AIMC Topic: Transcriptome
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Tox-GAN: An Artificial Intelligence Approach Alternative to Animal Studies-A Case Study With Toxicogenomics.
Toxicological sciences : an official journal of the Society of Toxicology
Mar 28, 2022
Animal studies are a critical component in biomedical research, pharmaceutical product development, and regulatory submissions. There is a worldwide effort in toxicology toward "reducing, refining, and replacing" animal use. Here, we proposed a deep ...
A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data.
Briefings in bioinformatics
Mar 10, 2022
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 ...
A functional module states framework reveals transcriptional states for drug and target prediction.
Cell reports
Jan 18, 2022
Cells are complex systems in which many functions are performed by different genetically defined and encoded functional modules. To systematically understand how these modules respond to drug or genetic perturbations, we develop a functional module s...
Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.
Briefings in bioinformatics
Jan 17, 2022
Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant incre...
[Percellome Project: research on molecular mechanisms of toxicological responses based on transcriptomics and epigenetics].
Nihon yakurigaku zasshi. Folia pharmacologica Japonica
Jan 1, 2022
We are constructing the "Percellome Database" containing many transcriptomes of mice exposed to a series of chemicals to elucidate the molecular mechanism of toxicity and to develop toxicity prediction technology. Acute toxicity of a chemical can be ...
Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach.
Bioscience reports
Dec 22, 2021
BACKGROUND: Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. So far, very few attempts have been made to characterize the surfaceome of patien...
Representation learning of RNA velocity reveals robust cell transitions.
Proceedings of the National Academy of Sciences of the United States of America
Dec 7, 2021
RNA velocity is a promising technique for quantifying cellular transitions from single-cell transcriptome experiments and revealing transient cellular dynamics among a heterogeneous cell population. However, the cell transitions estimated from high-d...
scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network.
Nucleic acids research
Dec 2, 2021
Advances in single-cell RNA sequencing (scRNA-seq) have furthered the simultaneous classification of thousands of cells in a single assay based on transcriptome profiling. In most analysis protocols, single-cell type annotation relies on marker genes...
Construction of a 5-feature gene model by support vector machine for classifying osteoporosis samples.
Bioengineered
Dec 1, 2021
Osteoporosis is a progressive bone disease in the elderly and lacks an effective classification method of patients. This study constructed a gene signature for an accurate prediction and classification of osteoporosis patients. Three gene expression ...
A nine-hub-gene signature of metabolic syndrome identified using machine learning algorithms and integrated bioinformatics.
Bioengineered
Dec 1, 2021
Early risk assessments and interventions for metabolic syndrome (MetS) are limited because of a lack of effective biomarkers. In the present study, several candidate genes were selected as a blood-based transcriptomic signature for MetS. We collected...
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