10 Result(s)
within
Andrea Passerini
Computational Biology/Bioinformatics
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Article
Open AccessCombining learning and constraints for genome-wide protein annotation
The advent of high-throughput experimental techniques paved the way to genome-wide computational analysis and predictive annotation studies. When considering the joint annotation of a large set of related enti...
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Article
Open AccessPredicting virus mutations through statistical relational learning
Viruses are typically characterized by high mutation rates, which allow them to quickly develop drug-resistant mutations. Mining relevant rules from mutation data can be extremely useful to understand the viru...
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Article
Open AccessImproved multi-level protein–protein interaction prediction with semantic-based regularization
Protein–protein interactions can be seen as a hierarchical process occurring at three related levels: proteins bind by means of specific domains, which in turn form interfaces through patches of residues. Detaile...
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Article
Open AccessJoint probabilistic-logical refinement of multiple protein feature predictors
Computational methods for the prediction of protein features from sequence are a long-standing focus of bioinformatics. A key observation is that several protein features are closely inter-related, that is, th...
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Article
Open AccessAutomatic prediction of catalytic residues by modeling residue structural neighborhood
Prediction of catalytic residues is a major step in characterizing the function of enzymes. In its simpler formulation, the problem can be cast into a binary classification task at the residue level, by predic...
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Chapter and Conference Paper
An On/Off Lattice Approach to Protein Structure Prediction from Contact Maps
An important unsolved problem in structural bioinformatics is that of protein structure prediction (PSP), the reconstruction of a biologically plausible three-dimensional structure for a given protein given on...
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Article
Open AccessA simplified approach to disulfide connectivity prediction from protein sequences
Prediction of disulfide bridges from protein sequences is useful for characterizing structural and functional properties of proteins. Several methods based on different machine learning algorithms have been ap...
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Chapter
Learning with Kernels and Logical Representations
In this chapter, we describe a view of statistical learning in the inductive logic programming setting based on kernel methods. The relational representation of data and background knowledge are used to form a...
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Chapter and Conference Paper
On the Convergence of Protein Structure and Dynamics. Statistical Learning Studies of Pseudo Folding Pathways
Many algorithms that attempt to predict proteins’ native structure from sequence need to generate a large set of hypotheses in order to ensure that nearly correct structures are included, leading to the proble...
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Article
Open AccessPredicting zinc binding at the proteome level
Metalloproteins are proteins capable of binding one or more metal ions, which may be required for their biological function, for regulation of their activities or for structural purposes. Metal-binding propert...
