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WormNet
Hosts a network-assisted hypothesis-generating server for C. elegans. WormNet includes a base gene network, which substantially improved predictions of RNAi phenotypes. The server generates various gene network-based hypotheses using three complementary network methods: (i) a phenotype-centric approach to ‘find new members for a pathway’; (ii) a gene-centric approach to ‘infer functions from network neighbors’ and (iii) a context-centric approach to ‘find context-associated hub genes’, which is a method to identify key genes that mediate physiology within a specific context.
FIRM / framework for the inference of regulation by miRNAs
Infers miRNA mediate regulation from co-expression signatures. FIRM was used to build a comprehensive miRNA regulatory network that links about 1300 coexpression signatures to post-transcriptional regulation mediated by about 600 miRNAs. It is based on a combination of three algorithms that use complementary strategies for inference of miRNA regulatory networks. This tool integrates disparate data types such as gene coexpression and distributions of both known and de novo-discovered miRNA-binding sites.
Yoon,De Micheli2005
Permits to identify important patterns hidden in the complex interactions. The algorithm of Yoon and De Micheli is a computational method to predict miRNA regulatory modules (MRMs) or groups of miRNAs and their targets that are believed to participate cooperatively in post-transcriptional gene regulation. This method provides groups of miRNAs and co-targeted genes automatically. This method consists of five major steps: (i) target identification, (ii) relation graph representation, (iii) seed finding, (iv) merging seeds to find candidate modules and (v) post-processing.
LiuEtAl2010
Allows discovering the functional miRNA regulatory modules (FMRMs). The algorithm of Liu et al. is a method that integrates heterogeneous datasets, including expression profiles of both miRNAs and mRNAs, with or without using prior target binding information. This model is inspired by the Correspondence Latent Dirichlet Allocation (Corr-LDA), which has been used to extract the correspondence patterns from heterogeneous data. This method simultaneously identifies groups of interactive miRNAs and mRNAs, which are believed to participate in specific biological functions.
DICORE / DIscovering COllective group RElationships
Identifies miRNA-mRNA regulatory modules (MMRMs) and hence reveals miRNA-mRNA regulatory relationships from heterogeneous data. DICORE is an effective computational framework to reveal correct group information with structural link information and the strength of collective relationships. The overall workflow comprises a data pre-processing step and two main stages: (i) forming separate miRNA and mRNA groups and (ii) searching for COREs.
TranEtAl2008
Performs a comprehensive analysis of the combinatorial nature of gene regulation by detecting rules that identify a set of miRNAs associated with genes. The algorithm of Tran et al. is a computational method for finding miRNA regulatory modules (MRMs) from their predicted target genes and expression datasets (mRNA expression profiles and miRNA expression profiles). The method extracts IFTHEN rules of miRNA combinations shared by target genes with a common expression profile.
BCM / BiCliques Merging
A method to predict miRNA regulatory modules (MRMs) based on bicliques merging. BCM exploits the miRNA/mRNA expression profiles, target site predictions as well as the gene-gene interactions. Furthermore, the module number is automatically determined during the merging process. We apply our method to breast cancer and thyroid cancer datasets downloaded from TCGA. Comparing with alternative formalisms, we show that the modules identified by our method are more densely connected and functionally enriched.
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