AIMC Topic: Transcriptome

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A Machine Learning Classifier for Assigning Individual Patients With Systemic Sclerosis to Intrinsic Molecular Subsets.

Arthritis & rheumatology (Hoboken, N.J.)
OBJECTIVE: High-throughput gene expression profiling of tissue samples from patients with systemic sclerosis (SSc) has identified 4 "intrinsic" gene expression subsets: inflammatory, fibroproliferative, normal-like, and limited. Prior methods require...

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA.

BMC cancer
BACKGROUND: Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion...

Multigene signatures of responses to chemotherapy derived by biochemically-inspired machine learning.

Molecular genetics and metabolism
Pharmacogenomic responses to chemotherapy drugs can be modeled by supervised machine learning of expression and copy number of relevant gene combinations. Such biochemical evidence can form the basis of derived gene signatures using cell line data, w...

HetEnc: a deep learning predictive model for multi-type biological dataset.

BMC genomics
BACKGROUND: Researchers today are generating unprecedented amounts of biological data. One trend in current biological research is integrated analysis with multi-platform data. Effective integration of multi-platform data into the solution of a singl...

Inferring Gene Regulatory Networks of Metabolic Enzymes Using Gradient Boosted Trees.

IEEE journal of biomedical and health informatics
Metabolic reprogramming is a hallmark of cancer. In cancer cells, transcription factors (TFs) govern metabolic reprogramming through abnormally increasing or decreasing the transcription rate of metabolic enzymes, which provides cancer cells growth a...

Differential gene expression and gene ontologies associated with increasing water-stress in leaf and root transcriptomes of perennial ryegrass (Lolium perenne).

PloS one
Perennial ryegrass (Lolium perenne) is a forage and amenity grass species widely cultivated in temperate regions worldwide. As such, perennial ryegrass populations are exposed to a range of environmental conditions and stresses on a seasonal basis an...

scGen predicts single-cell perturbation responses.

Nature methods
Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to ph...

Wx: a neural network-based feature selection algorithm for transcriptomic data.

Scientific reports
Next-generation sequencing (NGS), which allows the simultaneous sequencing of billions of DNA fragments simultaneously, has revolutionized how we study genomics and molecular biology by generating genome-wide molecular maps of molecules of interest. ...

Machine learning approaches to predict lupus disease activity from gene expression data.

Scientific reports
The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different mi...

A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation.

Cell
Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequence alone. We trained our model (APARENT, APA REgression NeT) on isoform expression data from over ...