title: Quantitative assessment of gene expression network module-validation methods.
abstract: Validation of pluripotent modules in diverse networks holds enormous potential for systems biology and network pharmacology. An arising challenge is how to assess the accuracy of discovering all potential modules from multi-omic networks and validating their architectural characteristics based on innovative computational methods beyond function enrichment and biological validation. To display the framework progress in this domain, we systematically divided the existing Computational Validation Approaches based on Modular Architecture (CVAMA) into topology-based approaches (TBA) and statistics-based approaches (SBA). We compared the available module validation methods based on 11 gene expression datasets, and partially consistent results in the form of homogeneous models were obtained with each individual approach, whereas discrepant contradictory results were found between TBA and SBA. The TBA of the Zsummary value had a higher Validation Success Ratio (VSR) (51%) and a higher Fluctuation Ratio (FR) (80.92%), whereas the SBA of the approximately unbiased (AU) p-value had a lower VSR (12.3%) and a lower FR (45.84%). The Gray area simulated study revealed a consistent result for these two models and indicated a lower Variation Ratio (VR) (8.10%) of TBA at 6 simulated levels. Despite facing many novel challenges and evidence limitations, CVAMA may offer novel insights into modular networks.
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, 16 Nanxiaojie, Dongzhimennei, Beijing 100700, China.|Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, 16 Nanxiaojie, Dongzhimennei, Beijing 100700, China.
authors.forename: Bing
authors.initials: B
authors.lastname: Li
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, 16 Nanxiaojie, Dongzhimennei, Beijing 100700, China.
authors.forename: Yingying
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, 16 Nanxiaojie, Dongzhimennei, Beijing 100700, China.
authors.forename: Yanan
authors.initials: Y
authors.lastname: Yu
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, 16 Nanxiaojie, Dongzhimennei, Beijing 100700, China.
authors.forename: Pengqian
authors.initials: P
authors.lastname: Wang
authors.affiliation: Brightech International, LLC, 285 Davidson Ave #504, Somerset, NJ 08873, USA.
authors.forename: Yongcheng
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, 16 Nanxiaojie, Dongzhimennei, Beijing 100700, China.
authors.forename: Zhong
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, 16 Nanxiaojie, Dongzhimennei, Beijing 100700, China.
authors.forename: Yongyan
authors.initials: Y
authors.lastname: Wang
mesh_terms: D019295:Computational Biology; D030541:Databases, Genetic; D015870:Gene Expression; D053263:Gene Regulatory Networks; D015233:Models, Statistical; D054928:Validation Studies as Topic