ICDS - Identification of Cancer Dysfunctional Subpathway with Omics Data

Identify Cancer Dysfunctional Sub-pathway by integrating gene expression, DNA methylation and copy number variation, and pathway topological information. 1)We firstly calculate the gene risk scores by integrating three kinds of data: DNA methylation, copy number variation, and gene expression. 2)Secondly, we perform a greedy search algorithm to identify the key dysfunctional sub-pathways within the pathways for which the discriminative scores were locally maximal. 3)Finally, the permutation test was used to calculate statistical significance level for these key dysfunctional sub-pathways.

Last updated 4 months ago

4.54 score 3 scripts 198 downloads

LncPath - Identifying the Pathways Regulated by LncRNA Sets of Interest

Identifies pathways synergisticly regulated by the interested lncRNA(long non-coding RNA) sets based on a lncRNA-mRNA(messenger RNA) interaction network. 1) The lncRNA-mRNA interaction network was built from the protein-protein interactions and the lncRNA-mRNA co-expression relationships in 28 RNA-Seq data sets. 2) The interested lncRNAs can be mapped into networks as seed nodes and a random walk strategy will be performed to evaluate the rate of each coding genes influenced by the seed lncRNAs. 3) Pathways regulated by the lncRNA set will be evaluated by a weighted Kolmogorov-Smirnov statistic as an ES Score. 4) The p value and false discovery rate value will also be calculated through a permutation analysis. 5) The running score of each pathway can be plotted and the heat map of each pathway can also be plotted if an expression profile is provided. 6) The rank and scores of the gene list of each pathway can be printed.

Last updated 4 years ago

4.52 score 1 stars 11 scripts 129 downloads

MiRSEA - 'MicroRNA' Set Enrichment Analysis

The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.

Last updated 4 years ago

statisticspathwaysmicrornaenrichment analysis

4.51 score 16 scripts 283 downloads

CITMIC - Estimation of Cell Infiltration Based on Cell Crosstalk

A systematic biology tool was developed to identify cell infiltration via an Individualized Cell crosstalk network. 'CITMIC' first constructed a weighted cell crosstalk network by integrating Cell-target interaction information, biological process data from the Gene Ontology (GO) database, and gene transcriptomic data in a specific sample, and then, it used a network propagation algorithm on the network to identify cell infiltration for the sample. Ultimately, cell infiltration in the patient dataset was obtained by normalizing the centrality scores of the cells.

Last updated 2 months ago

4.48 score 5 scripts 190 downloads

DTSEA - Drug Target Set Enrichment Analysis

It is a novel tool used to identify the candidate drugs against a particular disease based on the drug target set enrichment analysis. It assumes the most effective drugs are those with a closer affinity in the protein-protein interaction network to the specified disease. (See Gómez-Carballa et al. (2022) <doi: 10.1016/j.envres.2022.112890> and Feng et al. (2022) <doi: 10.7150/ijms.67815> for disease expression profiles; see Wishart et al. (2018) <doi: 10.1093/nar/gkx1037> and Gaulton et al. (2017) <doi: 10.1093/nar/gkw1074> for drug target information; see Kanehisa et al. (2021) <doi: 10.1093/nar/gkaa970> for the details of KEGG database.)

Last updated 2 years ago

4.32 score 42 scripts 221 downloads

SMDIC - Identification of Somatic Mutation-Driven Immune Cells

A computing tool is developed to automated identify somatic mutation-driven immune cells. The operation modes including: i) inferring the relative abundance matrix of tumor-infiltrating immune cells and integrating it with a particular gene mutation status, ii) detecting differential immune cells with respect to the gene mutation status and converting the abundance matrix of significant differential immune cell into two binary matrices (one for up-regulated and one for down-regulated), iii) identifying somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and iv) visualization of immune cell abundance of samples in different mutation status..

Last updated 1 months ago

4.30 score 2 stars 5 scripts 359 downloads

ssMutPA - Single-Sample Mutation-Based Pathway Analysis

A systematic bioinformatics tool to perform single-sample mutation-based pathway analysis by integrating somatic mutation data with the Protein-Protein Interaction (PPI) network. In this method, we use local and global weighted strategies to evaluate the effects of network genes from mutations according to the network topology and then calculate the mutation-based pathway enrichment score (ssMutPES) to reflect the accumulated effect of mutations of each pathway. Subsequently, the ssMutPES profiles are used for unsupervised spectral clustering to identify cancer subtypes.

