{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T01:04:19Z","timestamp":1781053459713,"version":"3.54.1"},"reference-count":83,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:00:00Z","timestamp":1759881600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Council of Thailand and Mahidol University","award":["N42A660380"],"award-info":[{"award-number":["N42A660380"]}]},{"name":"Mahidol University Partnering Initiative under the MU-KMUTT Biomedical Engineering & Biomaterials Research Consortium","award":["-"],"award-info":[{"award-number":["-"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Tyrosinase plays a central role in melanin biosynthesis, and its dysregulation has been implicated in the pathogenesis of various pigmentation disorders. The precise identification of tyrosinase inhibitory peptides (TIPs) is critical, as these bioactive molecules hold significant potential for therapeutic and cosmetic applications, including the treatment of hyperpigmentation and the development of skin-whitening agents. To date, computational methods have received significant attention as a complement to experimental methods for the <jats:italic><jats:italic>in silico<\/jats:italic><\/jats:italic> identification of TIPs, reducing the need for extensive material resources and labor-intensive processes. In this study, we propose an innovative computational approach, BLSAM-TIP, which combines a bidirectional long short-term memory (BiLSTM) network and a self-attention mechanism (SAM) for accurate and large-scale identification of TIPs. In BLSAM-TIP, we first employed various multi-source feature embeddings, including conventional feature encodings, natural language processing-based encodings, and protein language model-based encodings, to encode comprehensive information about TIPs. Secondly, we integrated these feature embeddings to enhance feature representation, while a feature selection method was applied to optimize the hybrid features. Thirdly, the BiLSTM-SAM architecture was specially developed to highlight the crucial features. Finally, the features from BiLSTM-SAM was fed to deep neural networks (DNN) in order to identify TIPs. Experimental results on an independent test dataset demonstrate that BLSAM-TIP attains superior predictive performance compared to existing methods, with a balanced accuracy of 0.936, MCC of 0.922, and AUC of 0.988. These results indicate that this new method is an accurate and efficient tool for identifying TIPs. Our proposed method is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/saeed344\/BLSAM-TIP\" xlink:type=\"simple\">https:\/\/github.com\/saeed344\/BLSAM-TIP<\/jats:ext-link> for TIP identification and reproducibility purposes.<\/jats:p>","DOI":"10.1371\/journal.pone.0333614","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T17:36:38Z","timestamp":1759944998000},"page":"e0333614","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["BLSAM-TIP: Improved and robust identification of tyrosinase inhibitory peptides by integrating bidirectional LSTM with self-attention mechanism"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6910-7613","authenticated-orcid":true,"given":"Saeed","family":"Ahmed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nalini","family":"Schaduangrat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3072-1733","authenticated-orcid":true,"given":"Pramote","family":"Chumnanpuen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. M. Hasan","family":"Mahmud","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9217-6390","authenticated-orcid":true,"given":"Kah Ong Michael","family":"Goh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3394-8709","authenticated-orcid":true,"given":"Watshara","family":"Shoombuatong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,10,8]]},"reference":[{"key":"pone.0333614.ref001","first-page":"211","article-title":"Sunlight and melanin pigmentation","volume":"1","author":"MA Pathak","year":"1976","journal-title":"Photochem Photobiol