{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T13:17:51Z","timestamp":1783948671777,"version":"3.55.0"},"reference-count":46,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T00:00:00Z","timestamp":1652918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC Program","doi-asserted-by":"publisher","award":["62072378"],"award-info":[{"award-number":["62072378"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC Program","doi-asserted-by":"publisher","award":["61873212"],"award-info":[{"award-number":["61873212"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC Program","doi-asserted-by":"publisher","award":["32170114"],"award-info":[{"award-number":["32170114"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC Program","doi-asserted-by":"publisher","award":["62002297"],"award-info":[{"award-number":["62002297"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC Program","doi-asserted-by":"publisher","award":["2022JQ-700"],"award-info":[{"award-number":["2022JQ-700"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC Program","doi-asserted-by":"publisher","award":["2018AAA0100103"],"award-info":[{"award-number":["2018AAA0100103"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["62072378"],"award-info":[{"award-number":["62072378"]}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["61873212"],"award-info":[{"award-number":["61873212"]}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["32170114"],"award-info":[{"award-number":["32170114"]}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["62002297"],"award-info":[{"award-number":["62002297"]}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["2022JQ-700"],"award-info":[{"award-number":["2022JQ-700"]}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["2018AAA0100103"],"award-info":[{"award-number":["2018AAA0100103"]}]},{"name":"Science and Technology Innovation 2030-New Generation Artificial Intelligence Major Project","award":["62072378"],"award-info":[{"award-number":["62072378"]}]},{"name":"Science and Technology Innovation 2030-New Generation Artificial Intelligence Major Project","award":["61873212"],"award-info":[{"award-number":["61873212"]}]},{"name":"Science and Technology Innovation 2030-New Generation Artificial Intelligence Major Project","award":["32170114"],"award-info":[{"award-number":["32170114"]}]},{"name":"Science and Technology Innovation 2030-New Generation Artificial Intelligence Major Project","award":["62002297"],"award-info":[{"award-number":["62002297"]}]},{"name":"Science and Technology Innovation 2030-New Generation Artificial Intelligence Major Project","award":["2022JQ-700"],"award-info":[{"award-number":["2022JQ-700"]}]},{"name":"Science and Technology Innovation 2030-New Generation Artificial Intelligence Major Project","award":["2018AAA0100103"],"award-info":[{"award-number":["2018AAA0100103"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biology"],"abstract":"<jats:p>Protein\u2013protein interactions (PPIs) are crucial for understanding the cellular processes, including signal cascade, DNA transcription, metabolic cycles, and repair. In the past decade, a multitude of high-throughput methods have been introduced to detect PPIs. However, these techniques are time-consuming, laborious, and always suffer from high false negative rates. Therefore, there is a great need of new computational methods as a supplemental tool for PPIs prediction. In this article, we present a novel sequence-based model to predict PPIs that combines Discrete Hilbert transform (DHT) and Rotation Forest (RoF). This method contains three stages: firstly, the Position-Specific Scoring Matrices (PSSM) was adopted to transform the amino acid sequence into a PSSM matrix, which can contain rich information about protein evolution. Then, the 400-dimensional DHT descriptor was constructed for each protein pair. Finally, these feature descriptors were fed to the RoF classifier for identifying the potential PPI class. When exploring the proposed model on the Yeast, Human, and Oryza sativa PPIs datasets, it yielded excellent prediction accuracies of 91.93, 96.35, and 94.24%, respectively. In addition, we also conducted numerous experiments on cross-species PPIs datasets, and the predictive capacity of our method is also very excellent. To further access the prediction ability of the proposed approach, we present the comparison of RoF with four powerful classifiers, including Support Vector Machine (SVM), Random Forest (RF), K-nearest Neighbor (KNN), and AdaBoost. We also compared it with some existing superiority works. These comprehensive experimental results further confirm the excellent and feasibility of the proposed approach. In future work, we hope it can be a supplemental tool for the proteomics analysis.<\/jats:p>","DOI":"10.3390\/biology11050775","type":"journal-article","created":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T12:25:57Z","timestamp":1652963157000},"page":"775","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Novel Ensemble Learning-Based Computational Method to Predict Protein-Protein Interactions from Protein Primary Sequences"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4993-298X","authenticated-orcid":false,"given":"Jie","family":"Pan","sequence":"first","affiliation":[{"name":"Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, College of Life Science, Northwest University, Xi\u2019an 710069, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8890-9080","authenticated-orcid":false,"given":"Shiwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, College of Life Science, Northwest University, Xi\u2019an 710069, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changqing","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Xijing University, Xi\u2019an 710123, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Xijing University, Xi\u2019an 710123, China"},{"name":"College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1266-2696","authenticated-orcid":false,"given":"Zhuhong","family":"You","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanmei","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, College of Life Science, Northwest University, Xi\u2019an 710069, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1039\/C8NP00038G","article-title":"The many faces and important roles of protein\u2013protein interactions during non-ribosomal peptide synthesis","volume":"35","author":"Cryle","year":"2018","journal-title":"Nat. Prod. