{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T01:54:38Z","timestamp":1778637278612,"version":"3.51.4"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T00:00:00Z","timestamp":1625788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Department of Science and Technology Government of India","award":["ECR\/2017\/000345"],"award-info":[{"award-number":["ECR\/2017\/000345"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In this paper, for accurate prediction of protein\u2013protein interaction (PPI), a novel hybrid classifier is developed by combining the functional-link Siamese neural network (FSNN) with the light gradient boosting machine (LGBM) classifier. The hybrid classifier (FSNN-LGBM) uses the fusion of features derived using pseudo amino acid composition and conjoint triad descriptors. The FSNN extracts the high-level abstraction features from the raw features and LGBM performs the PPI prediction task using these abstraction features. On performing 5-fold cross-validation experiments, the proposed hybrid classifier provides average accuracies of 98.70 and 98.38%, respectively, on the intraspecies PPI data sets of Saccharomyces cerevisiae and Helicobacter pylori. Similarly, the average accuracies for the interspecies PPI data sets of the Human-Bacillus and Human-Yersinia data sets are 98.52 and 97.40%, respectively. Compared with the existing methods, the hybrid classifier achieves higher prediction accuracy on the independent test sets and network data sets. The improved prediction performance obtained by the FSNN-LGBM makes it a flexible and effective PPI prediction model.<\/jats:p>","DOI":"10.1093\/bib\/bbab255","type":"journal-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T03:16:31Z","timestamp":1623986191000},"source":"Crossref","is-referenced-by-count":24,"title":["Improved prediction of protein\u2013protein interaction using a hybrid of functional-link Siamese neural network and gradient boosting machines"],"prefix":"10.1093","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5516-6978","authenticated-orcid":false,"given":"Satyajit","family":"Mahapatra","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication, Birla Institute of Technology Mesra, Ranchi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sitanshu Sekhar","family":"Sahu","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication, Birla Institute of Technology Mesra, Ranchi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,7,9]]},"reference":[{"issue":"4","key":"2021110815081622100_ref1","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1038\/mt.2015.214","article-title":"Modulation of protein\u2013protein interactions for the development of novel therapeutics","volume":"24","author":"Petta","year":"2016","journal-title":"Mol Ther"},{"issue":"1","key":"2021110815081622100_ref2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12033-007-0069-2","article-title":"Computational prediction of protein\u2013protein interactions","volume":"38","author":"Skrabanek","year":"2008","journal-title":"Mol Biotechnol"},{"issue":"1","key":"2021110815081622100_ref3","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1186\/s12859-017-1700-2","article-title":"Sequence-based prediction of protein protein interaction using a deep-learning algorithm","volume":"18","author":"Sun","year":"2017","journal-title":"BMC Bioinformatics"},{"issue":"5","key":"2021110815081622100_ref4","doi-asserted-by":"crossref","first-page":"e0125811","DOI":"10.1371\/journal.pone.0125811","article-title":"Predicting protein-protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest","volume":"10","author":"You","year":"2015","journal-title":"PLoS One"},{"issue":"9","key":"2021110815081622100_ref5","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1093\/nar\/gkn159","article-title":"Using support vector machine combined with auto covariance to predict protein\u2013protein interactions from protein sequences","volume":"36","author":"Guo","year":"2008","journal-title":"Nucleic Acids Res"},{"issue":"11","key":"2021110815081622100_ref6","doi-asserted-by":"crossref","first-page":"4337","DOI":"10.1073\/pnas.0607879104","article-title":"Predicting protein\u2013protein interactions based only on sequences information","volume":"104","author":"Shen","year":"2007","journal-title":"Proc Natl Acad Sci"},{"issue":"S15","key":"2021110815081622100_ref7","doi-asserted-by":"crossref","first-page":"S9","DOI":"10.1186\/1471-2105-15-S15-S9","article-title":"Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set","volume":"15","author":"You","year":"2014","journal-title":"BMC Bioinformatics"},{"issue":"9","key":"2021110815081622100_ref8","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.2174\/092986610791760306","article-title":"Prediction of protein-protein interactions from protein sequence using local descriptors","volume":"17","author":"Yang","year":"2010","journal-title":"Protein Pept Lett"},{"key":"2021110815081622100_ref9","first-page":"713","volume-title":"International Conference on Intelligent