2023
DOI: 10.1371/journal.pone.0288174
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MICFuzzy: A maximal information content based fuzzy approach for reconstructing genetic networks

Abstract: In systems biology, the accurate reconstruction of Gene Regulatory Networks (GRNs) is crucial since these networks can facilitate the solving of complex biological problems. Amongst the plethora of methods available for GRN reconstruction, information theory and fuzzy concepts-based methods have abiding popularity. However, most of these methods are not only complex, incurring a high computational burden, but they may also produce a high number of false positives, leading to inaccurate inferred networks. In th… Show more

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Cited by 2 publications
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“…The inference of biologically viable and accurate GRN models is still a challenging task. In addition to methods for the inference of Boolean networks, several GRN inference approaches have been proposed, including fuzzy logic-based approaches [ 17 , 18 ], regression and machine learning-based approaches [ 19 , 20 ], information theory-based approaches [ 21 , 22 ], correlation-based approaches [ 23 ], and probabilistic approaches [ 24 , 25 ]. Gene expression is a product guided by multiple processes and factors.…”
Section: Introductionmentioning
confidence: 99%
“…The inference of biologically viable and accurate GRN models is still a challenging task. In addition to methods for the inference of Boolean networks, several GRN inference approaches have been proposed, including fuzzy logic-based approaches [ 17 , 18 ], regression and machine learning-based approaches [ 19 , 20 ], information theory-based approaches [ 21 , 22 ], correlation-based approaches [ 23 ], and probabilistic approaches [ 24 , 25 ]. Gene expression is a product guided by multiple processes and factors.…”
Section: Introductionmentioning
confidence: 99%
“…The inference of biologically viable and accurate GRN models is still a challenging task. In addition to methods for the inference of Boolean networks, several GRN inference approaches have been proposed, including fuzzy logic-based approaches [17,18], regression and machine learning-based approaches [19,20], information theory-based approaches [21,22], correlation-based approaches [23], and probabilistic approaches [24,25]. Gene expression is a product guided by multiple processes and factors.…”
Section: Introductionmentioning
confidence: 99%