A novel signaling pathway impact analysis

By Adi L. Tarca, Sorin Draghici, Purvesh Khatri, Sonia S. Hassan, Pooja Mittal, Jung-sun Kim, Chong Jai Kim, Juan Pedro Kusanovic, Roberto Romero. Bioinform., 2009

996 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/44bc2cd1574307d65a0de98b4c730a96b6bb7b95

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Citations in the corpus

KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis (2021, methodology): “The common tools for the PT-based GSE method consist of SPIA (22), GANPA (23) and CEPA (24).”
Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation (2010, methodology): “Future work will include incorporating molecular interaction network and pathway information [47,48] into gene-set analysis methods, as has successfully been done for gene expression analysis [49,50,51,52,53].”
Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources (2020, background): “Methods that fall into this category include Signaling Pathway Impact Analysis [216], Pathway Regulation Score [217],”
pathfindR: An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks (2019, methodology): “The enrichment significance cutoff value was set to 0.25 for each analysis (changing the argument enrichment_threshold of run_ pathfindR function) as we later performed validation of the results using the three significance cutoff values of 0.05, 0.1, and 0.25 To better evaluate the performance of p”
MuSiC: Identifying mutational significance in cancer genomes (2012, methodology): “There are several published methods for identifying significantly mutated pathways or gene sets (e.g., Lin et al. 2007; Tarca et al. 2009; Vaske et al. 2010), but we have incorporated a new tool called PathScan into the MuSiC package.”
PathNet: a tool for pathway analysis using topological information (2012, positive): “We also compared the results of PathNet with three existing algorithms for pathway analysis that are currently in wide use: the hypergeometric test [5]; gene set enrichment analysis (GSEA) [6]; and signaling pathway impact analysis (SPIA) [8].”
Network-based machine learning and graph theory algorithms for precision oncology (2017, methodology): “Signaling pathway impact analysis (SPIA) 60 and mixed integer programming (MILP) 61 are two examples of earlier pathway-based methods for genomic data analysis.”
Pathview Web: user friendly pathway visualization and data integration (2017, methodology): “…range of databases and resources have been built (KEGG (1), Reactome (2), Wikipathways (3), MetaCyc (4), PANTHER (5), Pathway Commons (6) etc.) and numerous statistical methods and tools (generally applicable gene-set enrichment (GAGE) (7), GSEA (8), SPIA (9) etc.) developed for pathway analysis.”
Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges (2012, background): “SPIA Standalone (BioConductor) [40]”
Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM (2010, methodology): “SPIA from Tarca et al. (2009) was implemented in C to reduce runtime and to be compatible with our analysis environment.”
Pathway and Network Analysis of Cancer Genomes (2015, positive): “Signaling Pathway Impact Analysis (SPIA) 58 applies a recursive algorithm similar to that used by Google to rank search results.”
MetPA: a web-based metabolomics tool for pathway analysis and visualization (2010, background): “Availability: Freely available at http://metpa.metabolomics.ca Contact: david.wishart@ualberta.ca Received on May 17, 2010; revised on June 22, 2010; accepted on July 8, 2010”