Knowledge-based incremental induction of clinical algorithms

The current approaches for the induction of medical procedural knowledge suffer from several drawbacks: the structures produced may not be explicit medical structures, they are only based on statistical measures that do not necessarily respect medical criteria which can be essential to guarantee medical correct structures, or they are not prepared to deal with the incremental arrival of new data. In this thesis we propose a methodology to automatically induce medically correct clinical algorithms (CAs) from hospital databases. These CAs are represented according to the SDA knowledge model. The methodology considers relevant background knowledge and it is able to work in an incremental way. The methodology has been tested in the domains of hypertension, diabetes mellitus and the comborbidity of both diseases. As a result, we propose a repository of background knowledge for these pathologies and provide the SDA diagrams obtained. Later analyses show that the results are medically correct and comprehensible when validated with health care professionals

A graph theoretic approach to protein structure selection.

none4OBJECTIVE: Protein structure prediction (PSP) aims to reconstruct the 3D structure of a given protein starting from its primary structure (chain of amino acidic residues). It is a well-known fact that the 3D structure of a protein only depends on its primary structure. PSP is one of the most important and still unsolved problems in computational biology. Protein structure selection (PSS), instead of reconstructing a 3D model for the given chain, aims to select among a given, possibly large, number of 3D structures (called decoys) those that are closer (according to a given notion of distance) to the original (unknown) one. In this paper we address PSS problem using graph theoretic techniques. METHODS AND MATERIALS: Existing methods for solving PSS make use of suitably defined energy functions which heavily rely on the primary structure of the protein and on protein chemistry. In this paper we present a new approach to PSS which does not take advantage of the knowledge of the primary structure of the protein but only depends on the graph theoretic properties of the decoys graphs (vertices represent residues and edges represent pairs of residues whose Euclidean distance is less than or equal to a fixed threshold). RESULTS: Even if our methods only rely on approximate geometric information, experimental results show that some of the adopted graph properties score similarly to energy-based filtering functions in selecting the best decoys. CONCLUSION: Our results highlight the principal role of geometric information in PSS, setting a new starting point and filtering method for existing energy function-based techniques.DOI: 10.1016/j.artmed.2008.07.016 WOS:000264946700014 SCOPUS:2-s2.0-61449181341noneVassura M.; Margara L.; Fariselli P.; Casadio R.Vassura, M.; Margara, L.; Fariselli, Piero; Casadio, R

A Graph Theoretic Approach to Protein Structure Selection

Protein Structure Prediction (PSP) aims to reconstruct the 3D structure of a given protein starting from its primary structure (chain of amino acids). It is a well known fact that the 3D structure of a protein only depends on its primary structure. PSP is one of the most important and still unsolved problems in computational biology. Protein Structure Selection (PSS), instead of reconstructing a 3D model for the given chain, aims to select among a given, possibly large, number of 3D structures (called decoys) those that are closer (according to a given notion of distance) to the original (unknown) one. Each decoy is represented by a set of points in 3D. Existing methods for solving PSS make use of suitably defined energy functions which heavily rely on the primary structure of the protein and on protein chemistry. In this paper we present a completely different approach to PSS which does not take advantage at all of the knowledge of the primary structure of the protein but only relies on the graph theoretic properties of the decoys graphs (vertices represent amino acids and edges represent pairs of amino acids whose euclidean distance is less than or equal to a fixed threshold). Even if our methods only rely on approximate geometric information, experimental results show that some of the graph properties we adopt score similarly to energy-based filtering functions in selecting the best decoys. Our results show the principal role of geometric information in PSS, setting a new starting point and filtering method, for existing energy function-based techniques

A graph theoretic approach to protein structure selection.

OBJECTIVE: Protein structure prediction (PSP) aims to reconstruct the 3D structure of a given protein starting from its primary structure (chain of amino acidic residues). It is a well-known fact that the 3D structure of a protein only depends on its primary structure. PSP is one of the most important and still unsolved problems in computational biology. Protein structure selection (PSS), instead of reconstructing a 3D model for the given chain, aims to select among a given, possibly large, number of 3D structures (called decoys) those that are closer (according to a given notion of distance) to the original (unknown) one. In this paper we address PSS problem using graph theoretic techniques. METHODS AND MATERIALS: Existing methods for solving PSS make use of suitably defined energy functions which heavily rely on the primary structure of the protein and on protein chemistry. In this paper we present a new approach to PSS which does not take advantage of the knowledge of the primary structure of the protein but only depends on the graph theoretic properties of the decoys graphs (vertices represent residues and edges represent pairs of residues whose Euclidean distance is less than or equal to a fixed threshold). RESULTS: Even if our methods only rely on approximate geometric information, experimental results show that some of the adopted graph properties score similarly to energy-based filtering functions in selecting the best decoys. CONCLUSION: Our results highlight the principal role of geometric information in PSS, setting a new starting point and filtering method for existing energy function-based techniques

A Graph Theoretic Approach to Protein Structure Selection

Protein Structure Prediction (PSP) aims to reconstruct the 3D structure of a given protein starting from its primary structure (chain of amino acids). It is a well known fact that the 3D structure of a protein only depends on its primary structure. PSP is one of the most important and still unsolved problems in computational biology. Protein Structure Selection (PSS), instead of reconstructing a 3D model for the given chain, aims to select among a given, possibly large, number of 3D structures (called decoys) those that are closer (according to a given notion of distance) to the original (unknown) one. Each decoy is represented by a set of points in 3D. Existing methods for solving PSS make use of suitably defined energy functions which heavily rely on the primary structure of the protein and on protein chemistry. In this paper we present a completely different approach to PSS which does not take advantage at all of the knowledge of the primary structure of the protein but only relies on the graph theoretic properties of the decoys graphs (vertices represent amino acids and edges represent pairs of amino acids whose euclidean distance is less than or equal to a fixed threshold). Even if our methods only rely on approximate geometric information, experimental results show that some of the graph properties we adopt score similarly to energy-based filtering functions in selecting the best decoys. Our results show the principal role of geometric information in PSS, setting a new starting point and filtering method, for existing energy function-based techniques