{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-sequence-based-mesh-classifier-for-the","title":"A Sequence-Based Mesh Classifier for the Prediction of Protein-Protein Interactions","arxiv_id":"1711.04294","date":"2017-11-12","proceeding":null,"authors":["Edgar D. Coelho","Igor N. Cruz","André Santiago","José Luis Oliveira","António Dourado","Joel P. Arrais"],"abstract":"The worldwide surge of multiresistant microbial strains has propelled the\nsearch for alternative treatment options. The study of Protein-Protein\nInteractions (PPIs) has been a cornerstone in the clarification of complex\nphysiological and pathogenic processes, thus being a priority for the\nidentification of vital components and mechanisms in pathogens. Despite the\nadvances of laboratorial techniques, computational models allow the screening\nof protein interactions between entire proteomes in a fast and inexpensive\nmanner. Here, we present a supervised machine learning model for the prediction\nof PPIs based on the protein sequence. We cluster amino acids regarding their\nphysicochemical properties, and use the discrete cosine transform to represent\nprotein sequences. A mesh of classifiers was constructed to create\nhyper-specialised classifiers dedicated to the most relevant pairs of molecular\nfunction annotations from Gene Ontology. Based on an exhaustive evaluation that\nincludes datasets with different configurations, cross-validation and\nout-of-sampling validation, the obtained results outscore the state-of-the-art\nfor sequence-based methods. For the final mesh model using SVM with RBF, a\nconsistent average AUC of 0.84 was attained.","url_abs":"http://arxiv.org/abs/1711.04294v1","url_pdf":"http://arxiv.org/pdf/1711.04294v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-sequence-based-mesh-classifier-for-the","repo_url":"https://github.com/joelarrais/hydra","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"discrete-cosine-transform","method_name":"Discrete Cosine Transform"},{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}