{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T16:39:37Z","timestamp":1782751177377,"version":"3.54.5"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:00:00Z","timestamp":1742601600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:00:00Z","timestamp":1742601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"DOI":"10.1186\/s12859-025-06101-8","type":"journal-article","created":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T17:28:15Z","timestamp":1742664495000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Deep-ProBind: binding protein prediction with transformer-based deep learning model"],"prefix":"10.1186","volume":"26","author":[{"given":"Salman","family":"Khan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumaiya","family":"Noor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid Hussain","family":"Awan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shehryar","family":"Iqbal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salman A.","family":"AlQahtani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naqqash","family":"Dilshad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nijad","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,22]]},"reference":[{"issue":"3","key":"6101_CR1","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1021\/acs.jcim.3c01586","volume":"64","author":"E GoulardCoderc de Lacam","year":"2024","unstructured":"GoulardCoderc de Lacam E, Roux B, Chipot C. Classifying protein-protein binding affinity with free-energy calculations and machine learning approaches. J Chem Inf Model. 2024;64(3):1081\u201391. https:\/\/doi.org\/10.1021\/acs.jcim.3c01586.","journal-title":"J Chem Inf Model"},{"issue":"6","key":"6101_CR2","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/s40495-023-00342-y","volume":"9","author":"NSMH Mohaideen","year":"2023","unstructured":"Mohaideen NSMH, Vaani S, Hemalatha S. Antimicrobial peptides. Curr Pharmacol Reports. 2023;9(6):433\u201354. https:\/\/doi.org\/10.1007\/s40495-023-00342-y.","journal-title":"Curr Pharmacol Reports"},{"issue":"1","key":"6101_CR3","doi-asserted-by":"publisher","first-page":"5253","DOI":"10.1038\/s41467-018-07717-6","volume":"9","author":"L Tallorin","year":"2018","unstructured":"Tallorin L, et al. Discovering de novo peptide substrates for enzymes using machine learning. Nat Commun. 2018;9(1):5253. https:\/\/doi.org\/10.1038\/s41467-018-07717-6.","journal-title":"Nat Commun"},{"issue":"4","key":"6101_CR4","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1007\/s00232-015-9787-8","volume":"248","author":"X Xiao","year":"2015","unstructured":"Xiao X, Zou HL, Lin WZ. iMem-Seq: a multi-label learning classifier for predicting membrane proteins types. J Membr Biol. 2015;248(4):745\u201352. https:\/\/doi.org\/10.1007\/s00232-015-9787-8.","journal-title":"J Membr Biol"},{"key":"6101_CR5","doi-asserted-by":"publisher","first-page":"155040","DOI":"10.1109\/ACCESS.2024.3481244","volume":"12","author":"N Bibi","year":"2024","unstructured":"Bibi N, et al. Sequence-based intelligent model for identification of tumor T cell antigens using fusion features. IEEE Access. 2024;12:155040\u201351. https:\/\/doi.org\/10.1109\/ACCESS.2024.3481244.","journal-title":"IEEE Access"},{"key":"6101_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1042\/bse0590001","volume":"59","author":"PK Robinson","year":"2015","unstructured":"Robinson PK. Enzymes: principles and biotechnological applications. Essays Biochem. 2015;59:1\u201341. https:\/\/doi.org\/10.1042\/bse0590001.","journal-title":"Essays Biochem"},{"issue":"52","key":"6101_CR7","doi-asserted-by":"publisher","first-page":"e202309305","DOI":"10.1002\/anie.202309305","volume":"62","author":"E Radley","year":"2023","unstructured":"Radley E, Davidson J, Foster J, Obexer R, Bell EL, Green AP. Engineering enzymes for environmental sustainability. Angew Chemie Int Ed. 2023;62(52):e202309305. https:\/\/doi.org\/10.1002\/anie.202309305.","journal-title":"Angew Chemie Int Ed"},{"issue":"6","key":"6101_CR8","doi-asserted-by":"publisher","first-page":"1396","DOI":"10.3390\/cancers12061396","volume":"12","author":"DG Efremov","year":"2020","unstructured":"Efremov DG, Turkalj S, Laurenti L. Mechanisms of B cell receptor activation and responses to b cell receptor inhibitors in B cell malignancies. Cancers (Basel). 