Computing","author":"Wong","year":"2015"},{"key":"2021110815081622100_ref10","doi-asserted-by":"publisher","first-page":"114876","DOI":"10.1016\/j.eswa.2021.114876","article-title":"Prediction of protein-protein interactions based on elastic net and deep forest","volume":"176","author":"Yu","year":"2019","journal-title":"Expert Syst Appl"},{"issue":"S8","key":"2021110815081622100_ref11","doi-asserted-by":"crossref","first-page":"S10","DOI":"10.1186\/1471-2105-14-S8-S10","article-title":"Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis","volume":"14","author":"You","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2021110815081622100_ref12","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.chemolab.2019.06.003","article-title":"LightGBM-PPI: predicting protein-protein interactions through LightGBM with multi-information fusion","volume":"191","author":"Chen","year":"2019","journal-title":"Chemom Intel Lab Syst"},{"key":"2021110815081622100_ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.gpb.2021.01.001","article-title":"GTB-PPI: predict protein-protein interactions based on L1-regularized logistic regression and gradient tree boosting","author":"Yu","year":"2021","journal-title":"Genomics, Proteomics Bioinforma"},{"key":"2021110815081622100_ref14","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.neucom.2018.03.062","article-title":"Prediction of protein-protein interactions using an effective sequence based combined method","volume":"303","author":"G\u00f6ktepe","year":"2018","journal-title":"Neurocomputing"},{"issue":"10","key":"2021110815081622100_ref15","doi-asserted-by":"crossref","first-page":"3373","DOI":"10.1007\/s00500-017-2582-y","article-title":"An improved efficient rotation forest algorithm to predict the interactions among proteins","volume":"22","author":"Wang","year":"2018","journal-title":"Soft Computing"},{"issue":"6","key":"2021110815081622100_ref16","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1021\/acs.jcim.7b00028","article-title":"DeepPPI: boosting prediction of protein\u2013protein interactions with deep neural networks","volume":"57","author":"Du","year":"2017","journal-title":"J Chem Inf Model"},{"issue":"17","key":"2021110815081622100_ref17","doi-asserted-by":"crossref","first-page":"i802","DOI":"10.1093\/bioinformatics\/bty573","article-title":"Predicting protein\u2013protein interactions through sequence-based deep learning","volume":"34","author":"Hashemifar","year":"2018","journal-title":"Bioinformatics"},{"key":"2021110815081622100_ref18","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.neucom.2018.02.097","article-title":"Protein\u2013protein interactions prediction based on ensemble deep neural networks","volume":"324","author":"Zhang","year":"2019","journal-title":"Neurocomputing"},{"issue":"6","key":"2021110815081622100_ref19","doi-asserted-by":"crossref","first-page":"551","DOI":"10.2174\/1574893611666160815150746","article-title":"DeepInteract: deep neural network based protein-protein interaction prediction tool","volume":"12","author":"Patel","year":"2017","journal-title":"Current Bioinformatics"},{"issue":"14","key":"2021110815081622100_ref20","doi-asserted-by":"crossref","first-page":"i305","DOI":"10.1093\/bioinformatics\/btz328","article-title":"Multifaceted protein\u2013protein interaction prediction based on Siamese residual RCNN","volume":"35","author":"Chen","year":"2019","journal-title":"Bioinformatics"},{"key":"2021110815081622100_ref21","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.mbs.2019.04.002","article-title":"A novel conjoint triad auto covariance (CTAC) coding method for predicting protein-protein interaction based on amino acid sequence","volume":"313","author":"Wang","year":"2019","journal-title":"Math Biosci"},{"issue":"11","key":"2021110815081622100_ref22","doi-asserted-by":"crossref","first-page":"2373","DOI":"10.3390\/ijms18112373","article-title":"Protein-protein interactions prediction using a novel local conjoint triad descriptor of amino acid sequences","volume":"18","author":"Wang","year":"2017","journal-title":"Int J Mol Sci"},{"key":"2021110815081622100_ref23","doi-asserted-by":"crossref","DOI":"10.7717\/peerj.7126","article-title":"An integration of deep learning with feature embedding for protein-protein interaction prediction","volume":"7","author":"Yao","year":"2019","journal-title":"PeerJ"},{"issue":"1","key":"2021110815081622100_ref24","first-page":"1","article-title":"Predicting protein-protein interactions from matrix-based protein sequence using convolution neural network and feature-selective rotation forest","volume":"9","author":"Wang","year":"2019","journal-title":"Sci Rep"},{"key":"2021110815081622100_ref25","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.compbiolchem.2018.12.001","article-title":"A new sequence based encoding for prediction of host\u2013pathogen protein interactions","volume":"78","author":"K\u00f6sesoy","year":"2019","journal-title":"Comput Biol Chem"},{"issue":"11","key":"2021110815081622100_ref26","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0112034","article-title":"Prediction of interactions between viral and host proteins using supervised machine learning