Rev"},{"key":"pone.0333614.ref002","doi-asserted-by":"crossref","first-page":"133326","DOI":"10.1016\/j.ijbiomac.2024.133326","article-title":"Inhibitory effect and molecular mechanism on tyrosinase and browning of fresh-cut apple by longan shell tannins","volume":"274","author":"W Chai","year":"2024","journal-title":"Int J Biol Macromol"},{"issue":"26","key":"pone.0333614.ref003","doi-asserted-by":"crossref","first-page":"7764","DOI":"10.1021\/jf030131u","article-title":"Tyrosinase inhibitory activity of cucumber compounds: enzymes responsible for browning in cucumber","volume":"51","author":"F Gand\u00eda-Herrero","year":"2003","journal-title":"J Agricul Food Chem"},{"issue":"15","key":"pone.0333614.ref004","doi-asserted-by":"crossref","first-page":"5762","DOI":"10.3390\/molecules28155762","article-title":"Tyrosinase inhibitors: a perspective","volume":"28","author":"MA Baber","year":"2023","journal-title":"Molecules"},{"issue":"6","key":"pone.0333614.ref005","doi-asserted-by":"crossref","first-page":"2440","DOI":"10.3390\/ijms10062440","article-title":"An updated review of tyrosinase inhibitors","volume":"10","author":"T-S Chang","year":"2009","journal-title":"Int J Mol Sci"},{"issue":"22","key":"pone.0333614.ref006","doi-asserted-by":"crossref","first-page":"6998","DOI":"10.3390\/molecules26226998","article-title":"Theoretical exploring of a molecular mechanism for melanin inhibitory activity of calycosin in Zebrafish","volume":"26","author":"N Tayier","year":"2021","journal-title":"Molecules"},{"issue":"4","key":"pone.0333614.ref007","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1038\/sj.jid.5700683","article-title":"Approaches to identify inhibitors of melanin biosynthesis via the quality control of tyrosinase","volume":"127","author":"H Ando","year":"2007","journal-title":"J Invest Dermatol"},{"issue":"1","key":"pone.0333614.ref008","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1186\/s12859-023-05463-1","article-title":"TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides","volume":"24","author":"P Charoenkwan","year":"2023","journal-title":"BMC Bioinform"},{"issue":"9","key":"pone.0333614.ref009","doi-asserted-by":"crossref","first-page":"2710","DOI":"10.3390\/molecules27092710","article-title":"The hypopigmentation mechanism of tyrosinase inhibitory peptides derived from food proteins: an overview","volume":"27","author":"Y Song","year":"2022","journal-title":"Molecules"},{"issue":"1","key":"pone.0333614.ref010","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3109\/14756366.2015.1004058","article-title":"Natural, semisynthetic and synthetic tyrosinase inhibitors","volume":"31","author":"SY Lee","year":"2016","journal-title":"J Enzyme Inhib Med Chem"},{"issue":"1","key":"pone.0333614.ref011","first-page":"1","article-title":"The unravelling of the complex pattern of tyrosinase inhibition","volume":"6","author":"B Deri","year":"2016","journal-title":"Scient Rep"},{"issue":"4","key":"pone.0333614.ref012","doi-asserted-by":"crossref","first-page":"3154","DOI":"10.3390\/ijms24043154","article-title":"Computer-aided virtual screening and in vitro validation of biomimetic tyrosinase inhibitory peptides from abalone peptidome","volume":"24","author":"S Kongsompong","year":"2023","journal-title":"Int J Mol Sci"},{"issue":"1","key":"pone.0333614.ref013","doi-asserted-by":"crossref","first-page":"378","DOI":"10.3390\/molecules28010378","article-title":"Tyrosinase inhibitors naturally present in plants and synthetic modifications of these natural products as anti-melanogenic agents: a review","volume":"28","author":"M Hassan","year":"2023","journal-title":"Molecules"},{"issue":"21","key":"pone.0333614.ref014","doi-asserted-by":"crossref","first-page":"3884","DOI":"10.3390\/foods12213884","article-title":"Optimization of a novel tyrosinase inhibitory peptide from atrina pectinata mantle and its molecular inhibitory mechanism","volume":"12","author":"W Wang","year":"2023","journal-title":"Foods"},{"issue":"12","key":"pone.0333614.ref015","doi-asserted-by":"crossref","first-page":"3671","DOI":"10.3390\/molecules26123671","article-title":"K-nearest neighbor and