{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T16:56:11Z","timestamp":1776185771300,"version":"3.50.1"},"reference-count":78,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s11432-024-4147-8","type":"journal-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T08:01:48Z","timestamp":1729929708000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["TPpred-SC: multi-functional therapeutic peptide prediction based on multi-label supervised contrastive learning"],"prefix":"10.1007","volume":"67","author":[{"given":"Ke","family":"Yan","sequence":"first","affiliation":[]},{"given":"Hongwu","family":"Lv","sequence":"additional","affiliation":[]},{"given":"Jiangyi","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Shutao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,22]]},"reference":[{"key":"4147_CR1","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.drudis.2014.10.003","volume":"20","author":"K Fosgerau","year":"2015","unstructured":"Fosgerau K, Hoffmann T. Peptide therapeutics: current status and future directions. Drug Discov Today, 2015, 20: 122\u2013128","journal-title":"Drug Discov Today"},{"key":"4147_CR2","doi-asserted-by":"publisher","first-page":"2700","DOI":"10.1016\/j.bmc.2017.06.052","volume":"26","author":"J L Lau","year":"2018","unstructured":"Lau J L, Dunn M K. Therapeutic peptides: historical perspectives, current development trends, and future directions. Bioorg Med Chem, 2018, 26: 2700\u20132707","journal-title":"Bioorg Med Chem"},{"key":"4147_CR3","doi-asserted-by":"publisher","first-page":"23998","DOI":"10.1021\/acsomega.1c03132","volume":"6","author":"L Cai","year":"2021","unstructured":"Cai L, Wang L, Fu X, et al. Active semisupervised model for improving the identification of anticancer peptides. ACS Omega, 2021, 6: 23998\u201324008","journal-title":"ACS Omega"},{"key":"4147_CR4","doi-asserted-by":"publisher","first-page":"D1119","DOI":"10.1093\/nar\/gkv1114","volume":"44","author":"S Singh","year":"2016","unstructured":"Singh S, Chaudhary K, Dhanda S K, et al. SATPdb: a database of structurally annotated therapeutic peptides. Nucleic Acids Res, 2016, 44: D1119\u2013D1126","journal-title":"Nucleic Acids Res"},{"key":"4147_CR5","doi-asserted-by":"publisher","first-page":"0011","DOI":"10.34133\/research.0011","volume":"2022","author":"C Ao","year":"2022","unstructured":"Ao C, Jiao S, Wang Y, et al. Biological sequence classification: a review on data and general methods. Research, 2022, 2022: 0011","journal-title":"Research"},{"key":"4147_CR6","doi-asserted-by":"publisher","first-page":"D1123","DOI":"10.1093\/nar\/gkab957","volume":"50","author":"C Cao","year":"2022","unstructured":"Cao C, Wang J, Kwok D, et al. webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study. Nucleic Acids Res, 2022, 50: D1123\u2013D1130","journal-title":"Nucleic Acids Res"},{"key":"4147_CR7","doi-asserted-by":"publisher","first-page":"bbaa275","DOI":"10.1093\/bib\/bbaa275","volume":"22","author":"L Wei","year":"2021","unstructured":"Wei L, He W, Malik A, et al. Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework. Brief BioInf, 2021, 22: bbaa275","journal-title":"Brief BioInf"},{"key":"4147_CR8","doi-asserted-by":"publisher","first-page":"4272","DOI":"10.1093\/bioinformatics\/btz246","volume":"35","author":"L Wei","year":"2019","unstructured":"Wei L, Zhou C, Su R, et al. PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning. Bioinformatics, 2019, 35: 4272\u20134280","journal-title":"Bioinformatics"},{"key":"4147_CR9","doi-asserted-by":"publisher","first-page":"e1010511","DOI":"10.1371\/journal.pcbi.1010511","volume":"18","author":"W Yan","year":"2022","unstructured":"Yan W, Tang W, Wang L, et al. PrMFTP: multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization. PLoS Comput Biol, 2022, 18: e1010511","journal-title":"PLoS Comput Biol"},{"key":"4147_CR10","doi-asserted-by":"publisher","first-page":"bbab414","DOI":"10.1093\/bib\/bbab414","volume":"23","author":"W Tang","year":"2022","unstructured":"Tang W, Dai R, Yan W, et al. Identifying multi-functional bioactive peptide functions using multi-label deep learning. Brief BioInf, 2022, 23: bbab414","journal-title":"Brief BioInf"},{"key":"4147_CR11","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhai Y, Ding Y, et al. SBSM-pro: support bio-sequence machine for proteins. 