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• W2898847664 abstract "Abstract Identifying a protein’s subcellular location is of great interest for understanding its function and behavior within the cell. In the last decade, many computational approaches have been proposed as a surrogate for expensive and labor-intensive wet-lab methods that are used for protein subcellular localization. Yet, there is still much room for improving the prediction accuracy of these methods. In this article, we meant to develop a customized computational method rather than using common machine learning predictors, which are used in the majority of computational research on this topic. The neighbourhood regularized logistic matrix factorization technique was used to create PSL-Recommender (Protein subcellular location recommender), a GO-based predictor. We declared statistical inference as the driving force behind the PSL-Recommender here. Following that, it was benchmarked against twelve well-known methods using five different datasets, demonstrating outstanding performance. Finally, we discussed potential research avenues for developing a comprehensive prediction tool for protein subcellular location prediction. The datasets and codes are available at: https://github.com/RJamali/PSL-Recommender" @default.
• W2898847664 created "2018-11-09" @default.
• W2898847664 creator A5001359653 @default.
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• W2898847664 date "2021-09-07" @default.
• W2898847664 modified "2023-09-23" @default.
• W2898847664 title "PSL-Recommender: Protein Subcellular Localization Prediction using Recommender System" @default.
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• W2898847664 doi "https://doi.org/10.21203/rs.3.rs-878139/v1" @default.
• W2898847664 hasPublicationYear "2021" @default.
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