LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data

Abstract Reconstructing the topology of gene regulatory network from gene expression data has been extensively studied. With the abundance functional transcriptomic data available, it is now feasible to systematically decipher regulatory interaction dynamics in a logic form such as a Boolean network...

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Vydané v:	Briefings in bioinformatics Ročník 25; číslo 4
Hlavní autori:	Zhang, Dezhen, Gao, Shuhua, Liu, Zhi-Ping, Gao, Rui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Oxford University Press 23.05.2024 Oxford Publishing Limited (England)
Predmet:	Algorithms Boolean Boolean algebra Boolean functions Computational Biology - methods Dynamic characteristics Functionals Gene expression Gene Expression Profiling - methods Gene Regulatory Networks Humans Inference Multiple objective analysis Network topologies Neural networks Neural Networks, Computer Problem Solving Protocol Software Structure-function relationships Time series Topology Topology optimization Transcriptome Transcriptomics single-cell RNA sequencing network inference Boolean network symbolic regression gene regulatory network
ISSN:	1467-5463, 1477-4054, 1477-4054
On-line prístup:	Získať plný text
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