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• W3201560543 abstract "Protein-protein interaction (PPI) plays essential roles in nearly all biological processes of living organisms. Identification of PPI helps in understanding the cellular pathways and complex structure of proteins. Most of the works on the prediction of PPI utilized one type of information, mainly sequence-based. With the recent development in deep learning technologies, capturing the diverse and sparse biological dataset's relevant features is possible. This paper proposes a framework that incorporates a multi-modal representation of proteins to predict the interaction between them. Current work utilizes 3D structure and Gene ontology (GO) information to generate the vector representations of proteins. The task of PPI is divided into two phases: feature generation and prediction. We use deep learning algorithms in both stages. We validate our approach on two datasets: Human and S. cerevisiae. The trained model achieves accuracy values of 97.94% and 95.33% on the human and S. cerevisiae test sets, respectively. The results obtained over these datasets illustrate the superiority of the proposed method as compared to state-of-the-art methods." @default.
• W3201560543 created "2021-09-27" @default.
• W3201560543 creator A5053086856 @default.
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• W3201560543 date "2021-07-18" @default.
• W3201560543 modified "2023-09-24" @default.
• W3201560543 title "Prediction of Protein-Protein Interactions using Deep Multi-Modal Representations" @default.
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• W3201560543 doi "https://doi.org/10.1109/ijcnn52387.2021.9533478" @default.
• W3201560543 hasPublicationYear "2021" @default.
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