{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T14:21:18Z","timestamp":1780323678291,"version":"3.54.1"},"reference-count":48,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T00:00:00Z","timestamp":1717632000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Antimicrobial resistance, particularly the emergence of resistant strains in fungal pathogens, has become a pressing global health concern. Antifungal peptides (AFPs) have shown great potential as a promising alternative therapeutic strategy due to their inherent antimicrobial properties and potential application in combating fungal infections. However, the identification of antifungal peptides using experimental approaches is time-consuming and costly. Hence, there is a demand to propose fast and accurate computational approaches to identifying AFPs. This paper introduces a novel multi-view feature learning (MVFL) model, called AFP-MVFL, for accurate AFP identification, utilizing multi-view feature learning. By integrating the sequential and physicochemical properties of amino acids and employing a multi-view approach, the AFP-MVFL model significantly enhances prediction accuracy. It achieves 97.9%, 98.4%, 0.98, and 0.96 in terms of accuracy, precision, F1 score, and Matthews correlation coefficient (MCC), respectively, outperforming previous studies found in the literature.<\/jats:p>","DOI":"10.3390\/a17060247","type":"journal-article","created":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T06:40:26Z","timestamp":1717656026000},"page":"247","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["New Multi-View Feature Learning Method for Accurate Antifungal Peptide Detection"],"prefix":"10.3390","volume":"17","author":[{"given":"Sayeda Muntaha","family":"Ferdous","sequence":"first","affiliation":[{"name":"Department of Computer Science & Engineering, United International University, Dhaka 1212, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shafayat Bin Shabbir","family":"Mugdha","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, United International University, Dhaka 1212, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8577-0271","authenticated-orcid":false,"given":"Iman","family":"Dehzangi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rutgers University, Camden, NJ 08854, USA"},{"name":"Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08103, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bongomin, F., Gago, S., Oladele, R.O., and Denning, D.W. (2017). Global and Multi-National Prevalence of Fungal Diseases\u2014Estimate Precision. J. Fungi, 3.","DOI":"10.3390\/jof3040057"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"i5","DOI":"10.1093\/jac\/dki218","article-title":"Changing patterns and trends in systemic fungal infections","volume":"56","author":"Richardson","year":"2005","journal-title":"J. Antimicrob. Chemother."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/S1473-3099(10)70218-8","article-title":"Emerging opportunistic yeast infections","volume":"11","author":"Miceli","year":"2011","journal-title":"Lancet Infect. Dis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"165rv13","DOI":"10.1126\/scitranslmed.3004404","article-title":"Hidden Killers: Human Fungal Infections","volume":"4","author":"Brown","year":"2012","journal-title":"Sci. Transl. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nrd.2017.46","article-title":"The antifungal pipeline: A reality check","volume":"16","author":"Perfect","year":"2017","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Butts, A., and Krysan, D.J. (2012). Antifungal Drug Discovery: Something Old and Something New. PLOS Pathog., 8.","DOI":"10.1371\/journal.ppat.1002870"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1626","DOI":"10.3923\/pjbs.2013.1626.1640","article-title":"Fungal\/mycotic diseases of poultry-diagnosis, treatment and control: A review","volume":"16","author":"Dhama","year":"2013","journal-title":"Pak. J. Biol. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1093\/cid\/ciy859","article-title":"Voricona-zole resistance and mortality in invasive aspergillosis: A multi-center retrospective cohort study","volume":"68","author":"Lestrade","year":"2019","journal-title":"Clin. Infect. Dis."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fang, Y., Xu, F., Wei, L., Jiang, Y., Chen, J., Wei, L., and Wei, D.