AIMC Topic: Gene Regulatory Networks

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Rapid Reconstruction of Time-varying Gene Regulatory Networks with Limited Main Memory.

IEEE/ACM transactions on computational biology and bioinformatics
Reconstruction of time-varying gene regulatory networks underlying a time-series gene expression data is a fundamental challenge in the computational systems biology. The challenge increases multi-fold if the target networks need to be constructed fo...

Prioritizing and characterizing functionally relevant genes across human tissues.

PLoS computational biology
Knowledge of genes that are critical to a tissue's function remains difficult to ascertain and presents a major bottleneck toward a mechanistic understanding of genotype-phenotype links. Here, we present the first machine learning model-FUGUE-combini...

Single-cell classification using graph convolutional networks.

BMC bioinformatics
BACKGROUND: Analyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the identification ...

SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions.

BMC bioinformatics
BACKGROUND: The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms-t...

Dual-Organ Transcriptomic Analysis of Rainbow Trout Infected With Through Co-Expression and Machine Learning.

Frontiers in immunology
is a major pathogen that causes a high mortality rate in trout farms. However, systemic responses to the pathogen and its interactions with multiple organs during the course of infection have not been well described. In this study, dual-organ transc...

Dynamical robustness and its structural dependence in biological networks.

Journal of theoretical biology
We discuss the dynamical robustness of biological networks represented by directed graphs, such as neural networks and gene regulatory networks. The theoretical results indicate that networks with low indegree variance and high outdegree variance are...

Finding gene network topologies for given biological function with recurrent neural network.

Nature communications
Searching for possible biochemical networks that perform a certain function is a challenge in systems biology. For simple functions and small networks, this can be achieved through an exhaustive search of the network topology space. However, it is di...

Identification of potential genomic biomarkers for Parkinson's disease using data pooling of gene expression microarrays.

Biomarkers in medicine
In this study, we aimed to identify potential diagnostic biomarkers Parkinson's disease (PD) by exploring microarray gene expression data of PD patients. Differentially expressed genes associated with PD were screened from the GSE99039 dataset usin...