{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:24:28Z","timestamp":1772040268560,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T00:00:00Z","timestamp":1712275200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T00:00:00Z","timestamp":1712275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Drug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor\u2019s performance is still not satisfactory.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>In this study, we developed a novel deep learning-based model DPI_CDF for predicting DPs based on protein sequence only. DPI_CDF utilizes evolutionary-based (i.e., histograms of oriented gradients for position-specific scoring matrix), physiochemical-based (i.e., component protein sequence representation), and compositional-based (i.e., normalized qualitative characteristic) properties of protein sequence to generate features. Then a hierarchical deep forest model fuses these three encoding schemes to build the proposed model DPI_CDF.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The empirical outcomes on 10-fold cross-validation demonstrate that the proposed model achieved 99.13 % accuracy and 0.982 of Matthew\u2019s-correlation-coefficient (MCC) on the training dataset. The generalization power of the trained model is further examined on an independent dataset and achieved 95.01% of maximum accuracy and 0.900 MCC. When compared to current state-of-the-art methods, DPI_CDF improves in terms of accuracy by 4.27% and 4.31% on training and testing datasets, respectively. We believe, DPI_CDF will support the research community to identify druggable proteins and escalate the drug discovery process.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Availability<\/jats:title>\n                <jats:p>The benchmark datasets and source codes are available in GitHub: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/github.com\/Muhammad-Arif-NUST\/DPI_CDF\">http:\/\/github.com\/Muhammad-Arif-NUST\/DPI_CDF<\/jats:ext-link>.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-024-05744-3","type":"journal-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T12:02:15Z","timestamp":1712318535000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["DPI_CDF: druggable protein identifier using cascade deep forest"],"prefix":"10.1186","volume":"25","author":[{"given":"Muhammad","family":"Arif","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ge","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Ghulam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saleh","family":"Musleh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tanvir","family":"Alam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,5]]},"reference":[{"issue":"9","key":"5744_CR1","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1038\/nrd892","volume":"1","author":"AL Hopkins","year":"2002","unstructured":"Hopkins AL, Groom CR. The druggable genome. Nat Rev Drug Discov. 2002;1(9):727\u201330.","journal-title":"Nat Rev Drug Discov"},{"key":"5744_CR2","doi-asserted-by":"publisher","first-page":"366","DOI":"10.3389\/fphys.2015.00366","volume":"6","author":"G Kandoi","year":"2015","unstructured":"Kandoi G, Acencio ML, Lemke N. Prediction of druggable proteins using machine learning and systems biology: a mini-review. Front Physiol. 2015;6:366.","journal-title":"Front Physiol"},{"issue":"1","key":"5744_CR3","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1038\/nrd.2016.230","volume":"16","author":"R Santos","year":"2017","unstructured":"Santos R, Ursu O, Gaulton A, Bento AP, Donadi RS, Bologa CG, Karlsson A, Al-Lazikani B, Hersey A, Oprea TI, et al. A comprehensive map of molecular drug targets. Nat Rev Drug Discov. 2017;16(1):19\u201334.","journal-title":"Nat Rev Drug Discov"},{"issue":"1","key":"5744_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.1472-8206.2007.00548.x","volume":"22","author":"Y Landry","year":"2008","unstructured":"Landry Y, Gies J-P. Drugs and their molecular targets: an updated overview. Fundam Clin Pharmacol. 