AIMC Topic: Transcriptome

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Metabonomic and transcriptomic analyses of glycosides tablet-induced hepatotoxicity in rats.

Drug and chemical toxicology
We aimed to explore novel biomarkers involved in alterations of metabolism and gene expression related to the hepatotoxic effects of glycosides tablet (TGT) in rats. Rats were randomly divided into groups based on oral administration of TGTs for 6 w...

Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning.

Nature communications
Despite the fact that the cell cycle is a fundamental process of life, a detailed quantitative understanding of gene regulation dynamics throughout the cell cycle is far from complete. Single-cell RNA-sequencing (scRNA-seq) technology gives access to...

Identifying common transcriptome signatures of cancer by interpreting deep learning models.

Genome biology
BACKGROUND: Cancer is a set of diseases characterized by unchecked cell proliferation and invasion of surrounding tissues. The many genes that have been genetically associated with cancer or shown to directly contribute to oncogenesis vary widely bet...

Cross-tissue immune cell analysis reveals tissue-specific features in humans.

Science (New York, N.Y.)
Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. We surveyed the immune compartment of 16 tissues from 12 adult donors by single-cell RNA sequencing and VDJ sequencing generating a ...

Gradient tree boosting and network propagation for the identification of pan-cancer survival networks.

STAR protocols
Cancer survival prediction is typically done with uninterpretable machine learning techniques, e.g., gradient tree boosting. Therefore, additional steps are needed to infer biological plausibility of the predictions. Here, we describe a protocol that...

Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin.

International journal of molecular sciences
To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means of DNA microarray or RNA-seq technology. Such data can also be useful to classify infected individual...

Machine Learning Identifies Pan-Cancer Landscape of Nrf2 Oxidative Stress Response Pathway-Related Genes.

Oxidative medicine and cellular longevity
BACKGROUND: Oxidative stress produced a large amount of reactive oxygen species (ROS), which played a pivotal role in balanced ability and determining cell fate. The activated Nrf2 signaling pathway that responds to the excessive ROS regulated the ex...

Construction of genetic classification model for coronary atherosclerosis heart disease using three machine learning methods.

BMC cardiovascular disorders
BACKGROUND: Although the diagnostic method for coronary atherosclerosis heart disease (CAD) is constantly innovated, CAD in the early stage is still missed diagnosis for the absence of any symptoms. The gene expression levels varied during disease de...

Inferring protein expression changes from mRNA in Alzheimer's dementia using deep neural networks.

Nature communications
Identifying the molecular systems and proteins that modify the progression of Alzheimer's disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic...