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Interaction-based transcriptome analysis via differential network inference.
Leng, Jiacheng; Wu, Ling-Yun.
  • Leng J; IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
  • Wu LY; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
Brief Bioinform ; 23(6)2022 Nov 19.
Article in English | MEDLINE | ID: covidwho-2087743
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Gene Regulatory Networks / COVID-19 Limits: Humans Language: English Journal subject: Biology / Medical Informatics Year: 2022 Document Type: Article Affiliation country: Bib

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Gene Regulatory Networks / COVID-19 Limits: Humans Language: English Journal subject: Biology / Medical Informatics Year: 2022 Document Type: Article Affiliation country: Bib