Nov 5, 2018

PSL-Recommender: Protein Subcellular Localization Prediction using Recommender System

BioRxiv : the Preprint Server for Biology
Ruhollah JamaliSoheil Jahangiri-Tazehkand

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 inefficient wet-lab methods that are used for protein subcellular localization. Yet, there is still much room for improving the prediction accuracy of these methods. PSL-Recommender (Protein subcellular location recommender) is a method that employs neighborhood regularized logistic matrix factorization to build a recommender system for protein subcellular localization. The effectiveness of PSL-Recommender method is benchmarked on one human and three animals datasets. The results indicate that the PSL-Recommender significantly outperforms state-of-the-art methods, improving the previous best method up to 31% in F1 – mean, up to 28% in ACC, and up to 47% in AVG. The source of datasets and codes are available at: <https://github.com/RJamali/PSL-Recommender>

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Mentioned in this Paper

Acetyl-CoA: Carboxylase Deficiency
Establishment and Maintenance of Localization
Evaluation
Staphylococcal Protein A
Location
Subcellular Structure
Motivation

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