AIMC Topic: Transcriptome

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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 Transcriptome Algorithms 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...
Humans Machine Learning Dermatitis, Allergic Contact Patch Tests Dermatitis, Irritant Adult Algorithms Male Biomarkers Gene Regulatory Networks Severity of Illness Index Female Transcriptome Databases, Genetic Reproducibility of Results Leukocytes Diagnosis, Differential Allergens Gene Expression Regulation Irritants Skin
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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...
Transcriptome Gene Regulatory Networks Bacillus subtilis Ethanol Quorum Sensing DNA Damage Machine Learning Gene Expression Regulation, Bacterial Bacterial Proteins Iron Chelating Agents Data Mining Histidine Tryptophan
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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...
Computational Biology Models, Genetic Databases, Genetic Cell Line Gene Expression Profiling Transcriptome Deep Learning Protein Interaction Maps Humans Algorithms
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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 Gene Expression Regulation, Neoplastic Histology Neoplasms Neoplasm Proteins Transcriptome 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 Regression Analysis Machine Learning Decision Trees Algorithms Sample Size Biomarkers Workflow Lipidomics Datasets as Topic
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Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values.

EBioMedicine Nov 10, 2020
BACKGROUND: Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subcla...
ROC Curve Computational Biology Cluster Analysis Predictive Value of Tests Gene Expression Profiling Transcriptome Deep Learning Sepsis Biomarkers Prognosis Female Male Disease Susceptibility Humans Algorithms
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Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features.

BMC bioinformatics Nov 7, 2020
BACKGROUND: Autism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which are genetically complex and heterogeneous with most of the genetic risk factors also found in the unaffected general population. Although all the cur...
Humans Machine Learning Autism Spectrum Disorder Brain Transcriptome RNA, Long Noncoding Risk
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Two machine learning methods identify a metastasis-related prognostic model that predicts overall survival in medulloblastoma patients.

Aging Nov 5, 2020
Approximately 30% of medulloblastoma (MB) patients exhibit metastasis at initial diagnosis, which often leads to a poor prognosis. Here, by using univariate Cox regression analysis, two machine learning methods (Lasso-penalized Cox regression and ran...
Clinical Decision-Making Female Child Reproducibility of Results Gene Expression Profiling Humans Nomograms Risk Factors Transcriptome Databases, Genetic Machine Learning Young Adult Adolescent Predictive Value of Tests Decision Support Techniques Middle Aged Adult Child, Preschool Cerebellar Neoplasms Prognosis Risk Assessment Biomarkers, Tumor Medulloblastoma Male Infant
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