2020;12(6):1396. https:\/\/doi.org\/10.3390\/cancers12061396.","journal-title":"Cancers (Basel)"},{"issue":"1","key":"6101_CR9","doi-asserted-by":"publisher","first-page":"9116","DOI":"10.1038\/s41598-024-59777-y","volume":"14","author":"S Khan","year":"2024","unstructured":"Khan S, et al. Sequence based model using deep neural network and hybrid features for identification of 5-hydroxymethylcytosine modification. Sci Rep. 2024;14(1):9116. https:\/\/doi.org\/10.1038\/s41598-024-59777-y.","journal-title":"Sci Rep"},{"issue":"1","key":"6101_CR10","doi-asserted-by":"publisher","first-page":"20819","DOI":"10.1038\/s41598-024-71568-z","volume":"14","author":"I Uddin","year":"2024","unstructured":"Uddin I, et al. A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications. Sci Rep. 2024;14(1):20819. https:\/\/doi.org\/10.1038\/s41598-024-71568-z.","journal-title":"Sci Rep"},{"key":"6101_CR11","doi-asserted-by":"publisher","first-page":"1052","DOI":"10.3389\/fgene.2020.539227","volume":"11","author":"F Khan","year":"2020","unstructured":"Khan F, et al. Prediction of recombination spots using novel hybrid feature extraction method via deep learning approach. Front Genet. 2020;11:1052. https:\/\/doi.org\/10.3389\/fgene.2020.539227.","journal-title":"Front Genet"},{"key":"6101_CR12","doi-asserted-by":"publisher","first-page":"40783","DOI":"10.1109\/ACCESS.2021.3062291","volume":"9","author":"N Inayat","year":"2021","unstructured":"Inayat N, et al. iEnhancer-DHF: identification of enhancers and their strengths using optimize deep neural network with multiple features extraction methods. IEEE Access. 2021;9:40783\u201396. https:\/\/doi.org\/10.1109\/ACCESS.2021.3062291.","journal-title":"IEEE Access"},{"issue":"1","key":"6101_CR13","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/s13321-023-00795-9","volume":"16","author":"DD Wang","year":"2024","unstructured":"Wang DD, Wu W, Wang R. Structure-based, deep-learning models for protein-ligand binding affinity prediction. J Cheminform. 2024;16(1):2. https:\/\/doi.org\/10.1186\/s13321-023-00795-9.","journal-title":"J Cheminform"},{"issue":"1","key":"6101_CR14","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s13321-022-00584-w","volume":"14","author":"I Lee","year":"2022","unstructured":"Lee I, Nam H. Sequence-based prediction of protein binding regions and drug\u2013target interactions. J Cheminform. 2022;14(1):5. https:\/\/doi.org\/10.1186\/s13321-022-00584-w.","journal-title":"J Cheminform"},{"issue":"1","key":"6101_CR15","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1093\/bioinformatics\/btab643","volume":"38","author":"Q Yuan","year":"2021","unstructured":"Yuan Q, Chen J, Zhao H, Zhou Y, Yang Y. Structure-aware protein\u2013protein interaction site prediction using deep graph convolutional network. Bioinformatics. 2021;38(1):125\u201332. https:\/\/doi.org\/10.1093\/bioinformatics\/btab643.","journal-title":"Bioinformatics"},{"issue":"9","key":"6101_CR16","doi-asserted-by":"publisher","first-page":"e51","DOI":"10.1093\/nar\/gkab044","volume":"49","author":"Y Xia","year":"2021","unstructured":"Xia Y, Xia C-Q, Pan X, Shen H-B. GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues. Nucleic Acids Res. 2021;49(9):e51\u2013e51. https:\/\/doi.org\/10.1093\/nar\/gkab044.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"6101_CR17","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1038\/s42003-022-03445-2","volume":"5","author":"O Abdin","year":"2022","unstructured":"Abdin O, Nim S, Wen H, Kim PM. PepNN: a deep attention model for the identification of peptide binding sites. Commun Biol. 2022;5(1):503. https:\/\/doi.org\/10.1038\/s42003-022-03445-2.","journal-title":"Commun Biol"},{"issue":"2","key":"6101_CR18","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1038\/s41592-019-0666-6","volume":"17","author":"P Gainza","year":"2020","unstructured":"Gainza P, et al. