AIMC Topic: Transcriptome

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Integrated meta-analysis and machine learning approach identifies acyl-CoA thioesterase with other novel genes responsible for biofilm development in Staphylococcus aureus.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases Jan 1, 2021
Biofilm forming Staphylococcus aureus is a major threat to the health-care industry. It is important to understand the differences between planktonic and biofilm growth forms in the pathogen since conventional treatments targeting the planktonic form...
Humans Algorithms Transcriptome Staphylococcal Infections Datasets as Topic Microarray Analysis Gene Expression Regulation, Bacterial Biofilms RNA-Seq Staphylococcus aureus Supervised Machine Learning
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Construction and analysis of a joint diagnosis model of random forest and artificial neural network for heart failure.

Aging Dec 26, 2020
Heart failure is a global health problem that affects approximately 26 million people worldwide. As conventional diagnostic techniques for heart failure have been in practice with various limitations, it is necessary to develop novel diagnostic model...
Humans Neural Networks, Computer Machine Learning Transcriptome RNA-Binding Proteins Databases, Genetic Heart Failure Receptors, Interleukin Lipocalins Serpins RNA Splicing Factors Heme Oxygenase (Decyclizing)
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Characterization of Antiphospholipid Syndrome Atherothrombotic Risk by Unsupervised Integrated Transcriptomic Analyses.

Arteriosclerosis, thrombosis, and vascular biology Dec 24, 2020
OBJECTIVE: Our aim was to characterize distinctive clinical antiphospholipid syndrome phenotypes and identify novel microRNA (miRNA)-mRNA-intracellular signaling regulatory networks in monocytes linked to cardiovascular disease. Approach and Results:...
Thrombosis Gene Expression Regulation Oligonucleotide Array Sequence Analysis Antiphospholipid Syndrome Signal Transduction Cluster Analysis MicroRNAs Unsupervised Machine Learning Protein Interaction Maps Gene Regulatory Networks Monocytes Risk Factors Adult Male Middle Aged Risk Assessment Gene Expression Profiling Transcriptome Case-Control Studies Humans Cells, Cultured Female
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Modeling the human aging transcriptome across tissues, health status, and sex.

Aging cell Dec 18, 2020
Aging in humans is an incredibly complex biological process that leads to increased susceptibility to various diseases. Understanding which genes are associated with healthy aging can provide valuable insights into aging mechanisms and possible avenu...
Health Status Sex Factors Humans Machine Learning Aging Transcriptome
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Denoising Autoencoder, A Deep Learning Algorithm, Aids the Identification of A Novel Molecular Signature of Lung Adenocarcinoma.

Genomics, proteomics & bioinformatics Dec 18, 2020
Precise biomarker development is a key step in disease management. However, most of the published biomarkers were derived from a relatively small number of samples with supervised approaches. Recent advances in unsupervised machine learning promise t...
Humans Algorithms Transcriptome Deep Learning Lung Neoplasms Adenocarcinoma of Lung
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Machine-learning-driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis.

Proceedings of the National Academy of Sciences of the United States of America Dec 14, 2020
Contact dermatitis tremendously impacts the quality of life of suffering patients. Currently, diagnostic regimes rely on allergy testing, exposure specification, and follow-up visits; however, distinguishing the clinical phenotype of irritant and all...
Algorithms Biomarkers Female Reproducibility of Results Humans Machine Learning Gene Regulatory Networks Databases, Genetic Diagnosis, Differential Skin Adult Male Severity of Illness Index Transcriptome Irritants Leukocytes Dermatitis, Allergic Contact Allergens Patch Tests Gene Expression Regulation Dermatitis, Irritant
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Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome.

Nature communications Dec 11, 2020
The transcriptional regulatory network (TRN) of Bacillus subtilis coordinates cellular functions of fundamental interest, including metabolism, biofilm formation, and sporulation. Here, we use unsupervised machine learning to modularize the transcrip...
Histidine Tryptophan Quorum Sensing Gene Expression Regulation, Bacterial Iron Chelating Agents Machine Learning Transcriptome Gene Regulatory Networks Bacillus subtilis Ethanol DNA Damage Bacterial Proteins Data Mining
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Deep Learning Benchmarks on L1000 Gene Expression Data.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2020
Gene expression data can offer deep, physiological insights beyond the static coding of the genome alone. We believe that realizing this potential requires specialized, high-capacity machine learning methods capable of using underlying biological str...
Cell Line Protein Interaction Maps Computational Biology Models, Genetic Databases, Genetic Gene Expression Profiling Humans Transcriptome Algorithms Deep Learning
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Identifying transcriptomic correlates of histology using deep learning.

PloS one Nov 25, 2020
Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide ...
Genome Deep Learning Humans Neoplasms Histology Gene Expression Regulation, Neoplastic Transcriptome Neoplasm Proteins Tissue Distribution
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A random forest based biomarker discovery and power analysis framework for diagnostics research.

BMC medical genomics Nov 23, 2020
BACKGROUND: Biomarker identification is one of the major and important goal of functional genomics and translational medicine studies. Large scale -omics data are increasingly being accumulated and can provide vital means for the identification of bi...
Computer Simulation Transcriptome Molecular Diagnostic Techniques Metabolomics Humans Machine Learning Algorithms Biomarkers Regression Analysis Decision Trees Lipidomics Sample Size Datasets as Topic Workflow
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