{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:03:02Z","timestamp":1790787782675,"version":"4.1.0"},"reference-count":70,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"vor","delay-in-days":62,"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":["62450002"],"award-info":[{"award-number":["62450002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62425107"],"award-info":[{"award-number":["62425107"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2022ZD0117700"],"award-info":[{"award-number":["2022ZD0117700"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Clathrin proteins, key elements of the vesicle coat, play a crucial role in various cellular processes, including neural function, signal transduction, and endocytosis. Disruptions in clathrin protein functions have been associated with a wide range of diseases, such as Alzheimer\u2019s, neurodegeneration, viral infection, and cancer. Therefore, correctly identifying clathrin protein functions is critical to unravel the mechanism of these fatal diseases and designing drug targets. This paper presents a novel computational method, named TargetCLP, to precisely identify clathrin proteins. TargetCLP leverages four single-view feature representation methods, including two transformed feature sets (PSSM-CLBP and RECM-CLBP), one qualitative characteristics feature, and one deep-learned-based embedding using ESM. The single-view features are integrated based on their weights using differential evolution, and the BTG feature selection algorithm is utilized to generate a more optimal and reduced subset. The model is trained using various classifiers, among which the proposed SnBiLSTM achieved remarkable performance. Experimental and comparative results on both training and independent datasets show that the proposed TargetCLP offers significant improvements in terms of both prediction accuracy and generalization to unseen data, furthering advancements in the research field.<\/jats:p>","DOI":"10.1093\/bib\/bbaf026","type":"journal-article","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T02:12:58Z","timestamp":1737598378000},"source":"Crossref","is-referenced-by-count":50,"title":["TargetCLP: clathrin proteins prediction combining transformed and evolutionary scale modeling-based multi-view features via weighted feature integration approach"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9461-6290","authenticated-orcid":false,"given":"Matee","family":"Ullah","sequence":"first","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , Chengdu, Sichuan 610054 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1106-0012","authenticated-orcid":false,"given":"Shahid","family":"Akbar","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , Chengdu, Sichuan 610054 ,","place":["China"]},{"name":"Department of Computer Science, Abdul Wali Khan University Mardan , Mardan 23200 ,","place":["Pakistan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Raza","sequence":"additional","affiliation":[{"name":"Department of Computer Science, MY University , Islamabad 45750 ,","place":["Pakistan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kashif Ahmad","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Abdul Wali Khan University Mardan , Mardan 23200 ,","place":["Pakistan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-1142","authenticated-orcid":false,"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , Chengdu, Sichuan 610054 ,","place":["China"]},{"name":"Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China , Quzhou, Zhejiang 324003 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,1,23]]},"reference":[{"key":"2025012302124484100_ref1"},{"key":"2025012302124484100_ref2","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1021\/acs.jcim.0c00979","article-title":"DeepCLA: A hybrid deep learning approach for the identification of clathrin","volume":"61","author":"Zhang","year":"2020","journal-title":"J Chem Inf Model"},{"key":"2025012302124484100_ref3","doi-asserted-by":"publisher","first-page":"1378576","DOI":"10.3389\/fnagi.2024.1378576","article-title":"Clathrin mediated endocytosis in Alzheimer\u2019s disease: Cell type specific involvement in amyloid beta pathology","volume":"16","author":"Jaye","year":"2024","journal-title":"Front Aging Neurosci"},{"key":"2025012302124484100_ref4","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.cmpb.2019.05.016","article-title":"Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles","volume":"177","author":"le","year":"2019","journal-title":"Comput Methods Programs Biomed"},{"key":"2025012302124484100_ref5","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.cmpb.2017.08.006","article-title":"An Alzheimers disease related genes identification method based on multiple classifier