2023. ArXiv:2308.10275","DOI":"10.1007\/s11432-024-4171-9"},{"key":"4147_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.xcrm.2022.100794","volume":"3","author":"X Zeng","year":"2022","unstructured":"Zeng X, Wang F, Luo Y, et al. Deep generative molecular design reshapes drug discovery. Cell Reports Medicine, 2022, 3:1\u201313","journal-title":"Cell Reports Medicine"},{"key":"4147_CR13","doi-asserted-by":"publisher","first-page":"174","DOI":"10.2174\/1574893617666211220153429","volume":"17","author":"K Yan","year":"2022","unstructured":"Yan K, Lv H, Wen J, et al. TP-MV: therapeutic peptides prediction by multi-view learning. CBIO, 2022, 17: 174\u2013183","journal-title":"CBIO"},{"key":"4147_CR14","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1016\/j.ab.2007.10.012","volume":"373","author":"H B Shen","year":"2008","unstructured":"Shen H B, Chou K C. PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Anal Biochem, 2008, 373: 386\u2013388","journal-title":"Anal Biochem"},{"key":"4147_CR15","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/978-1-4419-9326-7_11","volume-title":"Ensemble Machine Learning: Methods and Applications","author":"Y Qi","year":"2012","unstructured":"Qi Y. Random forest for bioinformatics. Ensemble Machine Learning: Methods and Applications. Springer. 2012: 307\u2013323"},{"key":"4147_CR16","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1002\/sim.4780080803","volume":"8","author":"O O Aalen","year":"1989","unstructured":"Aalen O O. A linear regression model for the analysis of life times. Stat Med, 1989, 8: 907\u2013925","journal-title":"Stat Med"},{"key":"4147_CR17","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/5254.708428","volume":"13","author":"M A Hearst","year":"1998","unstructured":"Hearst M A, Dumais S T, Osuna E, et al. Support vector machines. IEEE Intell Syst Their Appl, 1998, 13: 18\u201328","journal-title":"IEEE Intell Syst Their Appl"},{"key":"4147_CR18","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1186\/s12915-023-01596-0","volume":"21","author":"C Ao","year":"2023","unstructured":"Ao C, Ye X, Sakurai T, et al. m5U-SVM: identification of RNA 5-methyluridine modification sites based on multi-view features of physicochemical features and distributed representation. BMC Biol, 2023, 21: 93","journal-title":"BMC Biol"},{"key":"4147_CR19","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhai Y, Ding Y, et al. SBSM-pro: support bio-sequence machine for proteins. 2023. ArXiv:2308.10275","DOI":"10.1007\/s11432-024-4171-9"},{"key":"4147_CR20","doi-asserted-by":"publisher","first-page":"e129","DOI":"10.1093\/nar\/gkab829","volume":"49","author":"H L Li","year":"2021","unstructured":"Li H L, Pang Y H, Liu B. BioSeq-BLM: a platform for analyzing DNA, RNA and protein sequences based on biological language models. Nucleic Acids Res, 2021, 49: e129","journal-title":"Nucleic Acids Res"},{"key":"4147_CR21","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1142\/S0219720005001004","volume":"03","author":"C Ding","year":"2005","unstructured":"Ding C, Peng H. Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol, 2005, 03: 185\u2013205","journal-title":"J Bioinform Comput Biol"},{"key":"4147_CR22","doi-asserted-by":"publisher","first-page":"3982","DOI":"10.1093\/bioinformatics\/btaa275","volume":"36","author":"Y P Zhang","year":"2020","unstructured":"Zhang Y P, Zou Q, Luigi Martelli P. PPTPP: a novel therapeutic peptide prediction method using physicochemical property encoding and adaptive feature representation learning. Bioinformatics, 2020, 36: 3982\u20133987","journal-title":"Bioinformatics"},{"key":"4147_CR23","doi-asserted-by":"publisher","first-page":"2712","DOI":"10.1093\/bioinformatics\/btac200","volume":"38","author":"K Yan","year":"2022","unstructured":"Yan K, Lv H, Guo Y, et al. TPpred-ATMV: therapeutic peptide prediction by adaptive multi-view tensor learning model. Bioinformatics, 2022, 38: 2712\u20132718","journal-title":"Bioinformatics"},{"key":"4147_CR24","doi-asserted-by":"publisher","first-page":"btad059","DOI":"10.1093\/bioinformatics\/btad059","volume":"39","author":"L Chen","year":"2023","unstructured":"Chen L, Yu L, Gao L, et al. Potent antibiotic design via guided search from antibacterial activity evaluations. Bioinformatics, 2023, 39: btad059","journal-title":"Bioinformatics"},{"key":"4147_CR25","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1038\/s41746-023-00887-8","volume":"6","author":"H Yang","year":"2023","unstructured":"Yang H, Luo Y M, Ma C Y, et al. A gender specific risk assessment of coronary heart disease based on physical examination data. npj Digit Med, 2023, 6: 136","journal-title":"npj Digit Med"},{"key":"4147_CR26","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1038\/s42256-022-00557-6","volume":"4","author":"X Zeng","year":"2022","unstructured":"Zeng X, Xiang H, Yu L, et al. Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework. Nat Mach Intell, 2022, 4: 1004\u20131016","journal-title":"Nat Mach Intell"},{"key":"4147_CR27","doi-asserted-by":"publisher","first-page":"bbab041","DOI":"10.1093\/bib\/bbab041","volume":"22","author":"L Wei","year":"2021","unstructured":"Wei L, Ye X, Xue Y, et al. ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism. Brief BioInf, 2021, 22: bbab041","journal-title":"Brief BioInf"},{"key":"4147_CR28","doi-asserted-by":"publisher","first-page":"2740","DOI":"10.1093\/bioinformatics\/bty179","volume":"34","author":"D Veltri","year":"2018","unstructured":"Veltri D, Kamath U, Shehu A, et al. Deep learning improves antimicrobial peptide recognition. Bioinformatics, 2018, 34: 2740\u20132747","journal-title":"Bioinformatics"},{"key":"4147_CR29","doi-asserted-by":"publisher","first-page":"4007","DOI":"10.1093\/bioinformatics\/bty451","volume":"34","author":"L Wei","year":"2018","unstructured":"Wei L, Zhou C, Chen H, et al. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics, 2018, 34: 4007\u20134016","journal-title":"Bioinformatics"},{"key":"4147_CR30","doi-asserted-by":"publisher","first-page":"btac715","DOI":"10.1093\/bioinformatics\/btac715","volume":"39","author":"K Yan","year":"2023","unstructured":"Yan K, Lv H, Guo Y, et al. sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure. Bioinformatics, 2023, 39: btac715","journal-title":"Bioinformatics"},{"key":"4147_CR31","doi-asserted-by":"publisher","first-page":"btad125","DOI":"10.1093\/bioinformatics\/btad125","volume":"39","author":"K Yan","year":"2023","unstructured":"Yan K, Guo Y, Liu B, et al. PreTP-2L: identification of therapeutic peptides and their types using two-layer ensemble learning framework. Bioinformatics, 2023, 39: btad125","journal-title":"Bioinformatics"},{"key":"4147_CR32","doi-asserted-by":"publisher","first-page":"105717","DOI":"10.1016\/j.compbiomed.2022.105717","volume":"147","author":"J Yan","year":"2022","unstructured":"Yan J, Zhang B, Zhou M, et al. Multi-Branch-CNN: classification of ion channel interacting peptides using multi-branch convolutional neural