We present here a novel algorithm based on a MapReduce approach to facilitate the discovery of novel therapeutic targets. The proposed algorithm has been enabled to scan a set biological pathways in order to discover non-trivial (less common) routes. Such routes represent a chain of biochemical interactions among different biological actors. These actors can be represented by quite distant nodes along the devised pathway. Our approach detects nodes that are far from the initial target nodes, also showing the number of times that a given route has been found inside the selected set of biological pathways.

A MapReduce Based Tool for the Analysis and Discovery of Novel Therapeutic Targets

Parasiliti, Giuseppe;Biondi, Pietro;Sgroi, Giuseppe;Russo, Giulia;Napoli, Christian;Pappalardo, Francesco
2019-01-01

Abstract

We present here a novel algorithm based on a MapReduce approach to facilitate the discovery of novel therapeutic targets. The proposed algorithm has been enabled to scan a set biological pathways in order to discover non-trivial (less common) routes. Such routes represent a chain of biochemical interactions among different biological actors. These actors can be represented by quite distant nodes along the devised pathway. Our approach detects nodes that are far from the initial target nodes, also showing the number of times that a given route has been found inside the selected set of biological pathways.
2019
9781728116440
Biochemical Pathway; gene; MAPK1; MapReduce; Python; therapeutic target; Computer Networks and Communications; Computer Science Applications1707 Computer Vision and Pattern Recognition
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/363939
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