AIMC Topic: Gene Regulatory Networks

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DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning.

International journal of molecular sciences
Aberrant miRNA expression has been associated with a large number of human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem from the dysfunction of pathways regulated...

Cross-species cell-type assignment from single-cell RNA-seq data by a heterogeneous graph neural network.

Genome research
Cross-species comparative analyses of single-cell RNA sequencing (scRNA-seq) data allow us to explore, at single-cell resolution, the origins of the cellular diversity and evolutionary mechanisms that shape cellular form and function. Cell-type assig...

Deep learning based protocol to construct an immune-related gene network of host-pathogen interactions in plants.

STAR protocols
Investigating network behavior from host-pathogen interactions is challenging. Here, we present the deep-learning-based protocol to construct an immune-related gene network and list the genes involved in the defense response of host to specific bioti...

Deepgmd: A Graph-Neural-Network-Based Method to Detect Gene Regulator Module.

IEEE/ACM transactions on computational biology and bioinformatics
Regulatory module mining methods divide genes into multiple gene subgroups and explore potential biological mechanisms from omics data. By transforming gene expression profile data into gene co-expression network, we transform the task of gene module...

Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation.

IEEE/ACM transactions on computational biology and bioinformatics
Approximate Bayesian Computation is widely used in systems biology for inferring parameters in stochastic gene regulatory network models. Its performance hinges critically on the ability to summarize high-dimensional system responses such as time ser...

Graph-Based Bayesian Optimization for Large-Scale Objective-Based Experimental Design.

IEEE transactions on neural networks and learning systems
Design is an inseparable part of most scientific and engineering tasks, including real and simulation-based experimental design processes and parameter/hyperparameter tuning/optimization. Several model-based experimental design techniques have been d...

Deep Learning-Based Medical Data Association Rules to Explore the Connectivity and Regulation Mechanism of miRNA-mRNA Network in Myocarditis.

Computational and mathematical methods in medicine
Acute, chronic myocarditis as myocardial localized or diffuse inflammation lesions is usually involving cardiac function in patients with severe adverse outcomes such as heart failure, sudden death, and no unified, but its pathogenesis clinical is ma...

Latent space of a small genetic network: Geometry of dynamics and information.

Proceedings of the National Academy of Sciences of the United States of America
The high-dimensional character of most biological systems presents genuine challenges for modeling and prediction. Here we propose a neural network-based approach for dimensionality reduction and analysis of biological gene expression data, using, as...

Finite-Time H State Estimation for PDT-Switched Genetic Regulatory Networks With Randomly Occurring Uncertainties.

IEEE/ACM transactions on computational biology and bioinformatics
This article is concerned with the problem of finite-time H state estimation for switched genetic regulatory networks with randomly occurring uncertainties. The persistent dwell-time switching rule, as a more versatile class of switching rules, is co...

Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks.

PloS one
In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related...