AIMC Topic: Gene Regulatory Networks

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Characterization of Antiphospholipid Syndrome Atherothrombotic Risk by Unsupervised Integrated Transcriptomic Analyses.

Arteriosclerosis, thrombosis, and vascular biology
OBJECTIVE: Our aim was to characterize distinctive clinical antiphospholipid syndrome phenotypes and identify novel microRNA (miRNA)-mRNA-intracellular signaling regulatory networks in monocytes linked to cardiovascular disease. Approach and Results:...

Systems biology in cardiovascular disease: a multiomics approach.

Nature reviews. Cardiology
Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for anal...

Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks.

Scientific reports
The reconstruction of Gene Regulatory Networks (GRNs) from gene expression data, supported by machine learning approaches, has received increasing attention in recent years. The task at hand is to identify regulatory links between genes in a network....

On tower and checkerboard neural network architectures for gene expression inference.

BMC genomics
BACKGROUND: One possible approach how to economically facilitate gene expression profiling is to use the L1000 platform which measures the expression of ∼1,000 landmark genes and uses a computational method to infer the expression of another ∼10,000 ...

Machine-learning-driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis.

Proceedings of the National Academy of Sciences of the United States of America
Contact dermatitis tremendously impacts the quality of life of suffering patients. Currently, diagnostic regimes rely on allergy testing, exposure specification, and follow-up visits; however, distinguishing the clinical phenotype of irritant and all...

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome.

Nature communications
The transcriptional regulatory network (TRN) of Bacillus subtilis coordinates cellular functions of fundamental interest, including metabolism, biofilm formation, and sporulation. Here, we use unsupervised machine learning to modularize the transcrip...

Inferring a network from dynamical signals at its nodes.

PLoS computational biology
We give an approximate solution to the difficult inverse problem of inferring the topology of an unknown network from given time-dependent signals at the nodes. For example, we measure signals from individual neurons in the brain, and infer how they ...

CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph.

BMC bioinformatics
BACKGROUND: Elucidation of interactive relation between chemicals and genes is of key relevance not only for discovering new drug leads in drug development but also for repositioning existing drugs to novel therapeutic targets. Recently, biological n...

Global gene network exploration based on explainable artificial intelligence approach.

PloS one
In recent years, personalized gene regulatory networks have received significant attention, and interpretation of the multilayer networks has been a critical issue for a comprehensive understanding of gene regulatory systems. Although several statist...