2008;22(1):1\u201318.","journal-title":"Fundam Clin Pharmacol"},{"key":"5744_CR5","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.artmed.2019.07.005","volume":"98","author":"J Lin","year":"2019","unstructured":"Lin J, Chen H, Li S, Liu Y, Li X, Yu B. Accurate prediction of potential druggable proteins based on genetic algorithm and bagging-SVM ensemble classifier. Artif Intell Med. 2019;98:35\u201347.","journal-title":"Artif Intell Med"},{"issue":"1","key":"5744_CR6","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1111\/cbdd.12066","volume":"81","author":"LN Makley","year":"2013","unstructured":"Makley LN, Gestwicki JE. Expanding the number of \u2018druggable\u2019 targets: non-enzymes and protein\u2013protein interactions. Chem Biol Drug Des. 2013;81(1):22\u201332.","journal-title":"Chem Biol Drug Des"},{"key":"5744_CR7","doi-asserted-by":"crossref","unstructured":"Lavigne R, Ceyssens P-J, Robben J. Phage proteomics: applications of mass spectrometry. Bacteriophages: Methods and Protocols, Volume 2 Molecular and Applied Aspects, 2009:239\u2013251","DOI":"10.1007\/978-1-60327-565-1_14"},{"key":"5744_CR8","doi-asserted-by":"crossref","unstructured":"Ilari A, Savino C. Protein structure determination by x-ray crystallography. Bioinformatics: Data, Sequence Analysis and Evolution, 2008:63\u201387","DOI":"10.1007\/978-1-60327-159-2_3"},{"issue":"8","key":"5744_CR9","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1016\/j.tips.2019.06.004","volume":"40","author":"HS Chan","year":"2019","unstructured":"Chan HS, Shan H, Dahoun T, Vogel H, Yuan S. Advancing drug discovery via artificial intelligence. Trends Pharmacol Sci. 2019;40(8):592\u2013604.","journal-title":"Trends Pharmacol Sci"},{"issue":"12","key":"5744_CR10","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1038\/nrd2961","volume":"8","author":"B Munos","year":"2009","unstructured":"Munos B. Lessons from 60 years of pharmaceutical innovation. Nat Rev Drug Discov. 2009;8(12):959\u201368.","journal-title":"Nat Rev Drug Discov"},{"issue":"3","key":"5744_CR11","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/nrd3078","volume":"9","author":"SM Paul","year":"2010","unstructured":"Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL. How to improve r &d productivity: the pharmaceutical industry\u2019s grand challenge. Nat Rev Drug Discovery. 2010;9(3):203\u201314.","journal-title":"Nat Rev Drug Discovery"},{"issue":"5","key":"5744_CR12","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1016\/j.drudis.2016.01.007","volume":"21","author":"AA Jamali","year":"2016","unstructured":"Jamali AA, Ferdousi R, Razzaghi S, Li J, Safdari R, Ebrahimie E. Drugminer: comparative analysis of machine learning algorithms for prediction of potential druggable proteins. Drug Discovery Today. 2016;21(5):718\u201324.","journal-title":"Drug Discovery Today"},{"key":"5744_CR13","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1007\/s40484-018-0157-2","volume":"6","author":"T Sun","year":"2018","unstructured":"Sun T, Lai L, Pei J. Analysis of protein features and machine learning algorithms for prediction of druggable proteins. Quant Biol. 2018;6:334\u201343.","journal-title":"Quant Biol"},{"key":"5744_CR14","first-page":"3467","volume":"1","author":"Y Gong","year":"2021","unstructured":"Gong Y, Liao B, Wang P, Zou Q. Drughybrid_bs: using hybrid feature combined with bagging-SVM to predict potentially druggable proteins. Front Pharmacol. 2021;1:3467.","journal-title":"Front Pharmacol"},{"key":"5744_CR15","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.jare.2022.01.009","volume":"41","author":"L Yu","year":"2022","unstructured":"Yu L, Xue L, Liu F, Li Y, Jing R, Luo J. The applications of deep learning algorithms on in silico druggable proteins identification. J Adv Res. 2022;41:219\u201331.","journal-title":"J Adv Res"},{"issue":"1","key":"5744_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-09484-3","volume":"12","author":"R Sikander","year":"2022","unstructured":"Sikander R, Ghulam A, Ali F. Xgb-drugpred: computational prediction of druggable proteins using extreme gradient boosting and optimized features set. Sci Rep. 2022;12(1):1\u20139.","journal-title":"Sci Rep"},{"key":"5744_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106276","volume":"151","author":"MS Iraji","year":"2022","unstructured":"Iraji MS, Tanha J, Habibinejad M. Druggable protein prediction using a multi-canal deep convolutional neural network based on autocovariance method. Comput Biol Med. 2022;151: 106276.","journal-title":"Comput Biol Med"},{"issue":"9","key":"5744_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2022.104883","volume":"25","author":"P Charoenkwan","year":"2022","unstructured":"Charoenkwan P, Schaduangrat N, Moni MA, Shoombuatong W, Manavalan B, et al. Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework. Iscience. 2022;25(9): 104883.","journal-title":"Iscience"},{"key":"5744_CR19","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.jtbi.2018.01.008","volume":"442","author":"M Arif","year":"2018","unstructured":"Arif M, Hayat M, Jan Z. imem-2lsaac: a two-level model for discrimination of membrane proteins and their types by extending the notion of saac into chou\u2019s pseudo amino acid composition. J Theor Biol. 2018;442:11\u201321.","journal-title":"J Theor Biol"},{"key":"5744_CR20","doi-asserted-by":"publisher","first-page":"6400","DOI":"10.1016\/j.csbj.2021.11.024","volume":"19","author":"F Ge","year":"2021","unstructured":"Ge F, Zhu Y-H, Xu J, Muhammad A, Song J, Yu D-J. Muttmpredictor: Robust and accurate cascade xgboost classifier for prediction of mutations in transmembrane proteins. Comput Struct Biotechnol J. 2021;19:6400\u201316.","journal-title":"Comput Struct Biotechnol J"},{"issue":"1","key":"5744_CR21","doi-asserted-by":"publisher","first-page":"38","DOI":"10.2174\/1386207323666201204140438","volume":"25","author":"F Ge","year":"2022","unstructured":"Ge F, Hu J, Zhu Y-H, Arif M, Yu D-J. Targetmm: Accurate missense mutation prediction by utilizing local and global sequence information with classifier ensemble. Combin Chem High Throughput Screen. 2022;25(1):38\u201352.","journal-title":"Combin Chem High Throughput Screen"},{"issue":"3","key":"5744_CR22","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.jtbi.2008.10.007","volume":"256","author":"H-B Shen","year":"2009","unstructured":"Shen H-B, Chou K-C. Predicting protein fold pattern with functional domain and sequential evolution information. J Theor Biol. 2009;256(3):441\u20136.","journal-title":"J Theor Biol"},{"key":"5744_CR23","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1021\/acs.jcim.2c01417","volume":"63","author":"A Khan","year":"2023","unstructured":"Khan A, Uddin J, Ali F, Kumar H, Alghamdi W, Ahmad A. Afp-spts: an accurate prediction of antifreeze proteins using sequential and pseudo-tri-slicing evolutionary features with an extremely randomized tree. J Chem Inf Model. 2023;63:826.","journal-title":"J Chem Inf Model"},{"issue":"6","key":"5744_CR24","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1109\/TCBB.2016.2616469","volume":"14","author":"J Hu","year":"2016","unstructured":"Hu J, Li Y, Zhang M, Yang X, Shen H-B, Yu D-J. Predicting protein-DNA binding residues by weightedly combining sequence-based features and boosting multiple SVMs. IEEE\/ACM Trans Comput Biol Bioinf. 2016;14(6):1389\u201398.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"14","key":"5744_CR25","doi-asserted-by":"publisher","first-page":"2994","DOI":"10.1093\/nar\/29.14.2994","volume":"29","author":"AA Sch\u00e4ffer","year":"2001","unstructured":"Sch\u00e4ffer AA, Aravind L, Madden TL, Shavirin S, Spouge JL, Wolf YI, Koonin EV, Altschul SF. Improving the accuracy of psi-blast protein database searches with composition-based statistics and other refinements. Nucl Acids Res. 2001;29(14):2994\u20133005.","journal-title":"Nucl Acids Res"},{"issue":"1","key":"5744_CR26","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1093\/nar\/28.1.45","volume":"28","author":"A Bairoch","year":"2000","unstructured":"Bairoch A, Apweiler R. The swiss-prot protein sequence database and its supplement trembl in 2000. Nucl Acids Res. 2000;28(1):45\u20138.","journal-title":"Nucl Acids Res"},{"key":"5744_CR27","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), 2005;1:886\u2013893. IEEE","DOI":"10.1109\/CVPR.2005.177"},{"key":"5744_CR28","doi-asserted-by":"crossref","unstructured":"Junior OL, Delgado D, Gon\u00e7alves V, Nunes U. Trainable classifier-fusion schemes: an application to pedestrian detection. In: 2009 12Th International IEEE Conference on Intelligent Transportation Systems, 2009:1\u20136. IEEE","DOI":"10.1109\/ITSC.2009.5309700"},{"issue":"4","key":"5744_CR29","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1109\/34.917571","volume":"23","author":"A Mohan","year":"2001","unstructured":"Mohan A, Papageorgiou C, Poggio T. Example-based object detection in images by components. IEEE Trans Pattern Anal Mach Intell. 