AIMC Topic: Gene Regulatory Networks

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Gene Regulatory Networks: Improving Inferences with Transfer Learning.

Cellular reprogramming
Deep transfer learning improves the inference of gene regulatory networks in human cells, reveals disease-associated genes, and identifies network-based druggable targets in human heart disease.

Identification of significant gene expression changes in multiple perturbation experiments using knockoffs.

Briefings in bioinformatics
Large-scale multiple perturbation experiments have the potential to reveal a more detailed understanding of the molecular pathways that respond to genetic and environmental changes. A key question in these studies is which gene expression changes are...

Single-cell gene regulatory network prediction by explainable AI.

Nucleic acids research
The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding ...

Accurately modeling biased random walks on weighted networks using node2vec.

Bioinformatics (Oxford, England)
MOTIVATION: Accurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased...

Distributed nonsynchronous event-triggered state estimation of genetic regulatory networks with hidden Markovian jumping parameters.

Mathematical biosciences and engineering : MBE
In this paper, the distributed state estimation problem of genetic regulatory networks (GRNs) with hidden Markovian jumping parameters (HMJPs) is explored. Furthermore, in order to improve the communication efficiency among state estimation sensors, ...

A novel ergodic cellular automaton gene network model towards efficient hardware-based genome simulator.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In this paper, a novel ergodic cellular automaton model of Hes1 mRNA and Hes1 protein network is presented. Detailed analyses reveal that the presented network model can reproduce a typical nonlinear bifurcation phenomenon observed in a conventional ...

A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data.

Briefings in bioinformatics
Inferring gene regulatory networks (GRNs) based on gene expression profiles is able to provide an insight into a number of cellular phenotypes from the genomic level and reveal the essential laws underlying various life phenomena. Different from the ...

Reconstruction of human protein-coding gene functional association network based on machine learning.

Briefings in bioinformatics
Networks consisting of molecular interactions are intrinsically dynamical systems of an organism. These interactions curated in molecular interaction databases are still not complete and contain false positives introduced by high-throughput screening...