network. Comput Biol Med, 2022, 147: 105717","journal-title":"Comput Biol Med"},{"key":"4147_CR33","doi-asserted-by":"publisher","first-page":"1831","DOI":"10.1109\/TCBB.2020.2968419","volume":"18","author":"J Zhang","year":"2020","unstructured":"Zhang J, Zhang Z, Pu L, et al. AIEpred: an ensemble predictive model of classifier chain to identify anti-inflammatory peptides. IEEE ACM Trans Comput Biol Bioinf, 2020, 18: 1831\u20131840","journal-title":"IEEE ACM Trans Comput Biol Bioinf"},{"key":"4147_CR34","unstructured":"O\u2019Shea K. An introduction to convolutional neural networks. 2015. ArXiv:1511.08458"},{"key":"4147_CR35","unstructured":"Chung J, Gulcehre C, Cho K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling. 2014. ArXiv:1412.3555"},{"key":"4147_CR36","first-page":"1","volume-title":"Proceedings of Conference on Neural Information Processing Systems, Long Beach","author":"A Vaswani","year":"2017","unstructured":"Vaswani A. Attention is all you need. In: Proceedings of Conference on Neural Information Processing Systems, Long Beach, 2017. 1\u201311"},{"key":"4147_CR37","unstructured":"Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space. 2013. ArXiv:1301.3781"},{"key":"4147_CR38","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1186\/s12915-023-01740-w","volume":"21","author":"H Lv","year":"2023","unstructured":"Lv H, Yan K, Liu B. TPpred-LE: therapeutic peptide function prediction based on label embedding. BMC Biol, 2023, 21: 238","journal-title":"BMC Biol"},{"key":"4147_CR39","doi-asserted-by":"publisher","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","volume":"25","author":"S Altschul","year":"1997","unstructured":"Altschul S. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res, 1997, 25: 3389\u20133402","journal-title":"Nucleic Acids Res"},{"key":"4147_CR40","first-page":"1","volume":"32","author":"R Rao","year":"2019","unstructured":"Rao R, Bhattacharya N, Thomas N, et al. Evaluating protein transfer learning with TAPE. In: Proceedings of Conference on Neural Information Processing Systems, Vancouver, 2019. 32: 1\u201313","journal-title":"Proceedings of Conference on Neural Information Processing Systems, Vancouver"},{"key":"4147_CR41","doi-asserted-by":"publisher","first-page":"7112","DOI":"10.1109\/TPAMI.2021.3095381","volume":"44","author":"A Elnaggar","year":"2021","unstructured":"Elnaggar A, Heinzinger M, Dallago C, et al. ProtTrans: toward understanding the language of life through self-supervised learning. IEEE Trans Pattern Anal Mach Intell, 2021, 44: 7112\u20137127","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4147_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-019-3220-8","volume":"20","author":"M Heinzinger","year":"2019","unstructured":"Heinzinger M, Elnaggar A, Wang Y, et al. Modeling aspects of the language of life through transfer-learning protein sequences. BMC BioInf, 2019, 20: 1\u20137","journal-title":"BMC BioInf"},{"key":"4147_CR43","doi-asserted-by":"publisher","first-page":"107260","DOI":"10.1016\/j.compbiomed.2023.107260","volume":"164","author":"Z Li","year":"2023","unstructured":"Li Z, Jin J, Long W, et al. PLPMpro: enhancing promoter sequence prediction with prompt-learning based pre-trained language model. Comput Biol Med, 2023, 164: 107260","journal-title":"Comput Biol Med"},{"key":"4147_CR44","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1186\/s13059-022-02780-1","volume":"23","author":"J Jin","year":"2022","unstructured":"Jin J, Yu Y, Wang R, et al. iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations. Genome Biol, 2022, 23: 219","journal-title":"Genome Biol"},{"key":"4147_CR45","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1038\/s41587-021-01156-3","volume":"40","author":"F Teufel","year":"2022","unstructured":"Teufel