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nat Methods. 2020;17(2):184\u201392. https:\/\/doi.org\/10.1038\/s41592-019-0666-6.","journal-title":"Nat Methods"},{"issue":"11","key":"6101_CR19","doi-asserted-by":"publisher","first-page":"2835","DOI":"10.1093\/molbev\/msy166","volume":"35","author":"QK Langdon","year":"2018","unstructured":"Langdon QK, Peris D, Kyle B, Hittinger CT. sppIDer: a species identification tool to investigate hybrid genomes with high-throughput sequencing. Mol Biol Evol. 2018;35(11):2835\u201349. https:\/\/doi.org\/10.1093\/molbev\/msy166.","journal-title":"Mol Biol Evol"},{"issue":"7","key":"6101_CR20","doi-asserted-by":"publisher","first-page":"2428","DOI":"10.1016\/j.jmb.2020.02.026","volume":"432","author":"J Qiu","year":"2020","unstructured":"Qiu J, et al. ProNA2020 predicts protein\u2013DNA, protein\u2013RNA, and protein\u2013protein binding proteins and residues from sequence. J Mol Biol. 2020;432(7):2428\u201343. https:\/\/doi.org\/10.1016\/j.jmb.2020.02.026.","journal-title":"J Mol Biol"},{"key":"6101_CR21","doi-asserted-by":"publisher","first-page":"115637","DOI":"10.1016\/j.ab.2024.115637","volume":"694","author":"J Hu","year":"2024","unstructured":"Hu J, et al. Protein-peptide binding residue prediction based on protein language models and cross-attention mechanism. Anal Biochem. 2024;694:115637. https:\/\/doi.org\/10.1016\/j.ab.2024.115637.","journal-title":"Anal Biochem"},{"issue":"8","key":"6101_CR22","doi-asserted-by":"publisher","first-page":"1829","DOI":"10.1021\/acs.jproteome.2c00020","volume":"21","author":"S Romero-Molina","year":"2022","unstructured":"Romero-Molina S, et al. PPI-Affinity: a web tool for the prediction and optimization of protein-peptide and protein-protein binding affinity. J Proteome Res. 2022;21(8):1829\u201341. https:\/\/doi.org\/10.1021\/acs.jproteome.2c00020.","journal-title":"J Proteome Res"},{"issue":"1","key":"6101_CR23","doi-asserted-by":"publisher","first-page":"20882","DOI":"10.1038\/s41598-023-47624-5","volume":"13","author":"A Chandra","year":"2023","unstructured":"Chandra A, Sharma A, Dehzangi I, Tsunoda T, Sattar A. PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features. Sci Rep. 2023;13(1):20882. https:\/\/doi.org\/10.1038\/s41598-023-47624-5.","journal-title":"Sci Rep"},{"issue":"9","key":"6101_CR24","doi-asserted-by":"publisher","first-page":"409","DOI":"10.3390\/a17090409","volume":"17","author":"SM Azim","year":"2024","unstructured":"Azim SM, Balasubramanyam A, Islam SR, Fu J, Dehzangi I. Explainable machine learning model to accurately predict protein-binding peptides. Algorithms. 2024;17(9):409. https:\/\/doi.org\/10.3390\/a17090409.","journal-title":"Algorithms"},{"issue":"2","key":"6101_CR25","doi-asserted-by":"publisher","first-page":"2874","DOI":"10.1021\/acsomega.3c08303","volume":"9","author":"M Arif","year":"2024","unstructured":"Arif M, Fang G, Fida H, Musleh S, Yu D-J, Alam T. iMRSAPred: improved prediction of anti-MRSA peptides using physicochemical and pairwise contact-energy properties of amino acids. ACS Omega. 2024;9(2):2874\u201383. https:\/\/doi.org\/10.1021\/acsomega.3c08303.","journal-title":"ACS Omega"},{"issue":"13","key":"6101_CR26","doi-asserted-by":"publisher","first-page":"4355","DOI":"10.1073\/pnas.84.13.4355","volume":"84","author":"M Gribskov","year":"1987","unstructured":"Gribskov M, McLachlan AD, Eisenberg D. Profile analysis: detection of distantly related proteins. Proc Natl Acad Sci. 1987;84(13):4355\u20138. https:\/\/doi.org\/10.1073\/pnas.84.13.4355.","journal-title":"Proc Natl Acad Sci"},{"issue":"S7","key":"6101_CR27","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1186\/s12859-022-04880-y","volume":"23","author":"Y Li","year":"2022","unstructured":"Li Y, et al. Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier. BMC Bioinformatics. 2022;23(S7):518. https:\/\/doi.org\/10.1186\/s12859-022-04880-y.","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"6101_CR28","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1186\/s12864-018-4849-9","volume":"19","author":"B Yu","year":"2018","unstructured":"Yu B, et al. Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction. BMC Genomics. 2018;19(1):478. https:\/\/doi.org\/10.1186\/s12864-018-4849-9.","journal-title":"BMC Genomics"},{"key":"6101_CR29","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.neucom.2016.03.025","volume":"199","author":"M Waris","year":"2016","unstructured":"Waris M, Ahmad K, Kabir M, Hayat M. Identification of DNA binding proteins using evolutionary profiles position specific scoring matrix. Neurocomputing. 2016;199:154\u201362. https:\/\/doi.org\/10.1016\/j.neucom.2016.03.025.","journal-title":"Neurocomputing"},{"issue":"2","key":"6101_CR30","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s00726-011-1114-9","volume":"43","author":"L Nanni","year":"2012","unstructured":"Nanni L, Brahnam S, Lumini A. Wavelet images and Chou\u2019s pseudo amino acid composition for protein classification. Amino Acids. 2012;43(2):657\u201365. https:\/\/doi.org\/10.1007\/s00726-011-1114-9.","journal-title":"Amino Acids"},{"issue":"5","key":"6101_CR31","doi-asserted-by":"publisher","first-page":"7211","DOI":"10.1016\/S1452-3981(23)14840-1","volume":"8","author":"X Wang","year":"2013","unstructured":"Wang X, Wang J, Fu C, Gao Y. Determination of corrosion type by wavelet-based fractal dimension from electrochemical noise. Int J Electrochem Sci. 2013;8(5):7211\u201322. https:\/\/doi.org\/10.1016\/S1452-3981(23)14840-1.","journal-title":"Int J Electrochem Sci"},{"key":"6101_CR32","doi-asserted-by":"publisher","first-page":"885627","DOI":"10.3389\/fgene.2022.885627","volume":"13","author":"K Lin","year":"2022","unstructured":"Lin K, Quan X, Jin C, Shi Z, Yang J. An interpretable double-scale attention model for enzyme protein class prediction based on transformer encoders and multi-scale convolutions. Front Genet. 2022;13:885627. https:\/\/doi.org\/10.3389\/fgene.2022.885627.","journal-title":"Front Genet"},{"issue":"1","key":"6101_CR33","doi-asserted-by":"publisher","first-page":"e4529","DOI":"10.1002\/pro.4529","volume":"32","author":"H Lee","year":"2023","unstructured":"Lee H, Lee S, Lee I, Nam H. AMP-BERT\u202f: Prediction of antimicrobial peptide function based on a BERT model. Protein Sci. 2023;32(1):e4529. https:\/\/doi.org\/10.1002\/pro.4529.","journal-title":"Protein Sci"},{"key":"6101_CR34","doi-asserted-by":"publisher","first-page":"144352","DOI":"10.1109\/ACCESS.2021.3119110","volume":"9","author":"D Fryer","year":"2021","unstructured":"Fryer D, Str\u00fcmke I, Nguyen H. Shapley values for feature selection: the good, the bad, and the axioms. IEEE Access. 2021;9:144352\u201360. https:\/\/doi.org\/10.1109\/ACCESS.2021.3119110.","journal-title":"IEEE Access"},{"issue":"1","key":"6101_CR35","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1109\/MCI.2021.3129959","volume":"17","author":"A Heuillet","year":"2022","unstructured":"Heuillet A, Couthouis F, Diaz-Rodriguez N. Collective eXplainable AI: explaining cooperative strategies and agent contribution in multiagent reinforcement learning with shapley values. IEEE Comput Intell Mag. 2022;17(1):59\u201371. https:\/\/doi.org\/10.1109\/MCI.2021.3129959.","journal-title":"IEEE Comput Intell Mag"},{"issue":"11","key":"6101_CR36","doi-asserted-by":"publisher","first-page":"2153","DOI":"10.3390\/life13112153","volume":"13","author":"S Khan","year":"2023","unstructured":"Khan S, Khan M, Iqbal N, Dilshad N, Almufareh MF, Alsubaie N. Enhancing sumoylation site prediction: a deep neural network with discriminative features. Life. 2023;13(11):2153. https:\/\/doi.org\/10.3390\/life13112153.","journal-title":"Life"},{"issue":"1","key":"6101_CR37","doi-asserted-by":"publisher","first-page":"16992","DOI":"10.1038\/s41598-024-67433-8","volume":"14","author":"M Arif","year":"2024","unstructured":"Arif M, Musleh S, Fida H, Alam T. PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation. Sci Rep. 2024;14(1):16992. https:\/\/doi.org\/10.1038\/s41598-024-67433-8.","journal-title":"Sci