random forest-based prediction of putative tyrosinase inhibitory peptides of abalone haliotis diversicolor","volume":"26","author":"S Kongsompong","year":"2021","journal-title":"Molecules"},{"issue":"1","key":"pone.0333614.ref016","doi-asserted-by":"crossref","first-page":"7697","DOI":"10.1038\/s41598-022-11897-z","article-title":"AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning","volume":"12","author":"P Charoenkwan","year":"2022","journal-title":"Sci Rep"},{"key":"pone.0333614.ref017","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.ymeth.2021.12.001","article-title":"StackDPPIV: a novel computational approach for accurate prediction of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides","volume":"204","author":"P Charoenkwan","year":"2022","journal-title":"Methods"},{"key":"pone.0333614.ref018","doi-asserted-by":"crossref","first-page":"107455","DOI":"10.1016\/j.future.2024.07.033","article-title":"M3S-ALG: Improved and robust prediction of allergenicity of chemical compounds by using a novel multi-step stacking strategy","volume":"162","author":"P Charoenkwan","year":"2025","journal-title":"Future Gener Comp Syst"},{"issue":"1","key":"pone.0333614.ref019","doi-asserted-by":"crossref","first-page":"4463","DOI":"10.1038\/s41598-024-55160-z","article-title":"Leveraging a meta-learning approach to advance the accuracy of Nav blocking peptides prediction","volume":"14","author":"W Shoombuatong","year":"2024","journal-title":"Sci Rep"},{"key":"pone.0333614.ref020","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.ymeth.2024.08.003","article-title":"Stack-HDAC3i: a high-precision identification of HDAC3 inhibitors by exploiting a stacked ensemble-learning framework","volume":"230","author":"W Shoombuatong","year":"2024","journal-title":"Methods"},{"key":"pone.0333614.ref021","doi-asserted-by":"crossref","first-page":"108859","DOI":"10.1016\/j.compbiomed.2024.108859","article-title":"HOTGpred: enhancing human O-linked threonine glycosylation prediction using integrated pretrained protein language model-based features and multi-stage feature selection approach","volume":"179","author":"NT Pham","year":"2024","journal-title":"Comput Biol Med"},{"issue":"2","key":"pone.0333614.ref022","article-title":"ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein-DNA binding site prediction","volume":"25","author":"Y-H Zhu","year":"2024","journal-title":"Brief Bioinform"},{"issue":"1","key":"pone.0333614.ref023","doi-asserted-by":"crossref","first-page":"2542","DOI":"10.1038\/s41467-018-04964-5","article-title":"Clustering huge protein sequence sets in linear time","volume":"9","author":"M Steinegger","year":"2018","journal-title":"Nat Commun"},{"issue":"7","key":"pone.0333614.ref024","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/s41592-019-0437-4","article-title":"Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold","volume":"16","author":"M Steinegger","year":"2019","journal-title":"Nat Methods"},{"issue":"10","key":"pone.0333614.ref025","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.1093\/bioinformatics\/btm098","article-title":"UniRef: comprehensive and non-redundant UniProt reference clusters","volume":"23","author":"BE Suzek","year":"2007","journal-title":"Bioinformatics"},{"key":"pone.0333614.ref026","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkh121","article-title":"The Pfam protein families database","volume":"32","author":"A Bateman","year":"2004","journal-title":"Nucleic Acids Res"},{"issue":"23","key":"pone.0333614.ref027","doi-asserted-by":"crossref","first-page":"4007","DOI":"10.1093\/bioinformatics\/bty451","article-title":"ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides","volume":"34","author":"L Wei","year":"2018","journal-title":"Bioinformatics"},{"issue":"5","key":"pone.0333614.ref028","doi-asserted-by":"crossref","first-page":"1846","DOI":"10.1093\/bib\/bbz088","article-title":"ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides","volume":"21","author":"B Rao","year":"2020","journal-title":"Brief