jFuzzyMachine - An Open-Source Fuzzy Logic-based Regulatory Inference Engine for High Throughput Biological Data

Abstract

Elucidating mechanistic relationships between and among intracellular macromolecules is fundamental to understanding the molecular basis of normal and diseased processes. Here, we introduce jFuzzyMachine - a fuzzy logic-based regulatory network inference engine for high-throughput biological data. We describe its design and implementation. We demonstrate its functions on a sampled expression profile of the vorinostat-resistant HCT116 cell line. We compared jFuzzyMachines inferred regulatory network to that inferred by the ARACNe (an Algorithm for the Reconstruction of Gene Regulatory Networks) tool. Potentially more sensitive, jFuzzyMachine showed a slight increase in identified regulatory edges compared to ARACNe. A significant overlap was also observed in the identified edges between the two inference methods. Over 70 percent of edges identified by ARACNe were identified by jFuzzyMachine. Beyond identifying edges, jFuzzyMachine shows direction of interactions, including bidirectional interactions - specifying regulatory inputs and outputs of inferred relationships. jFuzzyMachine addresses an apparent lack of freely available community tool implementing a fuzzy logic regulatory network inference method - mitigating a limitation to applying and extending benefits of the fuzzy inference system to understanding biological data. jFuzzyMachines source codes and precompiled binaries are freely available at the Github repository locations: https://github.com/paiyetan/jfuzzymachine and https://github.com/paiyetan/jfuzzymachine/releases/tag/v1.7.21.