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Detailed knowledge about which domains and residues are involved in a given interaction has extensive applications to biology, including better understanding of the binding process and more efficient drug\/enzyme design. Alas, most current interaction prediction methods do not identify which parts of a protein actually instantiate an interaction. Furthermore, they also fail to leverage the hierarchical nature of the problem, ignoring otherwise useful information available at the lower levels; when they do, they do not generate predictions that are guaranteed to be consistent between levels.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Inspired by earlier ideas of Yip <jats:italic>et al.<\/jats:italic> (BMC Bioinformatics 10:241, 2009), in the present paper we view the problem as a multi-level learning task, with one task per level (proteins, domains and residues), and propose a machine learning method that collectively infers the binding state of all object pairs. Our method is based on Semantic Based Regularization (SBR), a flexible and theoretically sound machine learning framework that uses First Order Logic constraints to tie the learning tasks together. We introduce a set of biologically motivated rules that enforce consistent predictions between the hierarchy levels.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>We study the empirical performance of our method using a standard validation procedure, and compare its performance against the only other existing multi-level prediction technique. We present results showing that our method substantially outperforms the competitor in several experimental settings, indicating that exploiting the hierarchical nature of the problem can lead to better predictions. In addition, our method is also guaranteed to produce interactions that are consistent with respect to the protein\u2013domain\u2013residue hierarchy.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-15-103","type":"journal-article","created":{"date-parts":[[2014,4,12]],"date-time":"2014-04-12T01:02:00Z","timestamp":1397264520000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Improved multi-level protein\u2013protein interaction prediction with semantic-based regularization"],"prefix":"10.1186","volume":"15","author":[{"given":"Claudio","family":"Sacc\u00e0","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefano","family":"Teso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michelangelo","family":"Diligenti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Passerini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2014,4,12]]},"reference":[{"issue":"4","key":"6365_CR1","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1021\/cr040409x","volume":"108","author":"O Keskin","year":"2008","unstructured":"Keskin O, Gursoy A, Ma B, Nussinov R: Principles of protein-protein interactions: what are the preferred ways for proteins to interact?. 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