integration","volume":"150","author":"Miao","year":"2017","journal-title":"Comput Methods Programs Biomed"},{"key":"2025012302124484100_ref6","doi-asserted-by":"publisher","first-page":"2206151","DOI":"10.1002\/advs.202206151","article-title":"Explainable deep hypergraph learning modeling the peptide secondary structure prediction","volume":"10","author":"Jiang","year":"2023","journal-title":"Advanced Science"},{"key":"2025012302124484100_ref7","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/0003-2697(85)90282-9","article-title":"Applicability of agarose gel electrophoresis to the physical characterization of clathrin-coated vesicles","volume":"147","author":"Gottlieb","year":"1985","journal-title":"Anal Biochem"},{"key":"2025012302124484100_ref8","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/S0006-291X(05)81473-1","article-title":"Inositol hexakisphosphate receptor identified as the clathrin assembly protein AP-2","volume":"187","author":"Voglmaier","year":"1992","journal-title":"Biochem Biophys Res Commun"},{"key":"2025012302124484100_ref9","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.bbrc.2005.12.156","article-title":"Recruitment of clathrin onto endosomes by the Tom1\u2013Tollip complex","volume":"341","author":"Katoh","year":"2006","journal-title":"Biochem Biophys Res Commun"},{"key":"2025012302124484100_ref10","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/S0006-291X(89)80170-6","article-title":"Identification of the clathrin-binding domain of assembly protein AP-2","volume":"158","author":"Keen","year":"1989","journal-title":"Biochem Biophys Res Commun"},{"key":"2025012302124484100_ref11","doi-asserted-by":"publisher","first-page":"3017","DOI":"10.1093\/nar\/gkad055","article-title":"DeepBIO: An automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis","volume":"51","author":"Wang","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2025012302124484100_ref12","doi-asserted-by":"publisher","first-page":"6481","DOI":"10.1021\/acs.analchem.1c00354","article-title":"PepFormer: End-to-end transformer-based siamese network to predict and enhance peptide detectability based on sequence only","volume":"93","author":"Cheng","year":"2021","journal-title":"Anal Chem"},{"key":"2025012302124484100_ref13","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/s11432-024-4171-9","article-title":"SBSM-pro: Support bio-sequence machine for proteins","volume":"67","author":"Wang","year":"2024","journal-title":"Science China Information Sciences"},{"key":"2025012302124484100_ref14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/07391102.2024.2329777","article-title":"An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transform","author":"Khalid","year":"2024","journal-title":"Journal of Biomolecular Structure and Dynamics"},{"key":"2025012302124484100_ref15","volume-title":"Nucleic Acids Research"},{"key":"2025012302124484100_ref16","doi-asserted-by":"crossref","first-page":"D523","DOI":"10.1093\/nar\/gkac1052","article-title":"UniProt: The universal protein knowledgebase in 2023","volume":"51","author":"Consortium TU","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2025012302124484100_ref17","doi-asserted-by":"publisher","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":"2025012302124484100_ref18","doi-asserted-by":"publisher","first-page":"W228","DOI":"10.1093\/nar\/gkac278","article-title":"DeepLoc 2.0: Multi-label subcellular localization prediction using protein language models","volume":"50","author":"Thumuluri","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2025012302124484100_ref19","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13142465","article-title":"A first computational frame for recognizing heparin-binding protein","author":"Zhu","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"2025012302124484100_ref20","doi-asserted-by":"publisher","first-page":"e1011214","DOI":"10.1371\/journal.pcbi.1011214","article-title":"BioSeq-Diabolo: Biological sequence similarity analysis using Diabolo","volume":"19","author":"Li","year":"2023","journal-title":"PLoS Comput Biol"},{"key":"2025012302124484100_ref21","doi-asserted-by":"publisher","first-page":"e129","DOI":"10.1093\/nar\/gkab829","article-title":"BioSeq-BLM: A platform for analyzing DNA, RNA, and protein sequences based on biological language models","volume":"49","author":"Li","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2025012302124484100_ref22","doi-asserted-by":"publisher","first-page":"7538","DOI":"10.1038\/s41467-024-51933-2","article-title":"A foundation model identifies broad-Spectrum antimicrobial peptides against drug-resistant bacterial infection","volume":"15","author":"Li","year":"2024","journal-title":"Nat Commun"},{"key":"2025012302124484100_ref23","doi-asserted-by":"publisher","first-page":"2400829","DOI":"10.1002\/advs.202400829","article-title":"HydrogelFinder: A foundation model for efficient self-assembling peptide discovery guided by non-Peptidal small molecules, advanced","volume":"11","author":"Ren","year":"2024","journal-title":"Science"},{"key":"2025012302124484100_ref24","first-page":"17","author":"Xue","year":"2023"},{"key":"2025012302124484100_ref25","doi-asserted-by":"publisher","first-page":"218","DOI":"10.2174\/1573406411666141229162834","article-title":"Impacts of bioinformatics to medicinal chemistry","volume":"11","author":"Chou","year":"2015","journal-title":"Med Chem"},{"key":"2025012302124484100_ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-022-2135-0","article-title":"Unsupervised spectral feature selection algorithms for high dimensional data","volume":"17","author":"Wang","year":"2023","journal-title":"Front Comp Sci"},{"key":"2025012302124484100_ref27","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.compbiolchem.2010.09.002","article-title":"A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction","volume":"34","author":"Sahu","year":"2010","journal-title":"Comput Biol Chem"},{"key":"2025012302124484100_ref28","doi-asserted-by":"publisher","first-page":"1281880","DOI":"10.3389\/fmed.2023.1281880","article-title":"Accurately identifying hemagglutinin using sequence information and machine learning methods","volume":"10","author":"Zou","year":"2023","journal-title":"Front Med (Lausanne)"},{"key":"2025012302124484100_ref29","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1186\/s12864-018-4849-9","article-title":"Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction","volume":"19","author":"Yu","year":"2018","journal-title":"BMC Genomics"},{"key":"2025012302124484100_ref30","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1186\/s12859-021-04251-z","article-title":"Predicting antifreeze proteins with weighted generalized dipeptide composition and multi-regression feature selection ensemble","volume":"22","author":"Wang","year":"2021","journal-title":"BMC Bioinformatics"},{"key":"2025012302124484100_ref31","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1186\/s12859-024-05744-3","article-title":"DPI_CDF: Druggable protein identifier using cascade deep forest","volume":"25","author":"Arif","year":"2024","journal-title":"BMC Bioinformatics"},{"key":"2025012302124484100_ref32","doi-asserted-by":"publisher","first-page":"bbab278","DOI":"10.1093\/bib\/bbab278","article-title":"PScL-HDeep: Image-based prediction of protein subcellular location in human tissue using ensemble learning of handcrafted and deep learned features with two-layer feature selection","volume":"22","author":"Ullah","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025012302124484100_ref33","doi-asserted-by":"publisher","first-page":"3454","DOI":"10.1093\/bioinformatics\/btac353","article-title":"Conditional generative modeling for de novo protein design with hierarchical functions","volume":"38","author":"Kucera","year":"2022","journal-title":"Bioinformatics"},{"key":"2025012302124484100_ref34","volume-title":"Brief Bioinform","author":"Villegas-Morcillo"},{"key":"2025012302124484100_ref35","doi-asserted-by":"publisher","first-page":"1170785","DOI":"10.3389\/fmicb.2023.1170785","article-title":"Computational prediction of promotors in agrobacterium tumefaciens strain C58 by using the machine learning technique","volume":"14","author":"Zulfiqar","year":"2023","journal-title":"Front Microbiol"},{"key":"2025012302124484100_ref36","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad217","article-title":"Improved structure-related prediction for insufficient homologous proteins using MSA enhancement and pre-trained language model","volume":"24","author":"Meng","journal-title":"Brief Bioinform"},{"key":"2025012302124484100_ref37","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1186\/s12915-024-02049-y","article-title":"Bridging chemical structure and conceptual knowledge enables accurate prediction of compound-protein interaction","volume":"22","author":"Tao","year":"2024","journal-title":"BMC Biol"},{"key":"2025012302124484100_ref38","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-022-3579-1","article-title":"Unsupervised feature selection via multiple graph fusion and feature weight learning","volume":"66","author":"Tang","year":"2023","journal-title":"Science China-Information Sciences"},{"key":"2025012302124484100_ref39","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.jtbi.2008.10.007","article-title":"Predicting protein fold pattern with functional domain and sequential evolution information","volume":"256","author":"Shen","year":"2009","journal-title":"J Theor Biol"},{"key":"2025012302124484100_ref40","doi-asserted-by":"publisher","first-page":"965","DOI":"10.3390\/genes10120965","article-title":"SXGBsite: prediction of protein\u2013ligand binding sites using sequence information and extreme gradient boosting","volume":"10","author":"Zhao","year":"2019","journal-title":"Genes"},{"key":"2025012302124484100_ref41","doi-asserted-by":"publisher","first-page":"2441","DOI":"10.1093\/bioinformatics\/bts472","article-title":"Contact map prediction using a large-scale ensemble of rule sets and the fusion of multiple predicted structural features","volume":"28","author":"Bacardit","year":"2012","journal-title":"Bioinformatics"},{"key":"2025012302124484100_ref42","doi-asserted-by":"publisher","first-page":"1657","DOI":"10.1109\/TIP.2010.2044957","article-title":"A completed modeling of local binary pattern operator for texture classification","volume":"19","author":"Guo","year":"2010","journal-title":"IEEE Trans Image Process"},{"key":"2025012302124484100_ref43","doi-asserted-by":"publisher","first-page":"2994","DOI":"10.1093\/nar\/29.14.2994","article-title":"Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements","volume":"29","author":"Sch\u00e4ffer","year":"2001","journal-title":"Nucleic Acids Res"},{"key":"2025012302124484100_ref44","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1093\/bioinformatics\/bty653","article-title":"StackDPPred: A stacking based prediction of DNA-binding protein from sequence","volume":"35","author":"Mishra","year":"2018","journal-title":"Bioinformatics"},{"key":"2025012302124484100_ref45","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/j.jtbi.2015.06.012","article-title":"Improved prediction of accessible surface area results in efficient energy function application","volume":"380","author":"Iqbal","year":"2015","journal-title":"J Theor Biol"},{"key":"2025012302124484100_ref46","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1002\/jcc.24298","article-title":"sDFIRE: Sequence-specific statistical energy function for protein structure prediction by decoy selections","volume":"37","author":"Hoque","year":"2016","journal-title":"J Comput Chem"},{"key":"2025012302124484100_ref47","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1016\/j.jmb.2005.01.071","article-title":"The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins","volume":"347","author":"Doszt\u00e1nyi","year":"2005","journal-title":"J Mol Biol"},{"key":"2025012302124484100_ref48","doi-asserted-by":"publisher","first-page":"e0181426","DOI":"10.1371\/journal.pone.0181426","article-title":"Multi-scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree","volume":"12","author":"Zhou","year":"2017","journal-title":"PloS One"},{"key":"2025012302124484100_ref49","doi-asserted-by":"publisher","DOI":"10.1093\/bioadv\/vbad001","article-title":"Applications of transformer-based language models in bioinformatics: A survey, bioinformatics","volume":"3","author":"Zhang","year":"2023","journal-title":"Advances"},{"key":"2025012302124484100_ref50","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1093\/glycob\/cwad033","article-title":"LMNglyPred: Prediction of human N-linked glycosylation sites using embeddings from a pre-trained protein language model","volume":"33","author":"Pakhrin","year":"2023","journal-title":"Glycobiology"},{"key":"2025012302124484100_ref51","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btae643","article-title":"Prediction of human O-linked glycosylation sites using stacked generalization and embeddings from pre-trained protein language model","volume":"40","author":"Pakhrin","year":"2024","journal-title":"Bioinformatics"},{"key":"2025012302124484100_ref52","doi-asserted-by":"publisher","DOI":"10.1093\/nargab\/lqae011","article-title":"SumoPred-PLM: Human SUMOylation and SUMO2\/3 sites prediction using pre-trained protein language model","volume":"6","author":"Palacios","year":"2024","journal-title":"NAR Genomics and Bioinformatics"},{"key":"2025012302124484100_ref53","doi-asserted-by":"publisher","first-page":"2548","DOI":"10.1021\/acs.jproteome.2c00667","article-title":"LMPhosSite: A deep learning-based approach for general protein phosphorylation site prediction using Embeddings from the local window sequence and Pretrained protein language model","volume":"22","author":"Pakhrin","year":"2023","journal-title":"J Proteome Res"},{"key":"2025012302124484100_ref54","doi-asserted-by":"publisher","first-page":"e2016239118","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume":"118","author":"Rives","year":"2021","journal-title":"Proc Natl Acad Sci"},{"key":"2025012302124484100_ref55","first-page":"29287","article-title":"Language models enable zero-shot prediction of the effects of mutations on protein function","volume":"34","author":"Meier","year":"2021","journal-title":"Advances in neural information processing