Rep"},{"issue":"7","key":"6101_CR38","doi-asserted-by":"publisher","first-page":"17112","DOI":"10.3934\/math.2023874","volume":"8","author":"M Naeem","year":"2023","unstructured":"Naeem M, Qiyas M, Abdullah L, Khan N, Khan S. Spherical fuzzy rough Hamacher aggregation operators and their application in decision making problem. AIMS Math. 2023;8(7):17112\u201341. https:\/\/doi.org\/10.3934\/math.2023874.","journal-title":"AIMS Math"},{"key":"6101_CR39","doi-asserted-by":"publisher","first-page":"6204","DOI":"10.1109\/ACCESS.2023.3347043","volume":"12","author":"M Qiyas","year":"2024","unstructured":"Qiyas M, Naeem M, Khan N, Khan S, Khan F. Confidence Levels bipolar complex fuzzy aggregation operators and their application in decision making problem. IEEE Access. 2024;12:6204\u201314. https:\/\/doi.org\/10.1109\/ACCESS.2023.3347043.","journal-title":"IEEE Access"},{"issue":"March","key":"6101_CR40","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.compbiomed.2019.04.018","volume":"109","author":"Z Zhu","year":"2019","unstructured":"Zhu Z, Albadawy E, Saha A, Zhang J, Harowicz MR, Mazurowski MA. Deep learning for identifying radiogenomic associations in breast cancer. Comput Biol Med. 2019;109(March):85\u201390. https:\/\/doi.org\/10.1016\/j.compbiomed.2019.04.018.","journal-title":"Comput Biol Med"},{"issue":"12","key":"6101_CR41","doi-asserted-by":"publisher","first-page":"7059","DOI":"10.3390\/app13127059","volume":"13","author":"S Khan","year":"2023","unstructured":"Khan S, et al. Optimized feature learning for anti-inflammatory peptide prediction using parallel distributed computing. Appl Sci. 2023;13(12):7059. https:\/\/doi.org\/10.3390\/app13127059.","journal-title":"Appl Sci"},{"key":"6101_CR42","doi-asserted-by":"publisher","first-page":"136978","DOI":"10.1109\/ACCESS.2020.3011508","volume":"8","author":"S Khan","year":"2020","unstructured":"Khan S, Khan M, Iqbal N, Li M, Khan DM. Spark-based parallel deep neural network model for classification of large scale RNAs into piRNAs and Non-piRNAs. IEEE Access. 2020;8:136978\u201391. https:\/\/doi.org\/10.1109\/ACCESS.2020.3011508.","journal-title":"IEEE Access"},{"issue":"6","key":"6101_CR43","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386.","journal-title":"Commun ACM"},{"issue":"1","key":"6101_CR44","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1186\/s12859-024-05917-0","volume":"25","author":"S Khan","year":"2024","unstructured":"Khan S, AlQahtani SA, Noor S, Ahmad N. PSSM-Sumo: deep learning based intelligent model for prediction of sumoylation sites using discriminative features. BMC Bioinformatics. 2024;25(1):284. https:\/\/doi.org\/10.1186\/s12859-024-05917-0.","journal-title":"BMC Bioinformatics"},{"issue":"2","key":"6101_CR45","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1007\/s10989-019-09887-3","volume":"26","author":"S Khan","year":"2020","unstructured":"Khan S, Khan M, Iqbal N, Hussain T, Khan SA, Chou K-C. A Two-Level Computation model based on deep learning algorithm for identification of piRNA and their functions via Chou\u2019s 5-steps rule. Int J Pept Res Ther. 2020;26(2):795\u2013809. https:\/\/doi.org\/10.1007\/s10989-019-09887-3.","journal-title":"Int J Pept Res Ther"},{"issue":"2","key":"6101_CR46","doi-asserted-by":"publisher","first-page":"2243","DOI":"10.32604\/cmc.2022.022901","volume":"72","author":"S Khan","year":"2022","unstructured":"Khan S, Khan M, Iqbal N, AmiruddinAbd Rahman M, Khalis Abdul Karim M. Deep-piRNA: Bi-layered prediction model for PIWI-interacting RNA using discriminative features. Comput Mater Contin. 2022;72(2):2243\u201358. https:\/\/doi.org\/10.32604\/cmc.2022.022901.","journal-title":"Comput Mater Contin"},{"issue":"1","key":"6101_CR47","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s13040-024-00415-8","volume":"18","author":"S Khan","year":"2025","unstructured":"Khan S, et al. XGBoost-enhanced ensemble model using discriminative hybrid features for the prediction of sumoylation sites. BioData Min. 2025;18(1):12. https:\/\/doi.org\/10.1186\/s13040-024-00415-8.","journal-title":"BioData Min"},{"key":"6101_CR48","doi-asserted-by":"publisher","DOI":"10.1007\/s12517-020-05529-x","author":"A Obadi","year":"2020","unstructured":"Obadi A, AlHarbi A, Abdel-Razzak H, Al-Omran A. Biochar and compost as soil amendments: effect on sweet pepper (Capsicum annuum L.) growth under partial root zone drying irrigation. Arab J Geosci. 