{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:21Z","timestamp":1772138061056,"version":"3.50.1"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T00:00:00Z","timestamp":1686182400000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CCF-1718479"],"award-info":[{"award-number":["CCF-1718479"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Gene network reconstruction from gene expression profiles is a compute- and data-intensive problem. Numerous methods based on diverse approaches including mutual information, random forests, Bayesian networks, correlation measures, as well as their transforms and filters such as data processing inequality, have been proposed. However, an effective gene network reconstruction method that performs well in all three aspects of computational efficiency, data size scalability, and output quality remains elusive. Simple techniques such as Pearson correlation are fast to compute but ignore indirect interactions, while more robust methods such as Bayesian networks are prohibitively time consuming to apply to tens of thousands of genes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We developed maximum capacity path (MCP) score, a novel maximum-capacity-path-based metric to quantify the relative strengths of direct and indirect gene\u2013gene interactions. We further present MCPNet, an efficient, parallelized gene network reconstruction software based on MCP score, to reverse engineer networks in unsupervised and ensemble manners. Using synthetic and real Saccharomyces cervisiae datasets as well as real Arabidopsis thaliana datasets, we demonstrate that MCPNet produces better quality networks as measured by AUPRC, is significantly faster than all other gene network reconstruction software, and also scales well to tens of thousands of genes and hundreds of CPU cores. Thus, MCPNet represents a new gene network reconstruction tool that simultaneously achieves quality, performance, and scalability requirements.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Source code freely available for download at https:\/\/doi.org\/10.5281\/zenodo.6499747 and https:\/\/github.com\/AluruLab\/MCPNet, implemented in C++ and supported on Linux.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad373","type":"journal-article","created":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T12:03:23Z","timestamp":1686225803000},"source":"Crossref","is-referenced-by-count":1,"title":["MCPNet: a parallel maximum capacity-based genome-scale gene network construction framework"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7945-6534","authenticated-orcid":false,"given":"Tony C","family":"Pan","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Emory University , Woodruff Memorial Research Building 101 Woodruff Circle, 4th Floor East , Atlanta, GA 30322, United States"},{"name":"Institute for Data Engineering and Science, Georgia Institute of Technology , 756 W Peachtree St NW, 12th Floor , Atlanta, GA 30332, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1358-7691","authenticated-orcid":false,"given":"Sriram P","family":"Chockalingam","sequence":"additional","affiliation":[{"name":"Institute for Data Engineering and Science, Georgia Institute of Technology , 756 W Peachtree St NW, 12th Floor , Atlanta, GA 30332, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1353-9622","authenticated-orcid":false,"given":"Maneesha","family":"Aluru","sequence":"additional","affiliation":[{"name":"School of Biological Sciences, Georgia Institute of Technology , 310 Ferst Dr NW , Atlanta, GA 30332, United States"}]},{"given":"Srinivas","family":"Aluru","sequence":"additional","affiliation":[{"name":"Institute for Data Engineering and Science, Georgia Institute of Technology , 756 W Peachtree St NW, 12th Floor , Atlanta, GA 30332, United States"},{"name":"School of Computational Science and Engineering, Georgia Institute of Technology , 756 W Peachtree St NW, 13th Floor , Atlanta, GA 30332, United States"}]}],"member":"286","published-online":{"date-parts":[[2023,6,8]]},"reference":[{"key":"2023062303471882100_btad373-B1","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1038\/nmeth.4463","article-title":"SCENIC: single-cell regulatory network inference and clustering","volume":"14","author":"Aibar","year":"2017","journal-title":"Nat Methods"},{"key":"2023062303471882100_btad373-B2","doi-asserted-by":"crossref","first-page":"e24","DOI":"10.1093\/nar\/gks904","article-title":"Reverse engineering and analysis of large genome-scale gene 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