{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T20:59:42Z","timestamp":1778101182734,"version":"3.51.4"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T00:00:00Z","timestamp":1666569600000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12231018"],"award-info":[{"award-number":["12231018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Gene-based transcriptome analysis, such as differential expression analysis, can identify the key factors causing disease production, cell differentiation and other biological processes. However, this is not enough because basic life activities are mainly driven by the interactions between genes. Although there have been already many differential network inference methods for identifying the differential gene interactions, currently, most studies still only use the information of nodes in the network for downstream analyses. To investigate the insight into differential gene interactions, we should perform interaction-based transcriptome analysis (IBTA) instead of gene-based analysis after obtaining the differential networks. In this paper, we illustrated a workflow of IBTA by developing a Co-hub Differential Network inference (CDN) algorithm, and a novel interaction-based metric, pivot APC2. We confirmed the superior performance of CDN through simulation experiments compared with other popular differential network inference algorithms. Furthermore, three case studies are given using colorectal cancer, COVID-19 and triple-negative breast cancer datasets to demonstrate the ability of our interaction-based analytical process to uncover causative mechanisms.<\/jats:p>","DOI":"10.1093\/bib\/bbac466","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T05:58:10Z","timestamp":1666591090000},"source":"Crossref","is-referenced-by-count":5,"title":["Interaction-based transcriptome analysis via differential network inference"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0816-9690","authenticated-orcid":false,"given":"Jiacheng","family":"Leng","sequence":"first","affiliation":[{"name":"Chinese Academy of Sciences IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, , Beijing 100190 , China"},{"name":"University of Chinese Academy of Sciences School of Mathematical Sciences, , Beijing 100049 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9487-0215","authenticated-orcid":false,"given":"Ling-Yun","family":"Wu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, , Beijing 100190 , China"},{"name":"University of Chinese Academy of Sciences School of Mathematical Sciences, , Beijing 100049 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"2022112111193598400_ref1","first-page":"1","article-title":"Differential expression analysis for sequence count data","volume":"2010","author":"Anders","year":"2010","journal-title":"Nat Preced"},{"key":"2022112111193598400_ref2","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1093\/bioinformatics\/btp616","article-title":"edgeR: A Bioconductor package for differential expression analysis of 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