%0 Journal Article %1 lippi2009prediction %A Lippi, M %A Frasconi, P %D 2009 %J Bioinformatics %K ai bioinformatics ilp intelligenzaartificiale relazione2010 %N 18 %P 2326-2333 %R 10.1093/bioinformatics/btp421 %T Prediction of protein beta-residue contacts by Markov logic networks with grounding-specific weights %U http://www.ncbi.nlm.nih.gov/pubmed/19592394 %V 25 %X MOTIVATION: Accurate prediction of contacts between beta-strand residues can significantly contribute towards ab initio prediction of the 3D structure of many proteins. Contacts in the same protein are highly interdependent. Therefore, significant improvements can be expected by applying statistical relational learners that overcome the usual machine learning assumption that examples are independent and identically distributed. Furthermore, the dependencies among beta-residue contacts are subject to strong regularities, many of which are known a priori. In this article, we take advantage of Markov logic, a statistical relational learning framework that is able to capture dependencies between contacts, and constrain the solution according to domain knowledge expressed by means of weighted rules in a logical language. RESULTS: We introduce a novel hybrid architecture based on neural and Markov logic networks with grounding-specific weights. On a non-redundant dataset, our method achieves 44.9% F(1) measure, with 47.3% precision and 42.7% recall, which is significantly better (P or= 70% on coarse map). It also outperforms more general contact predictors on recent CASP 2008 targets.

@article{lippi2009prediction, abstract = {MOTIVATION: Accurate prediction of contacts between beta-strand residues can significantly contribute towards ab initio prediction of the 3D structure of many proteins. Contacts in the same protein are highly interdependent. Therefore, significant improvements can be expected by applying statistical relational learners that overcome the usual machine learning assumption that examples are independent and identically distributed. Furthermore, the dependencies among beta-residue contacts are subject to strong regularities, many of which are known a priori. In this article, we take advantage of Markov logic, a statistical relational learning framework that is able to capture dependencies between contacts, and constrain the solution according to domain knowledge expressed by means of weighted rules in a logical language. RESULTS: We introduce a novel hybrid architecture based on neural and Markov logic networks with grounding-specific weights. On a non-redundant dataset, our method achieves 44.9% F(1) measure, with 47.3% precision and 42.7% recall, which is significantly better (P or= 70% on coarse map). It also outperforms more general contact predictors on recent CASP 2008 targets.}, added-at = {2010-12-16T15:30:06.000+0100}, author = {Lippi, M and Frasconi, P}, biburl = {https://www.bibsonomy.org/bibtex/2b3c0f91cc8853d44968f4709b87a6bf7/naufraghi}, description = {Prediction of protein beta-residue contacts by Mar... [Bioinformatics. 2009] - PubMed result}, doi = {10.1093/bioinformatics/btp421}, interhash = {4ada7aca18d5d5b9f4972d4654768cca}, intrahash = {b3c0f91cc8853d44968f4709b87a6bf7}, journal = {Bioinformatics}, keywords = {ai bioinformatics ilp intelligenzaartificiale relazione2010}, month = sep, number = 18, pages = {2326-2333}, pmid = {19592394}, timestamp = {2010-12-16T15:30:06.000+0100}, title = {Prediction of protein beta-residue contacts by Markov logic networks with grounding-specific weights}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19592394}, volume = 25, year = 2009 }