AIMC Topic: Transcriptome

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Global transcriptome analysis reveals relevant effects at environmental concentrations of cypermethrin in honey bees (Apis mellifera).

Environmental pollution (Barking, Essex : 1987) Dec 4, 2019
Cypermethrin is a frequently used insecticide in agriculture and households but its chronic and molecular effects are poorly known are . Here we describe effects of sublethal cypermethrin exposure on the global transcriptome in the brain of honey bee...
Environmental Pollutants Genetic Markers Insecticides Reverse Transcriptase Polymerase Chain Reaction Bees RNA-Seq Pyrethrins RNA, Messenger Sequence Analysis, RNA Animals Gene Expression Profiling Transcriptome
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CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data.

PLoS computational biology Dec 2, 2019
Quantifying cell-type proportions and their corresponding gene expression profiles in tissue samples would enhance understanding of the contributions of individual cell types to the physiological states of the tissue. Current approaches that address ...
Humans Computer Simulation Gene Expression Profiling Unsupervised Machine Learning Leukocytes Pattern Recognition, Automated Databases, Nucleic Acid Transcriptome Computational Biology Sequence Analysis, RNA Cell Line
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Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis.

International journal of molecular sciences Nov 20, 2019
UNLABELLED: Personalized medicine relies on the integration and consideration of specific characteristics of the patient, such as tumor phenotypic and genotypic profiling.
Gene Regulatory Networks Magnetic Resonance Imaging MicroRNAs Female Breast Neoplasms Biomarkers, Tumor Transcriptome Humans Machine Learning
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Four transcription profile-based models identify novel prognostic signatures in oesophageal cancer.

Journal of cellular and molecular medicine Nov 19, 2019
Oesophageal cancer (ESCA) is a clinically challenging disease with poor prognosis and health-related quality of life. Here, we investigated the transcriptome of ESCA to identify high risk-related signatures. A total of 159 ESCA patients of The Cancer...
Humans Survival Rate Biomarkers, Tumor Male Middle Aged Machine Learning Algorithms Prognosis Female Esophageal Neoplasms Esophageal Squamous Cell Carcinoma Tumor Cells, Cultured Transcriptome Gene Expression Regulation, Neoplastic Cell Proliferation Apoptosis
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DeePathology: Deep Multi-Task Learning for Inferring Molecular Pathology from Cancer Transcriptome.

Scientific reports Nov 11, 2019
Despite great advances, molecular cancer pathology is often limited to the use of a small number of biomarkers rather than the whole transcriptome, partly due to computational challenges. Here, we introduce a novel architecture of Deep Neural Network...
Pathology, Molecular Gene Expression Regulation, Neoplastic Data Mining Disease Susceptibility Organ Specificity Neural Networks, Computer Algorithms Reproducibility of Results Neoplasms Gene Expression Profiling Humans Transcriptome Deep Learning Computational Biology
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Molecular expression profiles of morphologically defined hippocampal neuron types: Empirical evidence and relational inferences.

Hippocampus Oct 9, 2019
Gene and protein expressions are key determinants of cellular function. Neurons are the building blocks of brain circuits, yet the relationship between their molecular identity and the spatial distribution of their dendritic inputs and axonal outputs...
Data Mining Hippocampus Empirical Research Transcriptome Neurons Knowledge Bases Humans
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Identification of a blood-based 12-gene signature that predicts the severity of coronary artery stenosis: An integrative approach based on gene network construction, Support Vector Machine algorithm, and multi-cohort validation.

Atherosclerosis Oct 9, 2019
BACKGROUND AND AIMS: We aimed to identify a blood-based gene expression score (GES) to predict the severity of coronary artery stenosis in patients with known or suspected coronary artery disease (CAD) by integrative use of gene network construction,...
Humans Decision Support Techniques Phenotype Retrospective Studies Female China Gene Regulatory Networks Middle Aged Genetic Predisposition to Disease Severity of Illness Index Nomograms Support Vector Machine Reproducibility of Results Gene Expression Profiling Predictive Value of Tests Transcriptome Risk Factors Coronary Stenosis Aged Male
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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...
Machine Learning Algorithms Neoplasms Transcriptome RNA-Seq Humans Oligonucleotide Array Sequence Analysis
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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...
Hematopoietic Stem Cells Transcriptome Mice Sequence Analysis, RNA Mice, Knockout Machine Learning Animals Gene Expression Profiling
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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.
Humans Kaplan-Meier Estimate Machine Learning Prognosis Databases, Genetic Genes, Neoplasm Male Disease-Free Survival Lung Neoplasms Adenocarcinoma of Lung Female Middle Aged Gene Expression Regulation, Neoplastic Proportional Hazards Models Aged Disease Progression Follow-Up Studies Multivariate Analysis Transcriptome Survival Rate Biomarkers, Tumor
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