evolution. We present a probabilistic model and a maximum likelihood approach for cross-species clustering analysis and for identification of conserved as well as species-specific co-expression modules. This model enables a "soft" cross-species clustering (SCSC) approach(More)
We present a method to find motifs by simultaneously using the overrepresentation property and the evolutionary conservation property of motifs. This method is applicable to divergent species where alignment is unreliable, which overcomes a major limitation of the current methods. The method has been applied to search regulatory motifs in four yeast species(More)
We present a statistical methodology that largely improves the accuracy in computational predictions of transcription factor (TF) binding sites in eukaryote genomes. This method models the cross-species conservation of binding sites without relying on accurate sequence alignment. It can be coupled with any motif-finding algorithm that searches for(More)
UNLABELLED Structural variations (SVs) are large genomic rearrangements that vary significantly in size, making them challenging to detect with the relatively short reads from next-generation sequencing (NGS). Different SV detection methods have been developed; however, each is limited to specific kinds of SVs with varying accuracy and resolution. Previous(More)
ParaLearn is a scalable, parallel FPGA-based system for learning interaction networks using Bayesian statistics. ParaLearn includes problem specific parallel/scalable algorithms, system software and hardware architecture to address this complex problem. Learning interaction networks from data uncovers causal relationships and allows scientists to predict(More)