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yakubu, R.R., Nieves, E., and Weiss, L.M. (2019). The methods employed in mass spectrometric analysis of posttranslational modifications (PTMs) and protein\u2013protein interactions (PPIs). Advancements of Mass Spectrometry in Biomedical Research, Springer.","DOI":"10.1007\/978-3-030-15950-4_10"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Carnes, R.M., Kesterson, R.A., Korf, B.R., Mobley, J.A., and Wallis, D.J.G. (2019). Affinity purification of NF1 protein\u2013protein interactors identifies keratins and neurofibromin itself as binding partners. Genes, 10.","DOI":"10.3390\/genes10090650"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100023","DOI":"10.1074\/jbc.RA120.014284","article-title":"DoMY-Seq: A yeast two-hybrid\u2013based technique for precision mapping of protein\u2013protein interaction motifs","volume":"296","author":"Castel","year":"2021","journal-title":"J. Biol. Chem."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pan, J., You, Z.-H., Yu, C.-Q., Li, L.-P., and Zhan, X.-K. (2020, January 2\u20135). Predicting Protein-Protein Interactions from Protein Sequence Information Using Dual-Tree Complex Wavelet Transform. Proceedings of the International Conference on Intelligent Computing, Bari, Italy.","DOI":"10.1007\/978-3-030-60802-6_13"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"12874","DOI":"10.1038\/s41598-018-30694-1","article-title":"Using two-dimensional principal component analysis and rotation forest for prediction of protein-protein interactions","volume":"8","author":"Wang","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1002\/jcc.25780","article-title":"PPI-detect: A support vector machine model for sequence-based prediction of protein\u2013protein interactions","volume":"40","author":"Harms","year":"2019","journal-title":"J. Comput. Chem."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"12473","DOI":"10.1109\/ACCESS.2021.3051006","article-title":"Determining Protein\u2013Protein Interaction Using Support Vector Machine: A Review","volume":"9","author":"Chakraborty","year":"2021","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1002\/pmic.201200326","article-title":"Heterogeneous data integration by tree-augmented na\u00efve B ayes for protein\u2013Protein interactions prediction","volume":"13","author":"Lin","year":"2013","journal-title":"Proteomics"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"902198","DOI":"10.1155\/2015\/902198","article-title":"Using weighted sparse representation model combined with discrete cosine transformation to predict protein-protein interactions from protein sequence","volume":"2015","author":"Huang","year":"2015","journal-title":"BioMed Res. Int."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"You, Z.-H., Lei, Y.-K., Zhu, L., Xia, J., and Wang, B. (2013). Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. BMC Bioinform., 14.","DOI":"10.1186\/1471-2105-14-S8-S10"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1109\/TCBB.2017.2649529","article-title":"PSPEL: In silico prediction of self-interacting proteins from amino acids sequences using ensemble learning","volume":"14","author":"Li","year":"2017","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1093\/bioinformatics\/btz699","article-title":"Protein\u2013protein interaction site prediction through combining local and global features with deep neural networks","volume":"36","author":"Zeng","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, Z.-H., You, Z.-H., Li, L.-P., Wang, Y.-B., Wong, L., and Yi, H.-C. (2019). Prediction of self-interacting proteins from protein sequence information based on random projection model and fast Fourier transform. Int. J. Mol. Sci., 20.","DOI":"10.3390\/ijms20040930"},{"key":"ref_15","first-page":"1607946","article-title":"FWHT-RF: A Novel Computational Approach to Predict Plant Protein-Protein Interactions via an Ensemble Learning Method","volume":"2021","author":"Pan","year":"2021","journal-title":"Sci. Program."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sun, T., Zhou, B., Lai, L., and Pei, J. (2017). Sequence-based prediction of protein protein interaction using a deep-learning algorithm. BMC Bioinform., 18.","DOI":"10.1186\/s12859-017-1700-2"},{"key":"ref_17","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"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e7126","DOI":"10.7717\/peerj.7126","article-title":"An integration of deep learning with feature embedding for protein\u2013protein interaction prediction","volume":"7","author":"Yao","year":"2019","journal-title":"PeerJ"},{"key":"ref_19","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":"ref_20","unstructured":"Richoux, F., Servantie, C., Bor\u00e8s, C., and T\u00e9letch\u00e9a, S.J.A.P.A. (2019). Comparing two deep learning sequence-based models for protein-protein interaction prediction. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"807522","DOI":"10.3389\/fbioe.2022.807522","article-title":"DWPPI: A Deep Learning Approach for Predicting Protein\u2013Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network","volume":"10","author":"Pan","year":"2022","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"D449","DOI":"10.1093\/nar\/gkh086","article-title":"The database of interacting proteins: 2004 update","volume":"32","author":"Salwinski","year":"2004","journal-title":"Nucleic Acids Res."},{"key":"ref_23","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."