AIMC Topic: Transcriptome

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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...
Algorithms Gene Expression Profiling Transcriptome Gene Regulatory Networks Humans Machine Learning Lung Neoplasms Transcription Factors Enzymes
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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 Plant Leaves Gene Expression Profiling Gene Expression Regulation, Plant Plant Roots Droughts Transcriptome Lolium Dehydration Genotype 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...
Machine Learning Algorithms Computer Simulation Animals Humans Phagocytes Single-Cell Analysis Mice Computational Biology Species Specificity Leukocytes, Mononuclear Gene Expression Profiling Transcriptome
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Wx: a neural network-based feature selection algorithm for transcriptomic data.

Scientific reports Jul 19, 2019
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. ...
High-Throughput Nucleotide Sequencing Datasets as Topic RNA, Neoplasm Genes, Neoplasm Humans Neural Networks, Computer Algorithms Biomarkers, Tumor Neoplasms Gene Expression Profiling Transcriptome
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Machine learning approaches to predict lupus disease activity from gene expression data.

Scientific reports Jul 3, 2019
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...
Humans Machine Learning Disease Progression Gene Expression Profiling Gene Expression Lupus Erythematosus, Systemic Gene Expression Regulation Molecular Sequence Annotation Transcriptome ROC Curve Gene Regulatory Networks Computational Biology
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A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation.

Cell Jun 6, 2019
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 ...
3' Untranslated Regions RNA Cleavage Models, Genetic Databases, Genetic Gene Expression Synthetic Biology HEK293 Cells Base Sequence Mutagenesis Polyadenylation Transcriptome Deep Learning RNA-Seq RNA, Messenger Humans
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Type-2 Fuzzy PCA Approach in Extracting Salient Features for Molecular Cancer Diagnostics and Prognostics.

IEEE transactions on nanobioscience May 20, 2019
Machine learning is becoming a powerful tool for cancer diagnosis and prognosis based on classification using high dimensional molecular data. However, extracting classification features from high-dimensional datasets remains a challenging problem. P...
Fuzzy Logic Principal Component Analysis Humans Machine Learning Prognosis Neoplasms Gene Expression Profiling Transcriptome Computational Biology Databases, Genetic
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Attention-Based Multi-NMF Deep Neural Network with Multimodality Data for Breast Cancer Prognosis Model.

BioMed research international May 13, 2019
Today, it has become a hot issue in cancer research to make precise prognostic prediction for breast cancer patients, which can not only effectively avoid overtreatment and medical resources waste, but also provide scientific basis to help medical st...
Databases, Nucleic Acid Humans Breast Neoplasms Neural Networks, Computer Gene Expression Profiling Algorithms Transcriptome Female Gene Expression Regulation, Neoplastic
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Machine-learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time.

Journal of cellular and molecular medicine Apr 18, 2019
BACKGROUND: This study aimed to examine multi-dimensional MRI features' predictability on survival outcome and associations with differentially expressed Genes (RNA Sequencing) in groups of glioblastoma multiforme (GBM) patients.
Genomics Gene Expression Regulation, Neoplastic Magnetic Resonance Imaging Glioblastoma Humans Gene Expression Profiling Machine Learning Radiographic Image Interpretation, Computer-Assisted Transcriptome Algorithms Adolescent Brain Neoplasms Prognosis Young Adult ROC Curve Male Female Adult Middle Aged Aged Survival Rate Aged, 80 and over Biomarkers, Tumor
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A directed learning strategy integrating multiple omic data improves genomic prediction.

Plant biotechnology journal Apr 14, 2019
Genomic prediction (GP) aims to construct a statistical model for predicting phenotypes using genome-wide markers and is a promising strategy for accelerating molecular plant breeding. However, current progress of phenotype prediction using genomic d...
Phenotype Transcriptome Genomics Oryza Polymorphism, Single Nucleotide Genetic Markers Models, Genetic Supervised Machine Learning Models, Statistical Quantitative Trait Loci Metabolome
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