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Mutual Information

In probability theory and information theory, the mutual information (MI) of two random variables is a measure of the mutual dependence between the two variables. [Source: Wikipedia ]

mirConnX

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

mirConnX is a user-friendly web interface for inferring, displaying and parsing mRNA and microRNA (miRNA) gene regulatory networks. mirConnX combines sequence information with gene expression data analysis to create a disease-specific, genome-wide regulatory network. A prior, static network has been constructed for all human and mouse genes. It consists of computationally predicted transcription factor (TF)-gene associations and miRNA target predictions. The prior network is supplemented with known interactions from the literature.

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MVDA

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

Multiple high-throughput molecular profiling by omics technologies can be collected for the same individuals. Combining these data, rather than exploiting them separately, can significantly increase the power of clinically relevant patients subclassifications.

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miRLAB

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

microRNAs (miRNAs) are important gene regulators at post-transcriptional level, and inferring miRNA-mRNA regulatory relationships is a crucial problem. Consequently, several computational methods of predicting miRNA targets have been proposed using expression data with or without sequence based miRNA target information. A typical procedure for applying and evaluating such a method is i) collecting matched miRNA and mRNA expression profiles in a specific condition, e.g.

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LMMEL-miR-miner

Submitted by ChenLiang on Mon, 01/09/2017 - 10:31

BACKGROUND: In many cancers, microRNAs (miRs) contribute to metastatic progression by modulating phenotypic reprogramming processes such as epithelial-mesenchymal plasticity. This can be driven by miRs targeting multiple mRNA transcripts, inducing regulated changes across large sets of genes. The miR-target databases TargetScan and DIANA-microT predict putative relationships by examining sequence complementarity between miRs and mRNAs.

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miRsig

Submitted by ChenLiang on Mon, 01/09/2017 - 11:48

Decoding the patterns of miRNA regulation in diseases are important to properly realize its potential in diagnostic, prog- nostic, and therapeutic applications. Only a handful of studies computationally predict possible miRNA-miRNA interactions; hence, such interactions require a thorough investigation to understand their role in disease progression.

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DynaMod

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

A comprehensive analysis of enriched functional categories in differentially expressed genes is important to extract the underlying biological processes of genome-wide expression profiles. Moreover, identification of the network of significant functional modules in these dynamic processes is an interesting challenge. This study introduces DynaMod, a web-based application that identifies significant functional modules reflecting the change of modularity and differential expressions that are correlated with gene expression profiles under different conditions.

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FMIMS

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

MicroRNAs (miRNAs) act as a major biomarker of cancer. All miRNAs in human body are not equally important for cancer identification. We propose a methodology, called FMIMS, which automatically selects the most relevant miRNAs for a particular type of cancer. In FMIMS, miRNAs are initially grouped by using a SVM-based algorithm; then the group with highest relevance is determined and the miRNAs in that group are finally ranked for selection according to their redundancy.

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Average: 4.5 (2 votes)

FMIGS

Submitted by ChenLiang on Sun, 09/10/2017 - 17:05

MicroRNAs (miRNA) are one of the important regulators of cell division and also responsible for cancer development. Among the discovered miRNAs, not all are important for cancer detection. In this regard a fuzzy mutual information (FMI) based grouping and miRNA selection method (FMIGS) is developed to identify the miRNAs responsible for a particular cancer. First, the miRNAs are ranked and divided into several groups. Then the most important group is selected among the generated groups.

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