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1093\/bioinformatics\/17.3.282","article-title":"Clustering of highly homologous sequences to reduce the size of large protein databases","volume":"17","author":"Li","year":"2001","journal-title":"Bioinformatics"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1093\/bioinformatics\/btl158","article-title":"Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences","volume":"22","author":"Li","year":"2006","journal-title":"Bioinformatics"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"D767","DOI":"10.1093\/nar\/gkn892","article-title":"Human protein reference database\u20142009 update","volume":"37","author":"Goel","year":"2009","journal-title":"Nucleic Acids Res."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gu, H., Zhu, P., Jiao, Y., Meng, Y., and Chen, M. (2011). PRIN: A predicted rice interactome network. BMC Bioinform., 12.","DOI":"10.1186\/1471-2105-12-161"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4355","DOI":"10.1073\/pnas.84.13.4355","article-title":"Profile analysis: Detection of distantly related proteins","volume":"84","author":"Gribskov","year":"1987","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, Y., Cheng, J., Liu, Y., and Chen, Y. (2016, January 20\u201322). Prediction of protein secondary structure using support vector machine with PSSM profiles. Proceedings of the 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, Chongqing, China.","DOI":"10.1109\/ITNEC.2016.7560411"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"414327","DOI":"10.1155\/2013\/414327","article-title":"A novel method of predicting protein disordered regions based on sequence features","volume":"2013","author":"Zhao","year":"2013","journal-title":"BioMed Res. Int."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1089\/cmb.1995.2.87","article-title":"Prediction of function in DNA sequence analysis","volume":"2","author":"Gelfand","year":"1995","journal-title":"J. Comput. Biol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1109\/TAU.1970.1162139","article-title":"Electroacoustics. Discrete hilbert transform","volume":"18","author":"Cizek","year":"1970","journal-title":"IEEE Trans. Audio Electroacoust."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ponomareva, O., Ponomarev, A., and Ponomarev, V. (2018, January 14\u201317). Evolution of forward and inverse discrete fourier transform. Proceedings of the 2018 IEEE East-West Design & Test Symposium (EWDTS), Kazan, Russia.","DOI":"10.1109\/EWDTS.2018.8524820"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/TGE.1973.294304","article-title":"The stabilization of two-dimensional recursive filters via the discrete Hilbert transform","volume":"11","author":"Read","year":"1973","journal-title":"IEEE Trans. Geosci. Electron."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1109\/TPAMI.2006.211","article-title":"Rotation forest: A new classifier ensemble method","volume":"28","author":"Rodriguez","year":"2006","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/TSM.2010.2041263","article-title":"Introducing a unified PCA algorithm for model size reduction","volume":"23","author":"Good","year":"2010","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ding, Y., Tang, J., and Guo, F. (2016). Predicting protein-protein interactions via multivariate mutual information of protein sequences. BMC Bioinform., 17.","DOI":"10.1186\/s12859-016-1253-9"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1176934320934498","DOI":"10.1177\/1176934320934498","article-title":"Using Random Forest Model Combined with Gabor Feature to Predict Protein-Protein Interaction From Protein Sequence","volume":"16","author":"Zhan","year":"2020","journal-title":"Evol. Bioinform."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, Y.-B., You, Z.-H., Li, L.-P., Huang, Y.-A., and Yi, H.-C. (2017). Detection of interactions between proteins by using legendre moments descriptor to extract discriminatory information embedded in pssm. Molecules, 22.","DOI":"10.3390\/molecules22081366"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.csbj.2019.12.005","article-title":"Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method","volume":"18","author":"Yang","year":"2020","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1093\/bioinformatics\/bty995","article-title":"Protein\u2013protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique","volume":"35","author":"Wang","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhou, Y.Z., Gao, Y., and Zheng, Y.Y. (2011). Prediction of protein-protein interactions using local description of amino acid sequence. Advances in Computer Science and Education Applications, Springer.","DOI":"10.1007\/978-3-642-22456-0_37"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"An, J.-Y., Zhou, Y., Zhao, Y.-J., and Yan, Z.-J. (2019). An efficient feature extraction technique based on local coding PSSM and multifeatures fusion for predicting protein-protein interactions. Evol. Bioinform., 15.","DOI":"10.1177\/1176934319879920"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"16910","DOI":"10.1038\/s41598-021-96265-z","article-title":"Robust and accurate prediction of protein\u2013protein interactions by exploiting evolutionary information","volume":"11","author":"Li","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4992","DOI":"10.1021\/pr100618t","article-title":"Large-Scale prediction of human protein\u2212 protein interactions from amino acid sequence based on latent topic features","volume":"9","author":"Pan","year":"2010","journal-title":"J. Proteome Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"23638","DOI":"10.18632\/oncotarget.15564","article-title":"Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier","volume":"8","author":"Li","year":"2017","journal-title":"Oncotarget"}],"container-title":["Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-7737\/11\/5\/775\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:14:54Z","timestamp":1760138094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-7737\/11\/5\/775"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,19]]},"references-count":46,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["biology11050775"],"URL":"https:\/\/doi.org\/10.3390\/biology11050775","relation":{},"ISSN":["2079-7737"],"issn-type":[{"value":"2079-7737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,19]]}}}