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An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge.

ABSTRACT: Hexoses are simple sugars that play a key role in many cellular pathways, and in the regulation of development and disease mechanisms. Current protein-sugar computational models are based, at least partially, on prior biochemical findings and knowledge. They incorporate different parts of these findings in predictive black-box models. We investigate the empirical support for biochemical findings by comparing Inductive Logic Programming (ILP) induced rules to actual biochemical results. We mine the Protein Data Bank for a representative data set of hexose binding sites, non-hexose binding sites and surface grooves. We build an ILP model of hexose-binding sites and evaluate our results against several baseline machine learning classifiers. Our method achieves an accuracy similar to that of other black-box classifiers while providing insight into the discriminating process. In addition, it confirms wet-lab findings and reveals a previously unreported Trp-Glu amino acids dependency.

SUBMITTER: Nassif H

PROVIDER: S-EPMC4190110 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge.

Nassif Houssam H Al-Ali Hassan H Khuri Sawsan S Keirouz Walid W Page David D

Inductive logic programming. ILP 20100101

Hexoses are simple sugars that play a key role in many cellular pathways, and in the regulation of development and disease mechanisms. Current protein-sugar computational models are based, at least partially, on prior biochemical findings and knowledge. They incorporate different parts of these findings in predictive black-box models. We investigate the empirical support for biochemical findings by comparing Inductive Logic Programming (ILP) induced rules to actual biochemical results. We mine t ...[more]

PMID: 25309972

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Project description:BackgroundHealthcare research funders may undertake various roles to facilitate implementation of research findings. Their ability to enact such roles depends on several factors, knowledge of implementation being one essential requirement. However, previous studies do not assess the type or level of knowledge about implementation that research funders possess. This paper therefore presents findings from a qualitative, inductive study of the implementation knowledge of research funders. Three aspects of this knowledge are explored, namely how research funders define implementation, their level of self-assessed implementation knowledge and the factors influencing their self-assessment of implementation knowledge.MethodsResearch funders (n?=?18) were purposefully selected from a sample of research funding organisations in Sweden (n?=?10). In-depth semi-structured interviews were conducted, recorded and transcribed verbatim. An inductive method using a systematic coding procedure was employed to derive the findings.ResultsThe research funders defined implementation as either an outcome or a process, with the majority believing that implementation of healthcare research results demands a process, although its complexity varied in the research funders' view. They perceived their own level of implementation knowledge as either limited or substantial, with a majority regarding it as limited. Clinical research experience, clinical experience and task relevance were singled out as the clearest factors affecting the self-assessment of their own implementation knowledge.ConclusionsThis study, the first to focus on implementation knowledge of research funders, demonstrates that they are a category of policy-makers who may possess knowledge, based on their previous professional experience, that is comparable to some important findings from implementation research. Consequently, the findings not only pinpoint the relevance of professional experience, but also reveal a lack of awareness and knowledge of the results of implementation research among research funders in charge of healthcare research.

Dataset Information

An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge.

Publications

An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge.

Similar Datasets

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