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Giuseppe Agapito

Researcher at Magna Græcia University

Publications -  88
Citations -  974

Giuseppe Agapito is an academic researcher from Magna Græcia University. The author has contributed to research in topics: Association rule learning & Computer science. The author has an hindex of 15, co-authored 69 publications receiving 621 citations.

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Visualization of protein interaction networks: problems and solutions

TL;DR: A current trend is the deployment of open, extensible visualization tools (e.g. Cytoscape), that may be incrementally enriched by the interactomics community with novel and more powerful functions for PIN analysis, through the development of plug-ins.
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DMET™ (Drug Metabolism Enzymes and Transporters): a pharmacogenomic platform for precision medicine.

TL;DR: This review focuses on the potentiality, reliability and limitations of the DMET™ (Drug Metabolism Enzymes and Transporters) Plus as pharmacogenomic drug metabolism multi-gene panel platform for selecting biomarkers in the final aim to optimize drugs use and characterize the individual genetic background.
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DMET-Analyzer: automatic analysis of Affymetrix DMET Data

TL;DR: DMET Analyzer is a novel tool able to automatically analyse data produced by the DMET-platform in case-control association studies, and may avoid wasting time in the manual execution of multiple statistical tests avoiding possible errors and reducing the amount of time needed for a whole experiment.
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DIETOS: A dietary recommender system for chronic diseases monitoring and management.

TL;DR: DIETOS is a novel food recommender system for healthy people and individuals affected by diet-related chronic diseases, allowing to determine a medical-controlled user's health profile and to perform a fine-grained recommendation that is better adapted to each user health status.
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DMET-Miner

TL;DR: DMET-Miner extends the DMET-Analyzer tool with data mining capabilities and correlates the presence of a set of allelic variants with the conditions of patient's samples by exploiting association rules, to face the high number of frequent itemsets generated when considering large clinical studies based on DMET data.