Nov 5, 2018

PSL-Recommender: Protein Subcellular Localization Prediction using Recommender System

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


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: <>

  • References
  • Citations


  • We're still populating references for this paper, please check back later.
  • References
  • Citations


  • This paper may not have been cited yet.

Mentioned in this Paper

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

About this Paper

Related Feeds

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.