SLPred: a multi-view subcellular localization prediction tool for multi-location human proteins

Date

2022-09-01

Author

Özsarı, Gökhan
Rifaioglu, Ahmet Sureyya
Atakan, Ahmet
DOĞAN, TUNCA
Martin, Maria Jesus
Atalay, Rengul Cetin
Atalay, Mehmet Volkan

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Accurate prediction of the subcellular locations (SLs) of proteins is a critical topic in protein science. In this study, we present SLPred, an ensemble-based multi-view and multi-label protein subcellular localization prediction tool. For a query protein sequence, SLPred provides predictions for nine main SLs using independent machine-learning models trained for each location. We used UniProtKB/Swiss-Prot human protein entries and their curated SL annotations as our source data. We connected all disjoint terms in the UniProt SL hierarchy based on the corresponding term relationships in the cellular component category of Gene Ontology and constructed a training dataset that is both reliable and large scale using the re-organized hierarchy. We tested SLPred on multiple benchmarking datasets including our-in house sets and compared its performance against six state-of-the-art methods. Results indicated that SLPred outperforms other tools in the majority of cases.

URI

https://hdl.handle.net/11511/100343

Journal

BIOINFORMATICS

DOI

https://doi.org/10.1093/bioinformatics/btac458

Collections

Graduate School of Natural and Applied Sciences, Article

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Citation Formats

G. Özsarı et al., “SLPred: a multi-view subcellular localization prediction tool for multi-location human proteins,” BIOINFORMATICS, vol. 38, no. 17, pp. 4226–4229, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/100343.