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Understanding how genes are expressed and regulated in different tissues is a fundamental and challenging question. However, most of currently available biological databases do not focus on tissue-specific gene regulation. The recent development of computational methods for tissue-specific combinational gene regulation, based on transcription factor binding(More)
Tissue-specific gene expression is generally regulated by more than a single transcription factor (TF). Multiple TFs work in concert to achieve tissue specificity. In order to explore these complex TF interaction networks, we performed a large-scale analysis of TF interactions for 30 human tissues. We first identified tissue-specific genes for 30 tissues(More)
Experimental protein-protein interaction (PPI) networks are increasingly being exploited in diverse ways for biological discovery. Accordingly, it is vital to discern their underlying natures by identifying and classifying the various types of deterministic (specific) and probabilistic (nonspecific) interactions detected. To this end, we have analyzed PPI(More)
Combinatorial regulation by transcription factor complexes is an important feature of eukaryotic gene regulation. Here, we propose a new method for identification of interactions between transcription factors (TFs) that relies on the relationship of their binding sites, and we test it using Saccharomyces cerevisiae as a model system. The algorithm predicts(More)
MicroRNAs (miRNAs) negatively regulate the expression of target genes at the post-transcriptional level. Little is known about the crosstalk between miRNAs and transcription factors (TFs). Here we provide data suggesting that the interaction patterns between TFs and miRNAs can influence the biological functions of miRNAs. From this global survey, we find(More)
Evolutionary conservation has been used successfully to help identify cis-acting DNA regions that are important in regulating tissue-specific gene expression. Motivated by increasing evidence that some DNA regulatory regions are not evolutionary conserved, we have developed an approach for cis-regulatory region identification that does not rely upon(More)
We study the kinetics of filament bundling by variable time-step Brownian-dynamics simulations employing a simplified attractive potential based on earlier atomic-level calculations for actin filaments. Our results show that collisions often cluster in time, due to memory in the random walk. The clustering increases the bundling opportunities. Small-angle(More)
PURPOSE The liver is the most common site of systemic metastases from uveal melanoma (UM). Such metastases usually continue to develop despite the application of current treatment modalities. This study was conducted to obtain insight into the molecular pathways that underlie the development of UM metastasis and thus to identify potential novel therapeutic(More)
Protein-DNA interactions (PDIs) mediate a broad range of functions essential for cellular differentiation, function, and survival. However, it is still a daunting task to comprehensively identify and profile sequence-specific PDIs in complex genomes. Here, we have used a combined bioinformatics and protein microarray-based strategy to systematically(More)
BACKGROUND The use of structural alerts to de-prioritize compounds with undesirable features as drug candidates has been gaining in popularity. Hundreds of molecular structural moieties have been proposed as structural alerts. An emerging issue is that strict application of these alerts will result in a significant reduction of the chemistry space for new(More)