A Protein Interaction Information-based Generative Model for Enhancing Gene Clustering.

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ID: 85496
2020
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Abstract
In the field of computational bioinformatics, identifying a set of genes which are responsible for a particular cellular mechanism, is very much essential for tasks such as medical diagnosis or disease gene identification. Accurately grouping (clustering) the genes is one of the important tasks in understanding the functionalities of the disease genes. In this regard, ensemble clustering becomes a promising approach to combine different clustering solutions to generate almost accurate gene partitioning. Recently, researchers have used generative model as a smart ensemble method to produce the right consensus solution. In the current paper, we develop a protein-protein interaction-based generative model that can efficiently perform a gene clustering. Utilizing protein interaction information as the generative model's latent variable enables enhance the generative model's efficiency in inferring final probabilistic labels. The proposed generative model utilizes different weak supervision sources rather utilizing any ground truth information. For weak supervision sources, we use a multi-objective optimization based clustering technique together with the world's largest gene ontology based knowledge-base named Gene Ontology Consortium(GOC). These weakly supervised labels are supplied to a generative model that eventually assigns all genes to probabilistic labels. The comparative study with respect to silhouette score, Biological Homogeneity Index (BHI) and Biological Stability Index (BSI) proves that the proposed generative model outperforms than other state-of-the-art techniques.
Reference Key
dutta2020ascientific Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Dutta, Pratik;Saha, Sriparna;Pai, Sanket;Kumar, Aviral;
Journal Scientific reports
Year 2020
DOI
10.1038/s41598-020-57437-5
URL
Keywords
epigenetics chromatin epiblast stem cells haploid self-renewal

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