F, Almagro Armenteros J J, Johansen A R, et al. SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat Biotechnol, 2022, 40: 1023\u20131025","journal-title":"Nat Biotechnol"},{"key":"4147_CR46","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1186\/s12859-022-04952-z","volume":"23","author":"M Salem","year":"2022","unstructured":"Salem M, Keshavarzi Arshadi A, Yuan J S. AMPDeep: hemolytic activity prediction of antimicrobial peptides using transfer learning. BMC BioInf, 2022, 23: 389","journal-title":"BMC BioInf"},{"key":"4147_CR47","doi-asserted-by":"publisher","first-page":"bbab422","DOI":"10.1093\/bib\/bbab422","volume":"23","author":"R Sharma","year":"2022","unstructured":"Sharma R, Shrivastava S, Kumar Singh S, et al. Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM. Brief BioInf, 2022, 23: bbab422","journal-title":"Brief BioInf"},{"key":"4147_CR48","first-page":"vbac021","volume":"2","author":"W Dee","year":"2022","unstructured":"Dee W, Gromiha M. LMPred: predicting antimicrobial peptides using pre-trained language models and deep learning. BioInf Adv, 2022, 2: vbac021","journal-title":"BioInf Adv"},{"key":"4147_CR49","doi-asserted-by":"publisher","first-page":"4172","DOI":"10.1093\/bioinformatics\/btab422","volume":"37","author":"J Cheng","year":"2021","unstructured":"Cheng J, Bendjama K, Rittner K, et al. BERTMHC: improved MHC-peptide class II interaction prediction with transformer and multiple instance learning. Bioinformatics, 2021, 37: 4172\u20134179","journal-title":"Bioinformatics"},{"key":"4147_CR50","doi-asserted-by":"publisher","first-page":"2556","DOI":"10.1093\/bioinformatics\/btab133","volume":"37","author":"P Charoenkwan","year":"2021","unstructured":"Charoenkwan P, Nantasenamat C, Hasan M M, et al. BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides. Bioinformatics, 2021, 37: 2556\u20132562","journal-title":"Bioinformatics"},{"key":"4147_CR51","doi-asserted-by":"publisher","first-page":"106423","DOI":"10.1016\/j.compbiomed.2022.106423","volume":"152","author":"M Romero","year":"2023","unstructured":"Romero M, Nakano F K, Finke J, et al. Leveraging class hierarchy for detecting missing annotations on hierarchical multi-label classification. Comput Biol Med, 2023, 152: 106423","journal-title":"Comput Biol Med"},{"key":"4147_CR52","doi-asserted-by":"publisher","first-page":"180","DOI":"10.2174\/092986613804725307","volume":"20","author":"M Khosravian","year":"2013","unstructured":"Khosravian M, Kazemi Faramarzi F, Mohammad Beigi M, et al. Predicting Antibacterial peptides by the concept of Chou\u2019s pseudo-amino acid composition and machine learning methods. Protein Peptide Lett, 2013, 20: 180\u2013186","journal-title":"Protein Peptide Lett"},{"key":"4147_CR53","doi-asserted-by":"publisher","first-page":"4310","DOI":"10.3390\/ijms21124310","volume":"21","author":"M Burdukiewicz","year":"2020","unstructured":"Burdukiewicz M, Sidorczuk K, Rafacz D, et al. Proteomic screening for prediction and design of antimicrobial peptides with ampgram. Int J Mol Sci, 2020, 21: 4310","journal-title":"Int J Mol Sci"},{"key":"4147_CR54","doi-asserted-by":"publisher","first-page":"4691","DOI":"10.1021\/acs.jcim.0c00841","volume":"60","author":"K Kavousi","year":"2020","unstructured":"Kavousi K, Bagheri M, Behrouzi S, et al. IAMPE: NMR-assisted computational prediction of antimicrobial peptides. J Chem Inf Model, 2020, 60: 4691\u20134701","journal-title":"J Chem Inf Model"},{"key":"4147_CR55","doi-asserted-by":"publisher","first-page":"193907","DOI":"10.1109\/ACCESS.2020.3031549","volume":"8","author":"P H Le-Khac","year":"2020","unstructured":"Le-Khac P H, Healy G, Smeaton A F. Contrastive representation learning: a framework and review. IEEE Access, 2020, 8: 193907","journal-title":"IEEE Access"},{"key":"4147_CR56","first-page":"18661","volume":"33","author":"P Khosla","year":"2020","unstructured":"Khosla P, Teterwak P, Wang C, et al. Supervised contrastive learning. In: Proceedings of Conference on Neural Information Processing Systems, Vancouver, 2020. 