{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T04:20:25Z","timestamp":1774153225866,"version":"3.50.1"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2018,9,9]],"date-time":"2018-09-09T00:00:00Z","timestamp":1536451200000},"content-version":"vor","delay-in-days":8,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01GM089753"],"award-info":[{"award-number":["R01GM089753"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000143","name":"CCF","doi-asserted-by":"publisher","award":["AF-1618648"],"award-info":[{"award-number":["AF-1618648"]}],"id":[{"id":"10.13039\/100000143","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NVIDIA Inc."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>High-throughput experimental techniques have produced a large amount of protein\u2013protein interaction (PPI) data, but their coverage is still low and the PPI data is also very noisy. Computational prediction of PPIs can be used to discover new PPIs and identify errors in the experimental PPI data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We present a novel deep learning framework, DPPI, to model and predict PPIs from sequence information alone. Our model efficiently applies a deep, Siamese-like convolutional neural network combined with random projection and data augmentation to predict PPIs, leveraging existing high-quality experimental PPI data and evolutionary information of a protein pair under prediction. Our experimental results show that DPPI outperforms the state-of-the-art methods on several benchmarks in terms of area under precision-recall curve (auPR), and computationally is more efficient. We also show that DPPI is able to predict homodimeric interactions where other methods fail to work accurately, and the effectiveness of DPPI in specific applications such as predicting cytokine-receptor binding affinities.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Predicting protein-protein interactions through sequence-based deep learning): https:\/\/github.com\/hashemifar\/DPPI\/.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty573","type":"journal-article","created":{"date-parts":[[2018,7,6]],"date-time":"2018-07-06T01:20:56Z","timestamp":1530840056000},"page":"i802-i810","source":"Crossref","is-referenced-by-count":341,"title":["Predicting protein\u2013protein interactions through sequence-based deep learning"],"prefix":"10.1093","volume":"34","author":[{"given":"Somaye","family":"Hashemifar","sequence":"first","affiliation":[{"name":"Toyota Technological Institute at Chicago, Chicago, IL, USA"}]},{"given":"Behnam","family":"Neyshabur","sequence":"additional","affiliation":[{"name":"Toyota Technological Institute at Chicago, Chicago, IL, USA"}]},{"given":"Aly A","family":"Khan","sequence":"additional","affiliation":[{"name":"Toyota Technological Institute at Chicago, Chicago, IL, USA"}]},{"given":"Jinbo","family":"Xu","sequence":"additional","affiliation":[{"name":"Toyota Technological Institute at Chicago, Chicago, IL, USA"}]}],"member":"286","published-online":{"date-parts":[[2018,9,8]]},"reference":[{"key":"2023061402421970400_bty573-B1","doi-asserted-by":"crossref","first-page":"i38","DOI":"10.1093\/bioinformatics\/bti1016","article-title":"Kernel methods for predicting protein\u2013protein interactions","volume":"21","author":"Ben-Hur","year":"2005","journal-title":"Bioinformatics"},{"key":"2023061402421970400_bty573-B2","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/978-3-642-35289-8_26","volume-title":"Neural Networks: Tricks of the Trade","author":"Bengio","year":"2012"},{"key":"2023061402421970400_bty573-B3","first-page":"669","article-title":"Signature Verification Using A \u201cSiamese\u201d Time Delay Neural network","volume":"07","author":"Bromley","year":"1993","journal-title":"IJPRAI"},{"key":"2023061402421970400_bty573-B4","article-title":"Recurrent batch normalization","author":"Cooijmans","year":"2016","journal-title":"arXiv Preprint arXiv"},{"key":"2023061402421970400_bty573-B5","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1186\/1752-0509-6-92","article-title":"HINT: high-quality protein interactomes and their applications in understanding human disease","volume":"6","author":"Das","year":"2012","journal-title":"BMC Syst. Biol."},{"key":"2023061402421970400_bty573-B6","first-page":"89","article-title":"A model of evolutionary change in proteins","volume":"5","author":"Dayhoff","year":"1972","journal-title":"Atlas Protein Sequence Struct."},{"key":"2023061402421970400_bty573-B7","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."},{"key":"2023061402421970400_bty573-B8","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.1093\/bioinformatics\/btg352","article-title":"Learning to predict protein\u2013protein interactions from protein sequences","volume":"19","author":"Gomez","year":"2003","journal-title":"Bioinformatics"},{"key":"2023061402421970400_bty573-B9","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":"2023061402421970400_bty573-B10","doi-asserted-by":"crossref","first-page":"1945","DOI":"10.1093\/bioinformatics\/btv077","article-title":"Evolutionary profiles improve protein\u2013protein interaction prediction from sequence","volume":"31","author":"Hamp","year":"2015","journal-title":"Bioinformatics"},{"key":"2023061402421970400_bty573-B11","doi-asserted-by":"crossref","first-page":"e03430","DOI":"10.7554\/eLife.03430","article-title":"Sequence co-evolution gives 3D contacts and structures of protein complexes","volume":"3","author":"Hopf","year":"2014","journal-title":"Elife"},{"key":"2023061402421970400_bty573-B12","first-page":"1","article-title":"Using weighted sparse representation model combined with discrete cosine transformation to predict protein\u2013protein interactions from protein sequence","volume":"2015","author":"Huang","year":"2015","journal-title":"BioMed Res. Int."},{"key":"2023061402421970400_bty573-B13","first-page":"448","article-title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","volume-title":"International Conference on Machine Learning","author":"Ioffe","year":"2015"},{"key":"2023061402421970400_bty573-B14","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1073\/pnas.93.1.13","article-title":"Principles of protein\u2013protein interactions","volume":"93","author":"Jones","year":"1996","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023061402421970400_bty573-B15","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1142\/S021972000500120X","article-title":"Profile-based string kernels for remote homology detection and motif extraction","volume":"03","author":"Kuang","year":"2005","journal-title":"J. Bioinf. Comput. Biol."