A SURVEY ON PROTEIN-TO-PROTEIN INTERACTION PREDICTION USING TRANSFER LEARNING.

Autor:	Alam, Afaque, Sachi, Savya, Kumar, Santosh
Předmět:	DRUG discovery PROTEIN-protein interactions FORECASTING TECHNOLOGY transfer PREDICTION models
Zdroj:	Biochemical & Cellular Archives; Oct2024, Vol. 24 Issue 2, p2391-2403, 13p
Abstrakt:	Protein-protein interactions (PPIs) play a crucial role in various biological processes and pathways, making them attractive targets for drug discovery. This paper presents a comprehensive survey of transfer learning techniques applied to PPI prediction. We explore how transfer learning approaches can enhance the efficiency and accuracy of PPI prediction by leveraging knowledge from related tasks or domains. Our review investigates the effectiveness of various transfer learning models, identifies factors influencing their performance, and highlights potential areas for further research. We emphasize the significance of transfer learning-based models for PPI prediction in advancing our understanding of biological processes and developing novel therapeutic strategies. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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