WinBinVec: Cancer-Associated Protein-Protein Interaction Extraction and Identification of 20 Various Cancer Types and Metastasis Using Different Deep Learning Models

研究成果: Article同行評審

6 引文 斯高帕斯(Scopus)

摘要

Biophysical protein-protein interactions perform dominant roles in the initiation and progression of many cancer-related pathways. A protein-protein interaction might play different roles in diverse cancer types. Hence, prioritizing the PPIs in each cancer type would help detect cancer-associated pathways, find a better understanding of cancer biology, and facilitate drug discovery. Several studies to date have proposed computational methods for extracting the PPI essentiality of different cancer types based on the PPI network. The main drawback of these studies is not using a rich source such as genomics variant data. An amino acid sequence encodes useful information about protein structure and behavior. We represent each amino acid sequence based on its variants/mutations in seven different ways: binary vectors, pathogenicity scores, binding affinity changes upon mutations, gene expression-based network of the interactions, biophysicochemical properties, g-gap dipeptide, and one-hot vectors. Based on these representations, we design and consider seven different deep learning models. Then, we compare the accuracy of these models in predicting 20 different cancer types from the TCGA cohort. WinBinVec is a window-based model that outperforms the other models. Moreover, WinBinVec contains a PPI essentiality module that helps extract the essentiality probability of each PPI for every cancer type.

原文English
頁(從 - 到)4052-4063
頁數12
期刊IEEE Journal of Biomedical and Health Informatics
25
發行號10
DOIs
出版狀態Published - 2021 10月 1

All Science Journal Classification (ASJC) codes

  • 健康資訊管理
  • 健康資訊學
  • 電氣與電子工程
  • 電腦科學應用

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