-Q. (2023). AFP-MFL: Accurate identification of antifungal peptides using multi-view feature learning. Brief. Bioinform., 24.","DOI":"10.1093\/bib\/bbac606"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Agrawal, P., Bhalla, S., Chaudhary, K., Kumar, R., Sharma, M., and Raghava, G.P. (2018). In silico approach for prediction of antifungal peptides. Front. Microbiol., 9.","DOI":"10.3389\/fmicb.2018.00323"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1126\/science.aap7999","article-title":"Worldwide emergence of resistance to antifungal drugs challenges human health and food security","volume":"360","author":"Fisher","year":"2018","journal-title":"Science"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"104214","DOI":"10.1016\/j.chemolab.2020.104214","article-title":"Deep-AntiFP: Prediction of antifungal peptides using distanct multi-informative features incorporating with deep neural networks","volume":"208","author":"Ahmad","year":"2021","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"49024","DOI":"10.1109\/ACCESS.2023.3274601","article-title":"Identifying Neuropeptides via Evolutionary and Sequential Based Multi-Perspective Descriptors by Incorporation With Ensemble Classification Strategy","volume":"11","author":"Akbar","year":"2023","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e4758","DOI":"10.1002\/pro.4758","article-title":"DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning","volume":"32","author":"Yao","year":"2023","journal-title":"Protein Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1016\/j.bbamem.2015.07.008","article-title":"Antimicrobial peptide protonectin disturbs the membrane integrity and induces ROS production in yeast cells","volume":"1848","author":"Wang","year":"2015","journal-title":"Biochim. Biophys. Acta (BBA)-Biomembr."},{"key":"ref_16","first-page":"92","article-title":"Solution structures of stomoxyn and spinigerin, two insect antimicrobial peptides with an \u03b1-helical conformation","volume":"81","author":"Landon","year":"2006","journal-title":"Biopolym. Orig. Res. Biomol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mousavizadegan, M., and Mohabatkar, H. (2018). Computational prediction of antifungal peptides via Chou\u2019s PseAAC and SVM. J. Bioinform. Comput. Biol., 16.","DOI":"10.1142\/S0219720018500166"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"23676","DOI":"10.1038\/s41598-021-02703-3","article-title":"ACP-MHCNN: An accurate multi-headed deep-convolutional neural network to predict anticancer peptides","volume":"11","author":"Ahmed","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"21","DOI":"10.2197\/ipsjtbio.12.21","article-title":"Prediction of antifungal peptides by deep learning with character embedding","volume":"12","author":"Fang","year":"2019","journal-title":"IPSJ Trans. Bioinform."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"24482","DOI":"10.1038\/srep24482","article-title":"DRAMP: A comprehensive data repository of antimicrobial peptides","volume":"6","author":"Fan","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104516","DOI":"10.1016\/j.chemolab.2022.104516","article-title":"iAFPs-EnC-GA: Identifying antifungal peptides using sequential and evolutionary descrip-tors based multi-information fusion and ensemble learning approach","volume":"222","author":"Ahmad","year":"2022","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sharma, R., Shrivastava, S., Kumar Singh, S., Kumar, A., Saxena, S., and Kumar Singh, R. (2022). Deep-AFPpred: Identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM. Brief. Bioinform., 3.","DOI":"10.1093\/bib\/bbab422"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"He, W., Jiang, Y., Jin, J., Li, Z., Zhao, J., Manavalan, B., Su, R., Gao, X., and Wei, L. (2022). Accelerating bioactive peptide discovery via mutual information-based meta-learning. Brief. Bioinform., 23.","DOI":"10.1093\/bib\/bbab499"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e1900119","DOI":"10.1002\/pmic.201900119","article-title":"Protein function prediction: From traditional classifier to deep learning","volume":"19","author":"Lv","year":"2019","journal-title":"Proteomics"},{"key":"ref_25","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2024, May 01). Attention Is All You Need. Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/hash\/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1093\/bioinformatics\/btg424","article-title":"SVM based method for predicting HLA-DRB1* 0401 binding peptides in an antigen sequence","volume":"20","author":"Bhasin","year":"2004","journal-title":"Bioinformatics"},{"key":"ref_27","unstructured":"Zhang, Y. (2012). Information Computing and Applications: Proceedings of the Third International Conference, ICICA 2012, Chengde, China, 14\u201316 September 2012, Springer. Part II 3."