systems"},{"key":"2025012302124484100_ref56","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018","journal-title":"arXiv"},{"key":"2025012302124484100_ref57","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.csbj.2023.11.037","article-title":"TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding","volume":"23","author":"Yadav","year":"2024","journal-title":"Comput Struct Biotechnol J"},{"key":"2025012302124484100_ref58","doi-asserted-by":"publisher","first-page":"115550","DOI":"10.1016\/j.ab.2024.115550","article-title":"Improving protein-protein interaction prediction using protein language model and protein network features","volume":"693","author":"Hu","year":"2024","journal-title":"Anal Biochem"},{"key":"2025012302124484100_ref59","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1093\/bioinformatics\/btu739","article-title":"UniRef clusters: A comprehensive and scalable alternative for improving sequence similarity searches","volume":"31","author":"Suzek","year":"2014","journal-title":"Bioinformatics"},{"key":"2025012302124484100_ref60","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1186\/s12915-023-01796-8","article-title":"Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance","volume":"21","author":"Zhu","year":"2023","journal-title":"BMC Biol"},{"key":"2025012302124484100_ref61","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1186\/s12915-024-01968-0","article-title":"Identification of microbe\u2013disease signed associations via multi-scale variational graph autoencoder based on signed message propagation","volume":"22","author":"Zhu","year":"2024","journal-title":"BMC Biol"},{"key":"2025012302124484100_ref62","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution\u2014a simple and efficient heuristic for global optimization over continuous spaces","volume":"11","author":"Storn","year":"1997","journal-title":"Journal of Global Optimization"},{"key":"2025012302124484100_ref63","doi-asserted-by":"publisher","first-page":"2730","DOI":"10.1109\/TCYB.2017.2710626","article-title":"Abstract convex underestimation assisted multistage differential evolution","volume":"47","author":"Zhou","year":"2017","journal-title":"IEEE Transactions on Cybernetics"},{"key":"2025012302124484100_ref64","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1109\/TCBB.2019.2893634","article-title":"TargetDBP: Accurate DNA-binding protein prediction via sequence-based multi-view feature learning","volume":"17","author":"Hu","year":"2020","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2025012302124484100_ref65","doi-asserted-by":"publisher","first-page":"1676","DOI":"10.1109\/TVT.2015.2409319","article-title":"A modified differential evolution with heuristic algorithm for nonconvex optimization on sensor network localization","volume":"65","author":"Qiao","year":"2016","journal-title":"IEEE Trans Veh Technol"},{"key":"2025012302124484100_ref66","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1021\/acs.jcim.0c00735","article-title":"TargetDBP+: Enhancing the performance of identifying DNA-binding proteins via weighted convolutional features","volume":"61","author":"Hu","year":"2021","journal-title":"J Chem Inf Model"},{"key":"2025012302124484100_ref67","doi-asserted-by":"publisher","first-page":"65","DOI":"10.3390\/machines6040065","article-title":"Feature selection based on binary tree growth algorithm for the classification of myoelectric signals","volume":"6","author":"Too","year":"2018","journal-title":"Machines"},{"key":"2025012302124484100_ref68","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.engappai.2018.04.021","article-title":"Tree growth algorithm (TGA): A novel approach for solving optimization problems","volume":"72","author":"Cheraghalipour","year":"2018","journal-title":"Eng Appl Artif Intel"},{"key":"2025012302124484100_ref69","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00861","article-title":"Umap: Uniform manifold approximation and projection for dimension reduction","author":"McInnes","year":"2018","journal-title":"arXiv"},{"key":"2025012302124484100_ref70","first-page":"4768","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Lundberg","year":"2017"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/1\/bbaf026\/61562394\/bbaf026.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/1\/bbaf026\/61562394\/bbaf026.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T02:13:04Z","timestamp":1737598384000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf026\/7972809"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,22]]},"references-count":70,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,11,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf026","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,1]]},"published":{"date-parts":[[2024,11,22]]},"article-number":"bbaf026"}}