{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T01:49:37Z","timestamp":1784166577570,"version":"3.55.0"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T00:00:00Z","timestamp":1641427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100003051","name":"New Energy and Industrial Technology Development Organization (NEDO","doi-asserted-by":"crossref","award":["AJD30064"],"award-info":[{"award-number":["AJD30064"]}],"id":[{"id":"10.13039\/501100003051","id-type":"DOI","asserted-by":"crossref"}]},{"name":"JST COI-NEXT"},{"name":"Grants-in-Aid for Scientific Research under","award":["18H03250"],"award-info":[{"award-number":["18H03250"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071278"],"award-info":[{"award-number":["62071278"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Recently, peptides have emerged as a promising class of pharmaceuticals for various diseases treatment poised between traditional small molecule drugs and therapeutic proteins. However, one of the key bottlenecks preventing them from therapeutic peptides is their toxicity toward human cells, and few available algorithms for predicting toxicity are specially designed for short-length peptides.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We present ToxIBTL, a novel deep learning framework by utilizing the information bottleneck principle and transfer learning to predict the toxicity of peptides as well as proteins. Specifically, we use evolutionary information and physicochemical properties of peptide sequences and integrate the information bottleneck principle into a feature representation learning scheme, by which relevant information is retained and the redundant information is minimized in the obtained features. Moreover, transfer learning is introduced to transfer the common knowledge contained in proteins to peptides, which aims to improve the feature representation capability. Extensive experimental results demonstrate that ToxIBTL not only achieves a higher prediction performance than state-of-the-art methods on the peptide dataset, but also has a competitive performance on the protein dataset. Furthermore, a user-friendly online web server is established as the implementation of the proposed ToxIBTL.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The proposed ToxIBTL and data can be freely accessible at http:\/\/server.wei-group.net\/ToxIBTL. Our source code is available at https:\/\/github.com\/WLYLab\/ToxIBTL.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac006","type":"journal-article","created":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T12:09:28Z","timestamp":1641298168000},"page":"1514-1524","source":"Crossref","is-referenced-by-count":158,"title":["ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1444-190X","authenticated-orcid":false,"given":"Lesong","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Tsukuba , Tsukuba 3058577, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiucai","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Tsukuba , Tsukuba 3058577, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tetsuya","family":"Sakurai","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Tsukuba , Tsukuba 3058577, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zengchao","family":"Mu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Shandong University , Weihai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leyi","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University , Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,1,6]]},"reference":[{"key":"2023020108590180400_btac006-B1","author":"Alemi","year":"2016"},{"key":"2023020108590180400_btac006-B2","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res"},{"key":"2023020108590180400_btac006-B3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1745-7580-6-6","article-title":"Identification of conformational B-cell Epitopes in an antigen from its primary sequence","volume":"6","author":"Ansari","year":"2010","journal-title":"Immunome Res"},{"key":"2023020108590180400_btac006-B4","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s00726-011-1095-8","article-title":"Extraordinary metabolic stability of peptides containing \u03b1-aminoxy acids","volume":"43","author":"Chen","year":"2012","journal-title":"Amino Acids"},{"key":"2023020108590180400_btac006-B5","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1093\/bib\/bbz152","article-title":"DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features","volume":"22","author":"Chu","year":"2021","journal-title":"Brief. 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