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A computational framework that identifies high-probability signaling and regulatory paths that connect input data sets. The input includes two weighted lists of condition-related proteins or genes, such as a set of disease-associated genes and a set of differentially expressed disease genes, and a molecular interaction network (i.e., interactome). The output is a sparse, high-probability interactome sub-network connecting the two sets that is biased toward signaling pathways. This sub-network exposes additional proteins and interactions that are potentially involved in the studied condition and their likely modes of action.
Paradigm / PAthway Representation and Analysis by Direct Reference on Graphical Models
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A factor graph framework for pathway inference on high-throughput genomic data. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathway's activities (e.g. internal gene states, interactions or high-level 'outputs') are altered in the patient using probabilistic inference.
A general algorithm for identifying high weight subnetworks in a vertex-weighted network. HotNet was developed for identifying significantly mutated groups of interacting genes from large cancer sequencing studies. HotNet uses an “insulated” heat diffusion process to simultaneously analyze a gene’s mutations (or mutation score) and its local topology. This diffusion process encodes the source, or directionality, of heat within the network, allowing HotNet to uncover surprisingly “hot” subnetworks with wide ranges of heat scores.
MEMo / Mutually Exclusive Modules in cancer
A method for identifying mutually exclusive driver networks in cancer. MEMo identifies networks defined by three properties: first, member genes are recurrently altered via somatic mutation or copy number changes; second, member genes are likely to participate in the same biological pathway or process, as determined from prior pathway and network knowledge; and third, genomic events within the network exhibit a statistically significant level of mutual exclusivity.
Multi-species cMonkey
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An algorithm for the simultaneous biclustering of heterogeneous multiple-species data collections and apply the algorithm to a group of bacteria containing Bacillus subtilis, Bacillus anthracis, and Listeria monocytogenes. The algorithm reveals evolutionary insights into the surprisingly high degree of conservation of regulatory modules across these three species and allows data and insights from well-studied organisms to complement the analysis of related but less well studied organisms.
SAMBA / Statistical-Algorithmic Method for Bicluster Analysis
Enables a unified, high-level representation of heterogeneous biological information and provide a means for the analysis of the biological system under study in light of very large functional genomics databases. The framework provides the statistical robustness and computational efficiency that is required for large-scale studies and is readily extendable to future experimental techniques. Using samba, large repositories of functional genomics data can be used with maximum effect to enable the characterization of complex organisms and heterogeneous biological processes. The Samba algorithm is available either as a standalone executable or as part of the Expander software suite.
TieDIE / Tied Diffusion of Interacting Events
Computes a subnetwork of gene and protein interactions that connect genomic perturbations to gene expression changes characteristic of cancer subtypes. The pathways derived in this way predict interlinking genes that may correspond to essential components of cancer signaling and may provide a mechanistic explanation of tumor behavior and suggest subtype-specific drug targets. In addition, the algorithm can extend to generate connecting subnetworks for generic gene sets, and runs a null-model permutation test to determine if these sets are significantly close in pathway space.
CONTOUR / Cancer OncogeNes from disrupTed mOdUles and their Relationships
Allows users to detect dysregulated modules in cancer. CONTOUR is a standalone software that is able to perform three main features: (i) creating conditional Protein Protein Interactions (PPI) networks; (ii) determining complexes from PPI networks; (iii) permitting to compare different complexes issues from two conditional PPI networks; and (IV) detecting changes between them. The software aims to facilitate the identification of dysfunctional/disrupted complexes in cancer.
MITHrIL / Mirna enrIched paTHway Impact anaLysis
An algorithm developed for the analysis of signaling pathways. MITHrIL augments pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The method takes as input the expression values of genes and/or microRNAs and returns a list of pathways sorted according to their degree of deregulation, together with the corresponding statistical significance (p-values). Our analysis shows that MITHrIL outperforms its competitors even in the worst case. In addition, our method is able to correctly classify sets of tumor samples drawn from TCGA.
Identifies multiple mutated modules displaying specific mutation patterns between and within modules. BeWith is a general framework that reveals complex relations between mutual exclusivity, functional interactions, and co-occurrence. This application can be used to uncover relationships between genes, gene groups, and pathways that were not accessible by previous methods. Its formulation is very general and is appropriate to interrogate other aspects of the mutational landscape by exploring different combinations of within-between definitions and constraints with simple modifications.
Allows users to analyze high-throughput (HTP) data. WholePathwayScope is a standalone software which (i) generates biological association networks that can be modified as subnetworks, gene-gene, or pathway/term-pathway/term networks; (ii) supplies statistical evaluation of global functional enrichment in a user's gene list, or of user-defined pattern enrichment of choice genes by using Fisher’s exact test; and (iii) visualizes simultaneously results from multiple HTP experiments.
VAN / Variability Analysis in Networks
Detects and visualizes dysregulated network modules. VAN is an R package that is able to handle one or more transcriptomic datasets and molecular interaction networks. It can be employed to analyze both gene expression data or microRNA expression data. The application can determine network modules and hubs of biological relevance to complex human diseases or investigate changes across developmental timelines. In addition, its output file can be exported towards Cytoscape.
Identifies regulatory modules that can be identical (single-seed approach) or even more comprehensive (multi-seed approach). ModuleDiscoverer is an algorithm for the identification of regulatory modules based on large-scale, whole-genome protein-protein interaction networks (PPINs) and high-throughput gene expression data. The application of ModuleDiscoverer becomes favorable with increasing size and density of PPINs. It identifies the regulatory module of a rat-model of diet induced non-alcoholic steatohepatitis (NASH).
MIA / Matrix Integration Analysis
Detects multi-dimensitional modules in diverse genomics data as well as molecular network data. MIA is a MATLAB package that provides various types of genomic data as copy number variation, DNA methylation, gene expression, microRNA expression profiles and/or gene network data to identify the underlying modular patterns. This module performs the tasks included realizing a specific algorithm, drawing figures and outputting text files about the identified md-modules.
A fast hierarchical clustering algorithm based on the local metric of edge clustering value which can be used both in the unweighted network and in the weighted network. The proposed algorithm HC-PIN is applied to the yeast protein interaction network, and the identified modules are validated by all the three types of Gene Ontology (GO) Terms: Biological Process, Molecular Function, and Cellular Component. The experimental results show that HC-PIN is not only robust to false positives, but also can discover the functional modules with low density.
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