Bioinform"},{"issue":"6","key":"pone.0333614.ref029","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab172","article-title":"StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides","volume":"22","author":"P Charoenkwan","year":"2021","journal-title":"Brief Bioinform"},{"issue":"1","key":"pone.0333614.ref030","doi-asserted-by":"crossref","first-page":"2193676","DOI":"10.1080\/14756366.2023.2193676","article-title":"Tripeptides conjugated with thiosemicarbazones: new inhibitors of tyrosinase for cosmeceutical use","volume":"38","author":"P Ledwo\u0144","year":"2023","journal-title":"J Enzyme Inhib Med Chem"},{"issue":"5","key":"pone.0333614.ref031","article-title":"Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection","volume":"24","author":"NQK Le","year":"2023","journal-title":"Brief Bioinform"},{"key":"pone.0333614.ref032","doi-asserted-by":"crossref","first-page":"112307","DOI":"10.1016\/j.foodres.2022.112307","article-title":"Copper chelating peptides derived from tilapia (Oreochromis niloticus) skin as tyrosinase inhibitor: biological evaluation, in silico investigation and in vivo effects","volume":"163","author":"Y Song","year":"2023","journal-title":"Food Res Int"},{"key":"pone.0333614.ref033","article-title":"BERT-Kgly: a bidirectional encoder representations from transformers (BERT)-based model for predicting lysine glycation site for homo sapiens","volume":"2","author":"Y Liu","year":"2022","journal-title":"Original Res"},{"issue":"6","key":"pone.0333614.ref034","doi-asserted-by":"crossref","first-page":"2744","DOI":"10.1111\/1750-3841.16160","article-title":"Identification and molecular mechanism of novel tyrosinase inhibitory peptides from collagen","volume":"87","author":"W Xue","year":"2022","journal-title":"J Food Sci"},{"key":"pone.0333614.ref035","doi-asserted-by":"crossref","first-page":"106591","DOI":"10.1016\/j.compbiomed.2023.106591","article-title":"Prediction of antioxidant peptides using a quantitative structure-activity relationship predictor (AnOxPP) based on bidirectional long short-term memory neural network and interpretable amino acid descriptors","volume":"154","author":"D Qin","year":"2023","journal-title":"Comput Biol Med"},{"key":"pone.0333614.ref036","first-page":"1","article-title":"FEGS: a novel feature extraction model for protein sequences and its applications","volume":"22","author":"Z Mu","year":"2021","journal-title":"BMC Bioinform"},{"issue":"21","key":"pone.0333614.ref037","doi-asserted-by":"crossref","first-page":"6537","DOI":"10.1021\/acs.jcim.3c01563","article-title":"AIPs-SnTCN: Predicting anti-inflammatory peptides using fastText and transformer encoder-based hybrid word embedding with self-normalized temporal convolutional networks","volume":"63","author":"A Raza","year":"2023","journal-title":"J Chem Inform Model"},{"issue":"17","key":"pone.0333614.ref038","doi-asserted-by":"crossref","first-page":"2556","DOI":"10.1093\/bioinformatics\/btab133","article-title":"BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides","volume":"37","author":"P Charoenkwan","year":"2021","journal-title":"Bioinformatics"},{"key":"pone.0333614.ref039","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1016\/j.ymeth.2021.07.011","article-title":"Deep-4mCW2V: a sequence-based predictor to identify N4-methylcytosine sites in Escherichia coli","volume":"203","author":"H Zulfiqar","year":"2022","journal-title":"Methods"},{"key":"pone.0333614.ref040","doi-asserted-by":"crossref","first-page":"2300011","DOI":"10.1002\/pmic.202300011","article-title":"Leveraging transformers\u2010based language models in proteome bioinformatics","volume":"23","author":"NQK Le","year":"2023","journal-title":"Proteomics"},{"issue":"3","key":"pone.0333614.ref041","article-title":"Using deep neural networks and biological subwords to detect protein S-sulfenylation sites","volume":"22","author":"DT Do","year":"2021","journal-title":"Brief Bioinform"},{"key":"pone.0333614.ref042","article-title":"Bag of tricks for efficient text classification","author":"A Joulin","year":"2016","journal-title":"arXiv