Last updated 1 months ago

4.00 score 9 scripts 199 downloads

SubtypeDrug - Prioritization of Candidate Cancer Subtype Specific Drugs

A systematic biology tool was developed to prioritize cancer subtype-specific drugs by integrating genetic perturbation, drug action, biological pathway, and cancer subtype. The capabilities of this tool include inferring patient-specific subpathway activity profiles in the context of gene expression profiles with subtype labels, calculating differentially expressed subpathways based on cultured human cells treated with drugs in the 'cMap' (connectivity map) database, prioritizing cancer subtype specific drugs according to drug-disease reverse association score based on subpathway, and visualization of results (Castelo (2013) <doi:10.1186/1471-2105-14-7>; Han et al (2019) <doi:10.1093/bioinformatics/btz894>; Lamb and Justin (2006) <doi:10.1126/science.1132939>). Please cite using <doi:10.1093/bioinformatics/btab011>.

Last updated 11 months ago

4.00 score 2 stars 3 scripts 238 downloads

DrugSim2DR - Predict Drug Functional Similarity to Drug Repurposing

A systematic biology tool was developed to repurpose drugs via a drug-drug functional similarity network. 'DrugSim2DR' first predict drug-drug functional similarity in the context of specific disease, and then using the similarity constructed a weighted drug similarity network. Finally, it used a network propagation algorithm on the network to identify drugs with significant target abnormalities as candidate drugs.

Last updated 1 years ago

4.00 score 2 stars 2 scripts 161 downloads

PMAPscore - Identify Prognosis-Related Pathways Altered by Somatic Mutation

We innovatively defined a pathway mutation accumulate perturbation score (PMAPscore) to reflect the position and the cumulative effect of the genetic mutations at the pathway level. Based on the PMAPscore of pathways, identified prognosis-related pathways altered by somatic mutation and predict immunotherapy efficacy by constructing a multiple-pathway-based risk model (Tarca, Adi Laurentiu et al (2008) <doi:10.1093/bioinformatics/btn577>).

Last updated 2 years ago

3.70 score 2 scripts 142 downloads

pathwayTMB - Pathway Based Tumor Mutational Burden

A systematic bioinformatics tool to develop a new pathway-based gene panel for tumor mutational burden (TMB) assessment (pathway-based tumor mutational burden, PTMB), using somatic mutations files in an efficient manner from either The Cancer Genome Atlas sources or any in-house studies as long as the data is in mutation annotation file (MAF) format. Besides, we develop a multiple machine learning method using the sample's PTMB profiles to identify cancer-specific dysfunction pathways, which can be a biomarker of prognostic and predictive for cancer immunotherapy.

Last updated 2 years ago

2.48 score 1 packages 2 scripts 255 downloads

iPRISM - Intelligent Predicting Response to Cancer Immunotherapy Through Systematic Modeling

Immunotherapy has revolutionized cancer treatment, but predicting patient response remains challenging. Here, we presented Intelligent Predicting Response to cancer Immunotherapy through Systematic Modeling (iPRISM), a novel network-based model that integrates multiple data types to predict immunotherapy outcomes. It incorporates gene expression, biological functional network, tumor microenvironment characteristics, immune-related pathways, and clinical data to provide a comprehensive view of factors influencing immunotherapy efficacy. By identifying key genetic and immunological factors, it provides an insight for more personalized treatment strategies and combination therapies to overcome resistance mechanisms.

Last updated 4 months ago

2.00 score 122 downloads

ProgModule - Identification of Prognosis-Related Mutually Exclusive Modules

A novel tool to identify candidate driver modules for predicting the prognosis of patients by integrating exclusive coverage of mutations with clinical characteristics in cancer.

Last updated 6 months ago

2.00 score 1 scripts 111 downloads

DRviaSPCN - Drug Repurposing in Cancer via a Subpathway Crosstalk Network

A systematic biology tool was developed to repurpose drugs via a subpathway crosstalk network. The operation modes include 1) calculating centrality scores of SPs in the context of gene expression data to reflect the influence of SP crosstalk, 2) evaluating drug-disease reverse association based on disease- and drug-induced SPs weighted by the SP crosstalk, 3) identifying cancer candidate drugs through perturbation analysis. There are also several functions used to visualize the results.

Last updated 10 months ago

2.00 score 5 scripts 226 downloads

IDMIR - Identification of Dysregulated MiRNAs Based on MiRNA-MiRNA Interaction Network

A systematic biology tool was developed to identify dysregulated miRNAs via a miRNA-miRNA interaction network. 'IDMIR' first constructed a weighted miRNA interaction network through integrating miRNA-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific-disease context, and then, it used a network propagation algorithm on the network to identify significantly dysregulated miRNAs.

Last updated 1 years ago

2.00 score 2 scripts 130 downloads