Collective traffic forecasting
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Ranking Beta Sheet Topologies with Applications to Protein Structure Prediction
Journal of Mathematical Modelling and Algorithms
| Hi-index | 3.84 |
Motivation: Accurate prediction of contacts between β-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 β-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% F1 measure, with 47.3% precision and 42.7% recall, which is significantly better (P F1 ≥ 70% on coarse map). It also outperforms more general contact predictors on recent CASP 2008 targets. Contact: lippi@dsi.unifi.it Supplementary information: Supplementary data are available at Bioinformatics online.