Spotlight: Assembly of protein complexes by integrating graph clustering methods

Chin, Chia Hao; Chen, Shu Hwa; Chen, Chun Yu; Hsiung, Chao A.; Ho, Chin Wen; Ko, Ming-Tat; Lin, Chung Yen

doi:10.1016/j.gene.2012.11.087

Cited by 1 publication

(2 citation statements)

References 43 publications

(43 reference statements)

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“…Topological indexes, including intra-modular connectivity 44 and NB value 26 , have been applied to evaluate whether the intra-modular structure is different from other parts of the whole network. Other indexes, such as compactness 45 and weak community 46 , can be used to select good clusters from integrated clustering results. Because a single topological index is not likely to provide a global evaluation of the modular structure, an alternative choice is to combine multiple topological indexes into an integrated measure to assess a module’s validity.…”

Section: Resultsmentioning

confidence: 99%

“…Although it is not our focus in this study, we summarize and list functional module validation methods reported in the published literature ( Supplementary Table 1 ). Typically, the most widely used functional validation method is functional homogeneity evaluation 65 66 , with indexes such as functional enriched p value 7 45 67 and R score 7 68 . Furthermore, the index of quantitative score based on function enrichment analysis may be applied to assess a module’s confidence level 21 or disease relationship 69 .…”

Section: Resultsmentioning

confidence: 99%

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Quantitative assessment of gene expression network module-validation methods

Zhang

et al. 2015

Sci Rep

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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.

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