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Hypergeometric Test

In probability theory and statistics, the hypergeometric distribution is a discrete probability distribution that describes the probability of k successes in n draws, without replacement, from a finite population of size N that contains exactly K successes, wherein each draw is either a success or a failure. In contrast, the binomial distribution describes the probability of k successes in n draws with replacement. [Source: Wikipedia ]

Functional interpretation of microRNA-mRNA association

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

The prediction of microRNA targets is a challenging task that has given rise to several prediction algorithms. Databases of predicted targets can be used in a microRNA target enrichment analysis, enhancing our capacity to extract functional information from gene lists. However, the available tools in this field analyze gene sets one by one limiting their use in a meta-analysis. Here, we present an R system for miRNA enrichment analysis that is suitable for systems biology.

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ORCA

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Often during the analysis of biological data, it is of importance to interpret the correlation structure that exists between variables. Such correlations may reveal patterns of co-regulation that are indicative of biochemical pathways or common mechanisms of response to a related set of treatments. However, analyses of correlations are usually conducted by either subjective interpretation of the univariate covariance matrix or by applying multivariate modeling techniques, which do not take prior biological knowledge into account.

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Loregic

Submitted by ChenLiang on Thu, 04/06/2017 - 18:45

The topology of the gene-regulatory network has been extensively analyzed. Now, given the large amount of available functional genomic data, it is possible to go beyond this and systematically study regulatory circuits in terms of logic elements. To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors. Loregic uses all 16 possible two-input-one-output logic gates (e.g. AND or XOR) to describe triplets of two factors regulating a common target.

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