1. EnsemBic: An effective ensemble of biclustering to identify potential biomarkers of esophageal squamous cell carcinoma.
- Author
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Baruah B, Dutta MP, Banerjee S, and Bhattacharyya DK
- Subjects
- Humans, Algorithms, Cluster Analysis, Esophageal Squamous Cell Carcinoma genetics, Esophageal Neoplasms genetics, Biomarkers, Tumor genetics
- Abstract
The development of functionally enriched and biologically competent biclustering algorithm is essential for extracting hidden information from massive biological datasets. This paper presents a novel biclustering ensemble called EnsemBic based on p-value, which calculates the functional similarity of genetic associations. To validate the effectiveness and robustness of EnsemBic, we apply three well-known biclustering techniques, viz. Laplace Prior, iBBiG, and xMotif to implement EnsemBic and have been compared using different leading parameters. It is observed that the EnsemBic outperforms its competing algorithms in several prominent functional and biological measures. Next, the biclusters obtained from EnsemBic are used to identify potential biomarkers of Esophageal Squamous Cell Carcinoma (ESCC) by exploring topological and biological relevance with reference to the elite genes, attained from genecards. Finally, we discover that the genes F2RL3, APPL1, CALM1, IFNGR1, LPAR1, ANGPT2, ARPC2, CGN, CLDN7, ATP6V1C2, CEACAM1, FTL, PLAU,PSMB4, and EPHB2 carry both the topological and biological significance of previously established ESCC elite genes. Therefore, we declare the aforementioned genes as potential biomarkers of ESCC., Competing Interests: Declaration of Competing Interest I declare that there are no conflicts of interest related to this manuscript. I have no financial or personal relationships that could influence the research or the interpretation of the results., (Copyright © 2024 Elsevier Ltd. All rights reserved.)
- Published
- 2024
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