preprint"},{"issue":"11","key":"pone.0333614.ref043","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0141287","article-title":"Continuous distributed representation of biological sequences for deep proteomics and genomics","volume":"10","author":"E Asgari","year":"2015","journal-title":"PLoS One"},{"key":"pone.0333614.ref044","volume-title":"BERT: pre-training of deep bidirectional transformers for language understanding","author":"J Devlin","year":"2018"},{"issue":"140","key":"pone.0333614.ref045","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"C Raffel","year":"2020","journal-title":"J Mach Learn Res"},{"issue":"7873","key":"pone.0333614.ref046","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"J Jumper","year":"2021","journal-title":"Nature"},{"issue":"6637","key":"pone.0333614.ref047","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1126\/science.ade2574","article-title":"Evolutionary-scale prediction of atomic-level protein structure with a language model","volume":"379","author":"Z Lin","year":"2023","journal-title":"Science"},{"key":"pone.0333614.ref048","doi-asserted-by":"crossref","first-page":"107145","DOI":"10.1016\/j.compbiomed.2023.107145","article-title":"BiGRUD-SA: protein S-sulfenylation sites prediction based on BiGRU and self-attention","volume":"163","author":"T Zhang","year":"2023","journal-title":"Comput Biol Med"},{"key":"pone.0333614.ref049","doi-asserted-by":"crossref","first-page":"111354","DOI":"10.1016\/j.knosys.2023.111354","article-title":"DRBPPred-GAT: Accurate prediction of DNA-binding proteins and RNA-binding proteins based on graph multi-head attention network","volume":"285","author":"X Zhang","year":"2024","journal-title":"Knowledge-Based Syst"},{"key":"pone.0333614.ref050","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.neucom.2014.12.123","article-title":"A novel features ranking metric with application to scalable visual and bioinformatics data classification","volume":"173","author":"Q Zou","year":"2016","journal-title":"Neurocomputing"},{"issue":"3","key":"pone.0333614.ref051","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1007\/s00415-022-11476-2","article-title":"Identification of hub proteins in cerebrospinal fluid as potential biomarkers of Alzheimer\u2019s disease by integrated bioinformatics","volume":"270","author":"Y Li","year":"2023","journal-title":"J Neurol"},{"key":"pone.0333614.ref052","article-title":"LSTM can solve hard long time lag problems","volume":"9","author":"S Hochreiter","year":"1996","journal-title":"Adv Neural Inform Process Syst"},{"issue":"02","key":"pone.0333614.ref053","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1142\/S0218488598000094","article-title":"The vanishing gradient problem during learning recurrent neural nets and problem solutions","volume":"6","author":"S Hochreiter","year":"1998","journal-title":"Inter J Uncertainty, Fuzziness and Knowledge-Based Syst"},{"key":"pone.0333614.ref054","first-page":"2","article-title":"Recurrent neural networks","volume":"5","author":"LR Medsker","year":"2001","journal-title":"Design Appl"},{"key":"pone.0333614.ref055","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/S0065-2601(08)60321-4","article-title":"Self-attention and behavior: a review and theoretical update","volume":"23","author":"FX Gibbons","year":"1990","journal-title":"Adv Exp Soc Psychol"},{"key":"pone.0333614.ref056","article-title":"Attention is all you need","author":"A Vaswani","year":"2017","journal-title":"Adv Neural Inform Process Syst"},{"issue":"2","key":"pone.0333614.ref057","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1007\/s12539-024-00628-9","article-title":"A computational predictor for accurate identification of tumor homing peptides by integrating sequential and deep BiLSTM features","volume":"16","author":"R Arif","year":"2024","journal-title":"Interdiscip Sci"},{"key":"pone.0333614.ref058","doi-asserted-by":"crossref","first-page":"106186","DOI":"10.1016\/j.asoc.2020.106186","article-title":"BG-SAC: entity relationship classification model based on self-attention supported capsule