A Machine-Learning Approach for theof Enzymatic Activity of Proteins in Metagenomic Samples

Abstract : In this work, a machine-learning approach was developed, which performs the prediction of the putative enzymatic function of unknown proteins, based on the PFAM protein domain database and the Enzyme Commission (EC) numbers that describe the enzymatic activities. The classifier was trained with well annotated protein datasets from the Uniprot database, in order to define the characteristic domains of each enzymatic sub-category in the class of Hydrolases. As a conclusion, the machine-learning procedure based on Hmmer3 scores against the PFAM database can accurately predict the enzymatic activity of unknown proteins as a part of metagenomic analysis workflows.
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Communication dans un congrès
Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-412, pp.81-87, 2013, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-41142-7_9〉
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Theodoros Koutsandreas, Eleftherios Pilalis, Aristotelis Chatziioannou. A Machine-Learning Approach for theof Enzymatic Activity of Proteins in Metagenomic Samples. Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-412, pp.81-87, 2013, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-41142-7_9〉. 〈hal-01459667〉

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