2001;23(4):349\u201361.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5744_CR30","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s11263-005-6644-8","volume":"63","author":"P Viola","year":"2005","unstructured":"Viola P, Jones MJ, Snow D. Detecting pedestrians using patterns of motion and appearance. Int J Comput Vision. 2005;63:153\u201361.","journal-title":"Int J Comput Vision"},{"issue":"19","key":"5744_CR31","doi-asserted-by":"publisher","first-page":"8700","DOI":"10.1073\/pnas.92.19.8700","volume":"92","author":"I Dubchak","year":"1995","unstructured":"Dubchak I, Muchnik I, Holbrook SR, Kim S-H. Prediction of protein folding class using global description of amino acid sequence. Proc Natl Acad Sci. 1995;92(19):8700\u20134.","journal-title":"Proc Natl Acad Sci"},{"issue":"8","key":"5744_CR32","doi-asserted-by":"publisher","first-page":"0181426","DOI":"10.1371\/journal.pone.0181426","volume":"12","author":"C Zhou","year":"2017","unstructured":"Zhou C, Yu H, Ding Y, Guo F, Gong X-J. Multi-scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree. PLoS ONE. 2017;12(8):0181426.","journal-title":"PLoS ONE"},{"issue":"6","key":"5744_CR33","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1093\/bioinformatics\/btw730","volume":"33","author":"X Zhang","year":"2017","unstructured":"Zhang X, Liu S. Rbppred: predicting RNA-binding proteins from sequence using SVM. Bioinformatics. 2017;33(6):854\u201362.","journal-title":"Bioinformatics"},{"key":"5744_CR34","doi-asserted-by":"crossref","unstructured":"Golmohammadi SK, Kurgan L, Crowley B, Reformat M. Classification of cell membrane proteins. In: 2007 Frontiers in the Convergence of Bioscience and Information Technologies, 2007: 153\u2013158. IEEE","DOI":"10.1109\/FBIT.2007.21"},{"key":"5744_CR35","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1007\/PL00006412","volume":"47","author":"X Xia","year":"1998","unstructured":"Xia X, Li W-H. What amino acid properties affect protein evolution? J Mol Evol. 1998;47:557\u201364.","journal-title":"J Mol Evol"},{"issue":"5","key":"5744_CR36","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.ygeno.2017.10.008","volume":"110","author":"W-R Qiu","year":"2018","unstructured":"Qiu W-R, Sun B-Q, Xiao X, Xu Z-C, Jia J-H, Chou K-C. ikcr-pseens: identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier. Genomics. 2018;110(5):239\u201346.","journal-title":"Genomics"},{"issue":"1","key":"5744_CR37","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.jtbi.2010.11.017","volume":"271","author":"M Hayat","year":"2011","unstructured":"Hayat M, Khan A. Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition. J Theor Biol. 2011;271(1):10\u20137.","journal-title":"J Theor Biol"},{"key":"5744_CR38","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.chemolab.2018.09.007","volume":"182","author":"M Kabir","year":"2018","unstructured":"Kabir M, Arif M, Ahmad S, Ali Z, Swati ZNK, Yu D-J. Intelligent computational method for discrimination of anticancer peptides by incorporating sequential and evolutionary profiles information. Chemom Intell Lab Syst. 2018;182:158\u201365.","journal-title":"Chemom Intell Lab Syst"},{"key":"5744_CR39","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1007\/s10822-020-00307-z","volume":"34","author":"M Arif","year":"2020","unstructured":"Arif M, Ahmad S, Ali F, Fang G, Li M, Yu D-J. Targetcpp: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree. J Comput Aided Mol Des. 2020;34:841\u201356.","journal-title":"J Comput Aided Mol Des"},{"key":"5744_CR40","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.jtbi.2013.12.015","volume":"346","author":"M Hayat","year":"2014","unstructured":"Hayat M, Tahir M, Khan SA. Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces. J Theor Biol. 2014;346:8\u201315.","journal-title":"J Theor Biol"},{"key":"5744_CR41","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.jtbi.2011.09.026","volume":"292","author":"M Hayat","year":"2012","unstructured":"Hayat M, Khan A. Memhyb: predicting membrane protein types by hybridizing SAAC and PSSM. J Theor Biol. 2012;292:93\u2013102.","journal-title":"J Theor Biol"},{"key":"5744_CR42","doi-asserted-by":"crossref","unstructured":"Zhou Z-H, Feng J. Deep forest: Towards an alternative to deep neural networks. In: IJCAI, 2017:3553\u20133559","DOI":"10.24963\/ijcai.2017\/497"},{"issue":"5","key":"5744_CR43","doi-asserted-by":"publisher","first-page":"2749","DOI":"10.1109\/TCBB.2021.3102133","volume":"19","author":"M Arif","year":"2021","unstructured":"Arif M, Kabir M, Ahmed S, Khan A, Ge F, Khelifi A, Yu D-J. Deepcppred: a deep learning framework for the discrimination of cell-penetrating peptides and their uptake efficiencies. IEEE\/ACM Trans Comput Biol Bioinf. 