Robust Computational Method for Identification of miRNA-mRNA Modules in Cervical Cancer

Paul, Sushmita; Madhumita, Madhumita

doi:10.1109/icdmw.2018.00112

Cited by 1 publication

(1 citation statement)

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“…For the quantification of KPEA, BPEA, and DOEA, respective enrichment scores 69 , and annotation ratios 69 are calculated. The higher the value of these scores better is the enrichment; hence, the more biologically significant the differentially expressed biomarkers are, the better the cancer sub-typing.…”

Section: Normalized Mutual Information (Nmi)mentioning

confidence: 99%

Recursive integration of synergised graph representations of multi-omics data for cancer subtypes identification

Madhumita

Dwivedi

Paul

2022

Sci Rep

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Cancer subtypes identification is one of the critical steps toward advancing personalized anti-cancerous therapies. Accumulation of a massive amount of multi-platform omics data measured across the same set of samples provides an opportunity to look into this deadly disease from several views simultaneously. Few integrative clustering approaches are developed to capture shared information from all the views to identify cancer subtypes. However, they have certain limitations. The challenge here is identifying the most relevant feature space from each omic view and systematically integrating them. Both the steps should lead toward a global clustering solution with biological significance. In this respect, a novel multi-omics clustering algorithm named RISynG (Recursive Integration of Synergised Graph-representations) is presented in this study. RISynG represents each omic view as two representation matrices that are Gramian and Laplacian. A parameterised combination function is defined to obtain a synergy matrix from these representation matrices. Then a recursive multi-kernel approach is applied to integrate the most relevant, shared, and complementary information captured via the respective synergy matrices. At last, clustering is applied to the integrated subspace. RISynG is benchmarked on five multi-omics cancer datasets taken from The Cancer Genome Atlas. The experimental results demonstrate RISynG’s efficiency over the other approaches in this domain.

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Section: Normalized Mutual Information (Nmi)mentioning

confidence: 99%