},{"key":"2023061402421970400_bty573-B16","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature10975","article-title":"Exploiting a natural conformational switch to engineer an interleukin-2\/superkine\/\u2019","volume":"484","author":"Levin","year":"2012","journal-title":"Nature"},{"key":"2023061402421970400_bty573-B17","article-title":"A scored human protein\u2013protein interaction network to catalyze genomic interpretation","author":"Li","year":"2016","journal-title":"Nat. Methods"},{"key":"2023061402421970400_bty573-B18","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1093\/bioinformatics\/bth483","article-title":"Predicting protein\u2013protein interactions using signature products","volume":"21","author":"Martin","year":"2005","journal-title":"Bioinformatics"},{"key":"2023061402421970400_bty573-B19","doi-asserted-by":"crossref","first-page":"ra114","DOI":"10.1126\/scisignal.aab2677","article-title":"Instructive roles for cytokine-receptor binding parameters in determining signaling and functional potency","volume":"8","author":"Moraga","year":"2015","journal-title":"Sci. Signal."},{"key":"2023061402421970400_bty573-B20","doi-asserted-by":"crossref","first-page":"e02030","DOI":"10.7554\/eLife.02030","article-title":"Robust and accurate prediction of residue\u2013residue interactions across protein interfaces using evolutionary information","volume":"3","author":"Ovchinnikov","year":"2014","journal-title":"Elife"},{"key":"2023061402421970400_bty573-B21","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1038\/srep00239","article-title":"Short co-occurring polypeptide regions can predict global protein interaction maps","volume":"2","author":"Pitre","year":"2012","journal-title":"Sci. Rep."},{"key":"2023061402421970400_bty573-B22","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":"2023061402421970400_bty573-B23","doi-asserted-by":"crossref","first-page":"e31826","DOI":"10.1371\/journal.pone.0031826","article-title":"HIPPIE: integrating protein interaction networks with experiment based quality scores","volume":"7","author":"Schaefer","year":"2012","journal-title":"PloS One"},{"key":"2023061402421970400_bty573-B24","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/S0959-440X(00)00065-8","article-title":"Electrostatic aspects of protein\u2013protein interactions","volume":"10","author":"Sheinerman","year":"2000","journal-title":"Curr. Opin. Struct. Biol."},{"key":"2023061402421970400_bty573-B25","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. USA"},{"key":"2023061402421970400_bty573-B26","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/0893-6080(91)90033-2","article-title":"Creating artificial neural networks that generalize","volume":"4","author":"Sietsma","year":"1991","journal-title":"Neural Netw."},{"key":"2023061402421970400_bty573-B27","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"},{"key":"2023061402421970400_bty573-B28","first-page":"1139","article-title":"On the importance of initialization and momentum in deep learning","volume-title":"International Conference on Machine Learning","author":"Sutskever","year":"2013"},{"key":"2023061402421970400_bty573-B29","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1145\/1390156.1390294","article-title":"Extracting and composing robust features with denoising autoencoders","volume-title":"Proceedings of the 25th International Conference on Machine Learning","author":"Vincent","year":"2008"},{"key":"2023061402421970400_bty573-B30","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1126\/science.287.5450.116","article-title":"Protein interaction mapping in C. elegans using proteins involved in vulval development","volume":"287","author":"Walhout","year":"2000","journal-title":"Science"},{"key":"2023061402421970400_bty573-B31","first-page":"713","article-title":"Detection of protein\u2013protein interactions from amino acid sequences using a rotation forest model with a novel pr-lpq descriptor","volume-title":"International Conference on Intelligent Computing","author":"Wong","year":"2015"},{"key":"2023061402421970400_bty573-B32","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.2174\/092986610791760306","article-title":"Prediction of protein\u2013protein interactions from protein sequence using local descriptors","volume":"17","author":"Yang","year":"2010","journal-title":"Protein Peptide Lett."},{"key":"2023061402421970400_bty573-B33","doi-asserted-by":"crossref","first-page":"S10","DOI":"10.1186\/1471-2105-14-S8-S10","article-title":"Prediction of protein\u2013protein 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":"2023061402421970400_bty573-B34","doi-asserted-by":"crossref","first-page":"S9","DOI":"10.1186\/1471-2105-15-S15-S9","article-title":"Prediction of protein\u2013protein 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"},{"key":"2023061402421970400_bty573-B35","first-page":"818","article-title":"Visualizing and understanding convolutional networks","volume-title":"European Conference on Computer Vision","author":"Zeiler","year":"2014"},{"key":"2023061402421970400_bty573-B36","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1007\/978-3-642-22456-0_37","article-title":"Prediction of protein\u2013protein interactions using local description of amino acid sequence","volume-title":"Advances in Computer Science and Education Applications","author":"Zhou","year":"2011"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/17\/i802\/50582268\/bioinformatics_34_17_i802.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/17\/i802\/50582268\/bioinformatics_34_17_i802.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T02:43:37Z","timestamp":1686710617000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/34\/17\/i802\/5093239"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,1]]},"references-count":36,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2018,9,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bty573","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2018,9,1]]},"published":{"date-parts":[[2018,9,1]]}}}