2021;19(5):2749\u201359.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"5744_CR44","unstructured":"Cai R, Chen C. Learning deep forest with multi-scale local binary pattern features for face anti-spoofing (2019). arXiv preprint arXiv:1910.03850"},{"issue":"21","key":"5744_CR45","first-page":"8018","volume":"2019","author":"Y Wang","year":"2019","unstructured":"Wang Y, Bi X, Chen W, Li Y, Chen Q, Long T. Deep forest for radar HRRP recognition. J Eng. 2019;2019(21):8018\u201321.","journal-title":"J Eng"},{"key":"5744_CR46","doi-asserted-by":"publisher","first-page":"90","DOI":"10.3389\/fgene.2019.00090","volume":"10","author":"Z-H Chen","year":"2019","unstructured":"Chen Z-H, Li L-P, He Z, Zhou J-R, Li Y, Wong L. An improved deep forest model for predicting self-interacting proteins from protein sequence using wavelet transformation. Front Genet. 2019;10:90.","journal-title":"Front Genet"},{"key":"5744_CR47","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.knosys.2019.02.022","volume":"173","author":"LV Utkin","year":"2019","unstructured":"Utkin LV, Kovalev MS, Meldo AA. A deep forest classifier with weights of class probability distribution subsets. Knowl-Based Syst. 2019;173:15\u201327.","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"5744_CR48","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1093\/nsr\/nwy108","volume":"6","author":"Z-H Zhou","year":"2019","unstructured":"Zhou Z-H, Feng J. Deep forest. Natl Sci Rev. 2019;6(1):74\u201386.","journal-title":"Natl Sci Rev"},{"key":"5744_CR49","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45:5\u201332.","journal-title":"Mach Learn"},{"key":"5744_CR50","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 2016:785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"5744_CR51","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts P, Ernst D, Wehenkel L. Extremely randomized trees. Mach Learn. 2006;63:3\u201342.","journal-title":"Mach Learn"},{"issue":"1","key":"5744_CR52","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","volume":"143","author":"JA Hanley","year":"1982","unstructured":"Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology. 1982;143(1):29\u201336.","journal-title":"Radiology"},{"key":"5744_CR53","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.jpdc.2017.08.009","volume":"117","author":"L Wei","year":"2018","unstructured":"Wei L, Ding Y, Su R, Tang J, Zou Q. Prediction of human protein subcellular localization using deep learning. J Parall Distrib Comput. 2018;117:212\u20137.","journal-title":"J Parall Distrib Comput"},{"key":"5744_CR54","doi-asserted-by":"crossref","unstructured":"Ge R, Xia Y, Jiang M, Jia G, Jing X, Li Y, Cai Y. Hybavpnet: a novel hybrid network architecture for antiviral peptides identification. bioRxiv, 2022:2022\u201306","DOI":"10.1101\/2022.06.10.495721"},{"issue":"6","key":"5744_CR55","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1093\/bib\/bbab245","volume":"22","author":"F Li","year":"2021","unstructured":"Li F, Guo X, Jin P, Chen J, Xiang D, Song J, Coin LJ. Porpoise: a new approach for accurate prediction of RNA pseudouridine sites. Brief Bioinform. 2021;22(6):245.","journal-title":"Brief Bioinform"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-024-05744-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-024-05744-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-024-05744-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T14:03:40Z","timestamp":1724076220000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-024-05744-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,5]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["5744"],"URL":"https:\/\/doi.org\/10.1186\/s12859-024-05744-3","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,5]]},"assertion":[{"value":"1 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"145"}}