33: 18661\u201318673","journal-title":"Proceedings of Conference on Neural Information Processing Systems, Vancouver"},{"key":"4147_CR57","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3390\/technologies9010002","volume":"9","author":"A Jaiswal","year":"2020","unstructured":"Jaiswal A, Babu A R, Zadeh M Z, et al. A survey on contrastive self-supervised learning. Technologies, 2020, 9: 2","journal-title":"Technologies"},{"key":"4147_CR58","first-page":"6827","volume":"33","author":"Y Tian","year":"2020","unstructured":"Tian Y, Sun C, Poole B, et al. What makes for good views for contrastive learning? In: Proceedings of Conference on Neural Information Processing Systems, Vancouver, 2020. 33: 6827\u20136839","journal-title":"Proceedings of Conference on Neural Information Processing Systems, Vancouver"},{"key":"4147_CR59","first-page":"1597","volume-title":"Proceedings of International conference on machine learning, PMLR","author":"T Chen","year":"2020","unstructured":"Chen T, Kornblith S, Norouzi M, et al. A simple framework for contrastive learning of visual representations. In: Proceedings of International conference on machine learning, PMLR, 2020. 1597\u20131607"},{"key":"4147_CR60","doi-asserted-by":"crossref","unstructured":"Shen H, Price L C, Bahadori T, et al. Improving generalizability of protein sequence models with data augmentations. 2021. BioRxiv: 2021.02","DOI":"10.1101\/2021.02.18.431877"},{"key":"4147_CR61","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/BF02300754","volume":"19","author":"S French","year":"1983","unstructured":"French S, Robson B. What is a conservative substitution? J Mol Evol, 1983, 19: 171\u2013175","journal-title":"J Mol Evol"},{"key":"4147_CR62","unstructured":"Devlin J. Bert: Pre-training of deep bidirectional transformers for language understanding. 2018. ArXiv:1810.04805"},{"key":"4147_CR63","doi-asserted-by":"publisher","first-page":"170","DOI":"10.2174\/1574893618666221214091824","volume":"18","author":"C Polanco","year":"2023","unstructured":"Polanco C, Uversky V N, Huberman A, et al. Bioinformatics study of the DNA and RNA viruses infecting plants and bacteria that could potentially affect animals and humans. CBIO, 2023, 18: 170\u2013191","journal-title":"CBIO"},{"key":"4147_CR64","doi-asserted-by":"publisher","first-page":"2627","DOI":"10.1016\/S1352-2310(97)00447-0","volume":"32","author":"M W Gardner","year":"1998","unstructured":"Gardner M W, Dorling S R. Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmos Environ, 1998, 32: 2627\u20132636","journal-title":"Atmos Environ"},{"key":"4147_CR65","first-page":"2495","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, Virtual","author":"F Wang","year":"2021","unstructured":"Wang F, Liu H. Understanding the behaviour of contrastive loss. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, Virtual, 2021. 2495\u20132504"},{"key":"4147_CR66","first-page":"1","volume-title":"Proceedings of Conference on Neural Information Processing Systems, New Orleans","author":"V Zaigrajew","year":"2022","unstructured":"Zaigrajew V, Zieba M. Contrastive learning for multi-label classification. In: Proceedings of Conference on Neural Information Processing Systems, New Orleans, 2022. 