{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T02:46:20Z","timestamp":1770345980022,"version":"3.49.0"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T00:00:00Z","timestamp":1669939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62225209"],"award-info":[{"award-number":["62225209"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hunan Provincial Science and Technology Program"},{"name":"Hunan Province","award":["2021RC4008"],"award-info":[{"award-number":["2021RC4008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Protein essentiality is usually accepted to be a conditional trait and strongly affected by cellular environments. However, existing computational methods often do not take such characteristics into account, preferring to incorporate all available data and train a general model for all cell lines. In addition, the lack of model interpretability limits further exploration and analysis of essential protein predictions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this study, we proposed DeepCellEss, a sequence-based interpretable deep learning framework for cell line-specific essential protein predictions. DeepCellEss utilizes a convolutional neural network and bidirectional long short-term memory to learn short- and long-range latent information from protein sequences. Further, a multi-head self-attention mechanism is used to provide residue-level model interpretability. For model construction, we collected extremely large-scale benchmark datasets across 323 cell lines. Extensive computational experiments demonstrate that DeepCellEss yields effective prediction performance for different cell lines and outperforms existing sequence-based methods as well as network-based centrality measures. Finally, we conducted some case studies to illustrate the necessity of considering specific cell lines and the superiority of DeepCellEss. We believe that DeepCellEss can serve as a useful tool for predicting essential proteins across different cell lines.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The DeepCellEss web server is available at http:\/\/csuligroup.com:8000\/DeepCellEss. The source code and data underlying this study can be obtained from https:\/\/github.com\/CSUBioGroup\/DeepCellEss.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac779","type":"journal-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T14:48:22Z","timestamp":1669992502000},"source":"Crossref","is-referenced-by-count":26,"title":["DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning"],"prefix":"10.1093","volume":"39","author":[{"given":"Yiming","family":"Li","sequence":"first","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1726-0955","authenticated-orcid":false,"given":"Min","family":"Zeng","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}]},{"given":"Fuhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4593-9332","authenticated-orcid":false,"given":"Fang-Xiang","family":"Wu","sequence":"additional","affiliation":[{"name":"Division of Biomedical Engineering, Department of Computer Science, Department of Mechanical Engineering University of Saskatchewan , Saskatoon, SK S7N 5A9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0188-1394","authenticated-orcid":false,"given":"Min","family":"Li","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}]}],"member":"286","published-online":{"date-parts":[[2022,12,2]]},"reference":[{"key":"2023010805353898300_btac779-B1","doi-asserted-by":"crossref","first-page":"e2","DOI":"10.1371\/journal.pbio.0020002","article-title":"JAMM: a metalloprotease-like zinc site in the proteasome and signalosome","volume":"2","author":"Ambroggio","year":"2004","journal-title":"PLoS Biol"},{"key":"2023010805353898300_btac779-B2","doi-asserted-by":"crossref","first-page":"W39","DOI":"10.1093\/nar\/gkv416","article-title":"The MEME suite","volume":"43","author":"Bailey","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023010805353898300_btac779-B3","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1038\/nrg.2017.75","article-title":"Human gene essentiality","volume":"19","author":"Bartha","year":"2018","journal-title":"Nat. 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