networks","volume":"91","author":"D Peng","year":"2020","journal-title":"Appl Soft Comput"},{"key":"pone.0333614.ref059","first-page":"11","article-title":"Large-scale comparative review and assessment of computational methods for phage virion proteins identification","volume":"21","author":"M Kabir","year":"2022","journal-title":"EXCLI J"},{"issue":"10","key":"pone.0333614.ref060","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.3390\/molecules24101973","article-title":"ACPred: a computational tool for the prediction and analysis of anticancer peptides","volume":"24","author":"N Schaduangrat","year":"2019","journal-title":"Molecules"},{"key":"pone.0333614.ref061","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"NV Chawla","year":"2002","journal-title":"J Artificial Intelligence Res"},{"key":"pone.0333614.ref062","doi-asserted-by":"crossref","first-page":"105909","DOI":"10.1016\/j.bspc.2023.105909","article-title":"AntiCVP-Deep: identify anti-coronavirus peptides between different negative datasets based on self-attention and deep learning","volume":"90","author":"L Yan","year":"2024","journal-title":"Biomed Signal Process Control"},{"issue":"9","key":"pone.0333614.ref063","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1097\/JTO.0b013e3181ec173d","article-title":"Receiver operating characteristic curve in diagnostic test assessment","volume":"5","author":"JN Mandrekar","year":"2010","journal-title":"J Thorac Oncol"},{"issue":"22","key":"pone.0333614.ref064","doi-asserted-by":"crossref","first-page":"7239","DOI":"10.1021\/acs.jcim.3c00950","article-title":"MMPatho: leveraging multilevel consensus and evolutionary information for enhanced missense mutation pathogenic prediction","volume":"63","author":"F Ge","year":"2023","journal-title":"J Chem Inf Model"},{"issue":"3","key":"pone.0333614.ref065","doi-asserted-by":"crossref","DOI":"10.1121\/1.4865840","article-title":"Estimating confidence intervals for information transfer analysis of confusion matrices","volume":"135","author":"M Azadpour","year":"2014","journal-title":"J Acous Soc Am"},{"issue":"24","key":"pone.0333614.ref066","doi-asserted-by":"crossref","first-page":"9609","DOI":"10.1021\/acs.jcim.4c01758","article-title":"DeepAIPs-Pred: predicting anti-inflammatory peptides using local evolutionary transformation images and structural embedding-based optimal descriptors with self-normalized BiTCNs","volume":"64","author":"S Akbar","year":"2024","journal-title":"J Chem Inf Model"},{"key":"pone.0333614.ref067","doi-asserted-by":"crossref","first-page":"102860","DOI":"10.1016\/j.artmed.2024.102860","article-title":"iAFPs-Mv-BiTCN: predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks","volume":"151","author":"S Akbar","year":"2024","journal-title":"Artif Intell Med"},{"issue":"1","key":"pone.0333614.ref068","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1186\/s12859-024-05726-5","article-title":"Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model","volume":"25","author":"S Akbar","year":"2024","journal-title":"BMC Bioinform"},{"key":"pone.0333614.ref069","doi-asserted-by":"crossref","first-page":"103919","DOI":"10.1016\/j.chemolab.2019.103919","article-title":"RBPro-RF: use Chou\u2019s 5-steps rule to predict RNA-binding proteins via random forest with elastic net","volume":"197","author":"X Sun","year":"2020","journal-title":"Chemometrics Intelligent Lab Syst"},{"issue":"1","key":"pone.0333614.ref070","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"R Tibshirani","year":"1996","journal-title":"J Royal Stat Soc Series B: Stat Methodol"},{"key":"pone.0333614.ref071","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1145\/502512.502546","article-title":"Random projection in dimensionality reduction: applications to image and text data","author":"E Bingham","year":"2001","journal-title":"Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining"},{"issue":"11","key":"pone.0333614.ref072","article-title":"Visualizing data using t-SNE","volume":"9","author":"L Van der Maaten","year":"2008","journal-title":"J Machine Learn