2020. https:\/\/doi.org\/10.1007\/s12517-020-05529-x.","journal-title":"Arab J Geosci"},{"issue":"2","key":"6101_CR49","doi-asserted-by":"publisher","first-page":"487","DOI":"10.3390\/s21020487","volume":"21","author":"M Elsisi","year":"2021","unstructured":"Elsisi M, Mahmoud K, Lehtonen M, Darwish MMF. Reliable industry 4.0 based on machine learning and IoT for analyzing, monitoring, and securing smart meters. Sensors. 2021;21(2):487. https:\/\/doi.org\/10.3390\/s21020487.","journal-title":"Sensors"},{"issue":"1","key":"6101_CR50","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1080\/21642583.2014.956265","volume":"2","author":"K Fawagreh","year":"2014","unstructured":"Fawagreh K, Gaber MM, Elyan E. Random forests: from early developments to recent advancements. Syst Sci Control Eng. 2014;2(1):602\u20139. https:\/\/doi.org\/10.1080\/21642583.2014.956265.","journal-title":"Syst Sci Control Eng"},{"issue":"1","key":"6101_CR51","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1186\/s12859-024-05744-3","volume":"25","author":"M Arif","year":"2024","unstructured":"Arif M, Fang G, Ghulam A, Musleh S, Alam T. DPI_CDF: druggable protein identifier using cascade deep forest. BMC Bioinform. 2024;25(1):145. https:\/\/doi.org\/10.1186\/s12859-024-05744-3.","journal-title":"BMC Bioinform"},{"key":"6101_CR52","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.ymeth.2024.08.005","volume":"230","author":"A Amjad","year":"2024","unstructured":"Amjad A, Ahmed S, Kabir M, Arif M, Alam T. A novel deep learning identifier for promoters and their strength using heterogeneous features. Methods. 2024;230:119\u201328. https:\/\/doi.org\/10.1016\/j.ymeth.2024.08.005.","journal-title":"Methods"},{"issue":"22","key":"6101_CR53","doi-asserted-by":"publisher","first-page":"7239","DOI":"10.1021\/acs.jcim.3c00950","volume":"63","author":"F Ge","year":"2023","unstructured":"Ge F, et al. MMPatho: leveraging multilevel consensus and evolutionary information for enhanced missense mutation pathogenic prediction. J Chem Inf Model. 2023;63(22):7239\u201357. https:\/\/doi.org\/10.1021\/acs.jcim.3c00950.","journal-title":"J Chem Inf Model"},{"key":"6101_CR54","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/978-3-319-14717-8_39","volume-title":"Advanced data mining and applications","author":"D Cheng","year":"2014","unstructured":"Cheng D, Zhang S, Deng Z, Zhu Y, Zong M. kNN algorithm with data-driven k value. In: Luo X, Yu JX, Li Z, editors. Advanced data mining and applications. Cham: Springer International Publishing; 2014. p. 499\u2013512."},{"issue":"6","key":"6101_CR55","doi-asserted-by":"publisher","first-page":"581","DOI":"10.2174\/1568026615666150819104617","volume":"16","author":"G-P Zhou","year":"2015","unstructured":"Zhou G-P, Chen D, Liao S, Huang R-B. Recent Progresses in studying helix-helix interactions in proteins by incorporating the wenxiang diagram into the NMR spectroscopy. Curr Top Med Chem. 2015;16(6):581\u201390. https:\/\/doi.org\/10.2174\/1568026615666150819104617.","journal-title":"Curr Top Med Chem"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06101-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-025-06101-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06101-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T17:28:18Z","timestamp":1742664498000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-025-06101-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,22]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["6101"],"URL":"https:\/\/doi.org\/10.1186\/s12859-025-06101-8","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,22]]},"assertion":[{"value":"3 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"88"}}