1\u20138"},{"key":"4147_CR67","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1175\/1520-0434(1996)011<0003:TFAASE>2.0.CO;2","volume":"11","author":"A H Murphy","year":"1996","unstructured":"Murphy A H. The finley affair: a signal event in the history of forecast verification. Wea Forecasting, 1996, 11: 3\u201320","journal-title":"Wea Forecasting"},{"key":"4147_CR68","first-page":"1","volume-title":"Proceedings of IEEE conference on computational intelligence in bioinformatics and computational biology, Vina del Mar","author":"S Jadon","year":"2020","unstructured":"Jadon S. A survey of loss functions for semantic segmentation. In: Proceedings of IEEE conference on computational intelligence in bioinformatics and computational biology, Vina del Mar, 2020. 1\u20137"},{"key":"4147_CR69","unstructured":"Loshchilov I. Decoupled weight decay regularization. 2017. ArXiv:1711.05101"},{"key":"4147_CR70","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","volume":"30","author":"A P Bradley","year":"1997","unstructured":"Bradley A P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 1997, 30: 1145\u20131159","journal-title":"Pattern Recognition"},{"key":"4147_CR71","doi-asserted-by":"publisher","first-page":"254","DOI":"10.2174\/1574893616666211130125206","volume":"17","author":"J Wu","year":"2022","unstructured":"Wu J, Qu L, Yang G, et al. Diabetes induced factors prediction based on various improved machine learning methods. CBIO, 2022, 17: 254\u2013262","journal-title":"CBIO"},{"key":"4147_CR72","unstructured":"Powers D M W. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. 2020. ArXiv:2010.16061"},{"key":"4147_CR73","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-019-6413-7","volume":"21","author":"D Chicco","year":"2020","unstructured":"Chicco D, Jurman G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 2020, 21: 1\u20133","journal-title":"BMC Genomics"},{"key":"4147_CR74","doi-asserted-by":"publisher","first-page":"1281880","DOI":"10.3389\/fmed.2023.1281880","volume":"10","author":"X Zou","year":"2023","unstructured":"Zou X, Ren L, Cai P, et al. Accurately identifying hemagglutinin using sequence information and machine learning methods. Front Med, 2023, 10: 1281880","journal-title":"Front Med"},{"key":"4147_CR75","doi-asserted-by":"publisher","first-page":"2465","DOI":"10.3390\/diagnostics13142465","volume":"13","author":"W Zhu","year":"2023","unstructured":"Zhu W, Yuan S S, Li J, et al. A first computational frame for recognizing heparin-binding protein. Diagnostics, 2023, 13: 2465","journal-title":"Diagnostics"},{"key":"4147_CR76","doi-asserted-by":"publisher","first-page":"e1011214","DOI":"10.1371\/journal.pcbi.1011214","volume":"19","author":"H Li","year":"2023","unstructured":"Li H, Liu B, Libbrecht M W. BioSeq-Diabolo: Biological sequence similarity analysis using Diabolo. PLoS Comput Biol, 2023, 19: e1011214","journal-title":"PLoS Comput Biol"},{"key":"4147_CR77","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar T M. A survey on image data augmentation for deep learning. J Big Data, 2019, 6: 1\u201348","journal-title":"J Big Data"},{"key":"4147_CR78","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wics.101","volume":"2","author":"H Abdi","year":"2010","unstructured":"Abdi H, Williams L J. Principal component analysis. WIREs Comput Stats, 2010, 2: 433\u2013459","journal-title":"WIREs Comput Stats"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-024-4147-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-024-4147-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-024-4147-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:02:11Z","timestamp":1766268131000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-024-4147-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,22]]},"references-count":78,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["4147"],"URL":"https:\/\/doi.org\/10.1007\/s11432-024-4147-8","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,22]]},"assertion":[{"value":"13 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"212105"}}