methods","volume":"9","author":"Barman","year":"2014","journal-title":"PLoS One"},{"issue":"6","key":"2021110815081622100_ref27","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1186\/s12864-018-4924-2","article-title":"A generalized approach to predicting protein-protein interactions between virus and host","volume":"19","author":"Zhou","year":"2018","journal-title":"BMC Genomics"},{"key":"2021110815081622100_ref28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/SCEECS48394.2020.150","volume-title":"2020 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)","author":"Mahapatra","year":"2020"},{"issue":"3","key":"2021110815081622100_ref29","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa068","article-title":"Systematic evaluation of machine learning methods for identifying human-pathogen protein-protein interactions","volume":"22","author":"Chen","year":"2021","journal-title":"Brief Bioinform"},{"key":"2021110815081622100_ref30","first-page":"669","volume-title":"International Journal of Pattern Recognition and Artificial Intelligence","author":"Bromley","year":"1993"},{"key":"2021110815081622100_ref31","volume-title":"Adaptive Pattern Recognition and Neural Networks","author":"Pao","year":"1989"},{"issue":"3","key":"2021110815081622100_ref32","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1109\/TII.2019.2920831","article-title":"Intelligent secure ecosystem based on metaheuristic and functional link neural network for edge of things","volume":"16","author":"Naik","year":"2019","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2021110815081622100_ref33","doi-asserted-by":"crossref","first-page":"17804","DOI":"10.1109\/ACCESS.2019.2960161","article-title":"A new hybrid convolutional neural network and eXtreme gradient boosting classifier for recognizing handwritten Ethiopian characters","volume":"8","author":"Weldegebriel","year":"2019","journal-title":"IEEE Access"},{"key":"2021110815081622100_ref34","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/JSTARS.2019.2953234","article-title":"Very high resolution remote sensing imagery classification using a fusion of random forest and deep learning technique\u2014subtropical area for example","volume":"13","author":"Dong","year":"2019","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"issue":"5","key":"2021110815081622100_ref35","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1093\/bib\/bbz098","article-title":"DeepSVM-fold: protein fold recognition by combining support vector machines and pairwise sequence similarity scores generated by deep learning networks","volume":"21","author":"Liu","year":"2020","journal-title":"Brief Bioinform"},{"issue":"7","key":"2021110815081622100_ref36","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1109\/TASL.2013.2250961","article-title":"Towards scaling up classification-based speech separation","volume":"21","author":"Wang","year":"2013","journal-title":"IEEE Trans Audio Speech Lang Process"},{"key":"2021110815081622100_ref37","first-page":"3149","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17)","author":"Ke","year":"2017"},{"issue":"6","key":"2021110815081622100_ref38","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1093\/bib\/bby079","article-title":"Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework","volume":"20","author":"Zhang","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021110815081622100_ref39","doi-asserted-by":"crossref","first-page":"49456","DOI":"10.1109\/ACCESS.2019.2907132","article-title":"Improved prediction of protein-protein interactions using descriptors derived from PSSM via gray level co-occurrence matrix","volume":"7","author":"Zhu","year":"2019","journal-title":"IEEE Access"},{"issue":"1","key":"2021110815081622100_ref40","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1186\/s12859-016-1035-4","article-title":"Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding","volume":"17","author":"Huang","year":"2016","journal-title":"BMC Bioinformatics"},{"issue":"D1","key":"2021110815081622100_ref41","doi-asserted-by":"crossref","first-page":"D358","DOI":"10.1093\/nar\/gkt1115","article-title":"The MIntAct project\u2014IntAct as a common curation platform for 11 molecular interaction databases","volume":"42","author":"Orchard","year":"2014","journal-title":"Nucleic Acids Res"},{"issue":"Nov","key":"2021110815081622100_ref42","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"Journal of Machine Learning Research"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/22\/6\/bbab255\/41088398\/bbab255.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/22\/6\/bbab255\/41088398\/bbab255.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T15:22:51Z","timestamp":1636384971000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab255\/6318175"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,9]]},"references-count":42,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,11,5]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab255","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,11]]},"published":{"date-parts":[[2021,7,9]]},"article-number":"bbab255"}}