Res"},{"issue":"1","key":"pone.0333614.ref073","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1186\/s12859-023-05421-x","article-title":"StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens","volume":"24","author":"P Charoenkwan","year":"2023","journal-title":"BMC Bioinform"},{"issue":"3","key":"pone.0333614.ref074","article-title":"DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy","volume":"22","author":"R Xie","year":"2021","journal-title":"Brief Bioinform"},{"key":"pone.0333614.ref075","doi-asserted-by":"crossref","first-page":"114695","DOI":"10.1016\/j.lwt.2023.114695","article-title":"Identification and molecular mechanism of novel tyrosinase inhibitory peptides from the hydrolysate of \u201cFengdan\u201d peony (Paeonia ostii) seed meal proteins: peptidomics and in silico analysis","volume":"180","author":"Y Zhao","year":"2023","journal-title":"LWT"},{"issue":"31","key":"pone.0333614.ref076","doi-asserted-by":"crossref","first-page":"8346","DOI":"10.1021\/acs.jafc.8b01849","article-title":"Isolation and identification of tyrosinase-inhibitory and copper-chelating peptides from hydrolyzed rice-bran-derived albumin","volume":"66","author":"S Kubglomsong","year":"2018","journal-title":"J Agric Food Chem"},{"issue":"9","key":"pone.0333614.ref077","doi-asserted-by":"crossref","first-page":"758","DOI":"10.2174\/1389203724666230622095013","article-title":"Identification of tyrosinase inhibitory peptides from sea cucumber (Apostichopus japonicus) collagen by in silico methods and study of their molecular mechanism","volume":"24","author":"H Chen","year":"2023","journal-title":"Curr Protein Pept Sci"},{"key":"pone.0333614.ref078","doi-asserted-by":"crossref","first-page":"100598","DOI":"10.1016\/j.crfs.2023.100598","article-title":"Rational design of an N-terminal cysteine-containing tetrapeptide that inhibits tyrosinase and evaluation of its mechanism of action","volume":"7","author":"A Joompang","year":"2023","journal-title":"Curr Res Food Sci"},{"key":"pone.0333614.ref079","doi-asserted-by":"crossref","first-page":"645","DOI":"10.2147\/DDDT.S500004","article-title":"Peptide design for enhanced anti-melanogenesis: optimizing molecular weight, polarity, and cyclization","author":"SA Putri","year":"2025","journal-title":"Drug Design Develop Therapy"},{"issue":"6","key":"pone.0333614.ref080","doi-asserted-by":"crossref","first-page":"3648","DOI":"10.1002\/jsfa.13249","article-title":"Identification and molecular docking of tyrosinase inhibitory peptides from allophycocyanin in Spirulina platensis","volume":"104","author":"Z Yu","year":"2024","journal-title":"J Sci Food Agric"},{"issue":"7","key":"pone.0333614.ref081","doi-asserted-by":"crossref","first-page":"6158","DOI":"10.3390\/ijms24076158","article-title":"Neurog1-derived peptides RMNE1 and DualPep-Shine penetrate the skin and inhibit melanin synthesis by regulating MITF transcription","volume":"24","author":"EC Song","year":"2023","journal-title":"Int J Mol Sci"},{"key":"pone.0333614.ref082","doi-asserted-by":"crossref","first-page":"171215","DOI":"10.1016\/j.peptides.2024.171215","article-title":"Newly identified peptide Nigrocin-OA27 inhibits UVB induced melanin production via the MITF\/TYR pathway","volume":"177","author":"J Li","year":"2024","journal-title":"Peptides"},{"issue":"1","key":"pone.0333614.ref083","doi-asserted-by":"crossref","first-page":"21471","DOI":"10.1038\/s41598-020-78319-w","article-title":"AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides","volume":"10","author":"TH Olsen","year":"2020","journal-title":"Sci Rep"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0333614","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T17:36:51Z","timestamp":1759945011000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0333614"}},"subtitle":[],"editor":[{"given":"Yunhe","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,10,8]]},"references-count":83,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10,8]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0333614","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,8]]}}}