AIMC Topic: Transcriptome

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Cell type prioritization in single-cell data.

Nature biotechnology Jul 20, 2020
We present Augur, a method to prioritize the cell types most responsive to biological perturbations in single-cell data. Augur employs a machine-learning framework to quantify the separability of perturbed and unperturbed cells within a high-dimensio...
Databases, Genetic Walking Nerve Net Computational Biology Sequence Analysis, RNA Rats Chromatin Machine Learning Animals Single-Cell Analysis Mice Gene Expression Profiling Transcriptome
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Exploration of the mechanism of traditional Chinese medicine by AI approach using unsupervised machine learning for cellular functional similarity of compounds in heterogeneous networks, XiaoErFuPi granules as an example.

Pharmacological research Jul 17, 2020
'Polypharmacology' is usually used to describe the network-wide effect of a single compound, but traditional Chinese medicine (TCM) has a polypharmacological effect naturally based on the 'multi-components, multi-targets and multi-pathways' principle...
Male Gene Expression Profiling Transcriptome Protein Interaction Maps Humans Animals Polypharmacology Dyspepsia Proteomics Gene Regulatory Networks Medicine, Chinese Traditional Proteome Unsupervised Machine Learning Molecular Structure Systems Biology Signal Transduction Rats, Sprague-Dawley Structure-Activity Relationship Duodenum Drugs, Chinese Herbal Cluster Analysis
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A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth.

Proceedings of the National Academy of Sciences of the United States of America Jul 16, 2020
Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype-phenotype-environment relationship. Rather than being used in isolation, it is becoming clear th...
Transcriptome Systems Biology Models, Biological Machine Learning Saccharomyces cerevisiae Metabolic Flux Analysis
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DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning.

Genome biology Jul 10, 2020
Dropouts distort gene expression and misclassify cell types in single-cell transcriptome. Although imputation may improve gene expression and downstream analysis to some degree, it also inevitably introduces false signals. We develop DISC, a novel de...
Transcriptome Deep Learning Single-Cell Analysis Sequence Analysis, RNA Software
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Metatranscriptomics reveals the gene functions and metabolic properties of the major microbial community during Chinese Sichuan Paocai fermentation.

Food microbiology Jul 3, 2020
Chinese Sichuan Paocai (CSP) is one of the world's best-known fermented vegetables with a large presence in the Chinese market. The dynamic microbial community is the main contributor to Paocai fermentation. However, little is known about the ecologi...
Enterobacteriaceae Microbiota Fermented Foods Metagenomics Fermentation Transcriptome Lactobacillaceae Vegetables Brassica China Food Microbiology
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Selective Neuronal Vulnerability in Alzheimer's Disease: A Network-Based Analysis.

Neuron Jun 29, 2020
A major obstacle to treating Alzheimer's disease (AD) is our lack of understanding of the molecular mechanisms underlying selective neuronal vulnerability, a key characteristic of the disease. Here, we present a framework integrating high-quality neu...
Aging Humans Gene Expression Profiling Machine Learning Neurons Gene Regulatory Networks Transcriptome Animals Alzheimer Disease Mice
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Integrating spatial gene expression and breast tumour morphology via deep learning.

Nature biomedical engineering Jun 22, 2020
Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the develop...
Gene Expression Profiling Reproducibility of Results Transcriptome Breast Neoplasms Deep Learning Biomarkers, Tumor Humans Algorithms Image Processing, Computer-Assisted Female
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Gene Expression Profile Prediction in Uveal Melanoma Using Deep Learning: A Pilot Study for the Development of an Alternative Survival Prediction Tool.

Ophthalmology. Retina Jun 18, 2020
Uveal Melanoma Gene Expression Regulation, Neoplastic Anterior Eye Segment Transcriptome Melanoma Uveal Neoplasms Deep Learning Biomarkers, Tumor Pilot Projects Humans
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A Support Vector Machine Model Predicting the Risk of Duodenal Cancer in Patients with Familial Adenomatous Polyposis at the Transcript Levels.

BioMed research international Jun 16, 2020
OBJECTIVE: Familial adenomatous polyposis (FAP) is one major type of inherited duodenal cancer. The estimate of duodenal cancer risk in patients with FAP is critical for selecting the optimal treatment strategy.
Databases, Genetic Metabolic Networks and Pathways Adenomatous Polyposis Coli Humans Duodenal Neoplasms Transcriptome Risk Assessment Computational Biology Support Vector Machine
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Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma.

Blood cancer journal May 22, 2020
Non-Hodgkin B-cell lymphomas (B-NHLs) are a highly heterogeneous group of mature B-cell malignancies. Their classification thus requires skillful evaluation by expert hematopathologists, but the risk of error remains higher in these tumors than in ma...
Diagnosis, Computer-Assisted Progression-Free Survival Lymphoma, B-Cell Tumor Microenvironment Biomarkers, Tumor Gene Expression Profiling Humans Transcriptome Machine Learning
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