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- W2913305430 abstract "Discovering dynamic modules associated with disease progression is critical for revealing the mechanisms of cancers, which is the foundation for cancer diagnosis and therapy. And, advances in biological technology make it possible to generate heterogeneous data for cancers. Thus, it is promising to to discover cancer related modules to capture the dynamics of pathways via fusing heterogeneous genomic data. Even though great efforts have been devoted to either the dynamic module detection or heterogeneous data fusion, as far as we know, no attempt has been developed to extract dynamic modules via data fusion. In this study, we propose a general framework to detect dynamic modules based on nonnegative matrix factorization method (aka DrNMF), where the gene expression and protein interaction network are integrated. Specifically, to obtain the dynamic networks associated with cancer progression, we construct a gene co-expression network for each clinical stage of cancers. To extract the dynamic modules for each stage, we factorize the adjacency matrices at two subsequent stages, where the protein interaction network is integrated via regularization. Experimental results demonstrate that the proposed algorithm is more accurate than state-of-the-art methods in terms of accuracy in simulated data. By applying DrNMF to the breast cancer data, the obtained dynamic modules are more enriched by the known pathways. The proposed model and algorithm provide an effective way for the integrative analysis of heterogeneous genomic data to investigate dynamics of cancers." @default.
- W2913305430 created "2019-02-21" @default.
- W2913305430 creator A5029088205 @default.
- W2913305430 creator A5078745962 @default.
- W2913305430 date "2018-12-01" @default.
- W2913305430 modified "2023-09-26" @default.
- W2913305430 title "Fusing heterogeneous genomic data to discover cancer progression related dynamic modules" @default.
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- W2913305430 doi "https://doi.org/10.1109/bibm.2018.8621165" @default.
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