AIMC Topic: Transcriptome

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Compendiums of cancer transcriptomes for machine learning applications.

Scientific data Oct 8, 2019
There are massive transcriptome profiles in the form of microarray. The challenge is that they are processed using diverse platforms and preprocessing tools, requiring considerable time and informatics expertise for cross-dataset analyses. If there e...
Neoplasms Transcriptome RNA-Seq Oligonucleotide Array Sequence Analysis Humans Machine Learning Algorithms
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Machine learning predicts putative hematopoietic stem cells within large single-cell transcriptomics data sets.

Experimental hematology Sep 9, 2019
Hematopoietic stem cells (HSCs) are an essential source and reservoir for normal hematopoiesis, and their function is compromised in many blood disorders. HSC research has benefitted from the recent development of single-cell molecular profiling tech...
Mice, Knockout Hematopoietic Stem Cells Machine Learning Animals Gene Expression Profiling Transcriptome Mice Sequence Analysis, RNA
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A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies.

BMC cancer Sep 5, 2019
BACKGROUND: Predicting lung adenocarcinoma (LUAD) risk is crucial in determining further treatment strategies. Molecular biomarkers may improve risk stratification for LUAD.
Machine Learning Prognosis Female Aged Humans Disease Progression Transcriptome Lung Neoplasms Gene Expression Regulation, Neoplastic Survival Rate Biomarkers, Tumor Male Middle Aged Adenocarcinoma of Lung Proportional Hazards Models Multivariate Analysis Kaplan-Meier Estimate Genes, Neoplasm Follow-Up Studies Databases, Genetic Disease-Free Survival
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A Machine Learning Classifier for Assigning Individual Patients With Systemic Sclerosis to Intrinsic Molecular Subsets.

Arthritis & rheumatology (Hoboken, N.J.) Sep 2, 2019
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...
Gene Expression Supervised Machine Learning High-Throughput Nucleotide Sequencing Datasets as Topic Male Middle Aged Humans Gene Expression Profiling Machine Learning Transcriptome Algorithms Scleroderma, Systemic Female Aged Young Adult Adult
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Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA.

BMC cancer Aug 23, 2019
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...
Circulating Tumor DNA Humans Middle Aged Gene Expression Profiling Aged, 80 and over Genomics Transcriptome Machine Learning Colorectal Neoplasms Computational Biology Female ROC Curve Biomarkers, Tumor Neoplasm Staging Reproducibility of Results Male Genome, Human Aged
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Multigene signatures of responses to chemotherapy derived by biochemically-inspired machine learning.

Molecular genetics and metabolism Aug 19, 2019
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...
Transcriptome Cell Line, Tumor Urinary Bladder Neoplasms Metabolic Networks and Pathways Supervised Machine Learning Humans Gene Dosage Female Drug Therapy Breast Neoplasms Gene Expression Profiling
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HetEnc: a deep learning predictive model for multi-type biological dataset.

BMC genomics Aug 8, 2019
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...
Unsupervised Machine Learning Neuroblastoma Transcriptome Deep Learning Computational Biology Models, Statistical Humans Databases, Factual
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Inferring Gene Regulatory Networks of Metabolic Enzymes Using Gradient Boosted Trees.

IEEE journal of biomedical and health informatics Jul 30, 2019
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...
Transcription Factors Enzymes Transcriptome Humans Gene Regulatory Networks Machine Learning Lung Neoplasms Algorithms Gene Expression Profiling
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Differential gene expression and gene ontologies associated with increasing water-stress in leaf and root transcriptomes of perennial ryegrass (Lolium perenne).

PloS one Jul 30, 2019
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...
Gene Ontology Gene Expression Profiling Gene Expression Regulation, Plant Plant Leaves Transcriptome Droughts Genotype Plant Roots Lolium Dehydration Plant Proteins
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scGen predicts single-cell perturbation responses.

Nature methods Jul 29, 2019
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...
Computational Biology Species Specificity Phagocytes Humans Gene Expression Profiling Algorithms Machine Learning Transcriptome Computer Simulation Single-Cell Analysis Animals Mice Leukocytes, Mononuclear
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