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lata, S., Mishra, N.K., and Raghava, G.P. (2010). AntiBP2: Improved version of antibacterial peptide prediction. BMC Bioinform., 11.","DOI":"10.1186\/1471-2105-11-S1-S19"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"10121","DOI":"10.1016\/j.jfranklin.2021.10.005","article-title":"Random radial basis function kernel-based support vector machine","volume":"358","author":"Ding","year":"2021","journal-title":"J. Frankl. Inst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1016\/j.jclinepi.2009.11.020","article-title":"Propensity score estimation: Neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression","volume":"63","author":"Westreich","year":"2010","journal-title":"J. Clin. Epidemiol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2409","DOI":"10.1002\/sim.1047","article-title":"A solution to the problem of separation in logistic regression","volume":"21","author":"Heinze","year":"2002","journal-title":"Stat. Med."},{"key":"ref_32","unstructured":"Rish, I. (2001, January 4). An empirical study of the naive Bayes classifier. Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, Seattle, WA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4451","DOI":"10.1016\/j.patcog.2012.05.002","article-title":"A noise-detection based AdaBoost algorithm for mislabeled data","volume":"45","author":"Cao","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_35","first-page":"9","article-title":"Random forest vs logistic regression: Binary classification for heterogeneous datasets","volume":"1","author":"Kirasich","year":"2018","journal-title":"SMU Data Sci. Rev."},{"key":"ref_36","first-page":"2715","article-title":"Comparison of optimization techniques based on gradient descent algorithm: A review","volume":"18","author":"Haji","year":"2021","journal-title":"PalArch\u2019s J. Archaeol. Egypt\/Egyptol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bottou, L. (2010, January 22\u201327). Large-scale machine learning with stochastic gradient descent. Proceedings of the COMPSTAT\u20192010: 19th International Conference on Computational Statistics, Paris, France.","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s10489-016-0785-z","article-title":"Decision tree induction with a constrained number of leaf nodes","volume":"45","author":"Wu","year":"2016","journal-title":"Appl. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.1093\/bioinformatics\/bty140","article-title":"iFeature: A python package and web server for features extraction and selection from protein and peptide sequences","volume":"34","author":"Chen","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"D202","DOI":"10.1093\/nar\/gkm998","article-title":"AAindex: Amino acid index database, progress report 2008","volume":"36","author":"Kawashima","year":"2007","journal-title":"Nucleic Acids Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"23262","DOI":"10.1074\/jbc.M401932200","article-title":"Classification of nuclear receptors based on amino acid composition and dipeptide composition","volume":"279","author":"Bhasin","year":"2004","journal-title":"J. Biol. Chem."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1089\/omi.2015.0095","article-title":"Harnessing computational biology for exact linear b-cell epitope prediction: A novel amino acid composition-based feature descriptor","volume":"19","author":"Saravanan","year":"2015","journal-title":"OMICS J. Integr. Biol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chang, K.Y., and Yang, J.-R. (2013). Analysis and prediction of highly effective antiviral peptides based on random forests. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0070166"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Schaduangrat, N., Nantasenamat, C., Prachayasittikul, V., and Shoombuatong, W. (2019). ACPred: A computational tool for the prediction and analysis of anticancer peptides. Molecules, 24.","DOI":"10.3390\/molecules24101973"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.ymeth.2022.07.017","article-title":"AntiMF: A deep learning framework for predicting anticancer peptides based on multi-view feature extraction","volume":"207","author":"Liu","year":"2022","journal-title":"Methods"},{"key":"ref_46","unstructured":"Pareek, J., and Jacob, J. (2021). Advances in Information Communication Technology and Computing: Proceedings of AICTC 2019, Springer."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Charoenkwan, P., Schaduangrat, N., Moni, M.A., Manavalan, B., and Shoombuatong, W. (2022). SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins. Comput. Biol. Med., 146.","DOI":"10.1016\/j.compbiomed.2022.105704"},{"key":"ref_48","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":"Charoenkwan","year":"2022","journal-title":"Sci. Rep."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/6\/247\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:54:42Z","timestamp":1760108082000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/6\/247"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,6]]},"references-count":48,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["a17060247"],"URL":"https:\/\/doi.org\/10.3390\/a17060247","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,6]]}}}