AIMC Topic: Gene Expression
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Seeking for potential pathogenic genes of major depressive disorder in the Gene Expression Omnibus database.
Asia-Pacific psychiatry : official journal of the Pacific Rim College of Psychiatrists
Dec 30, 2019
INTRODUCTION: Major depressive disorder (MDD) is one of the most common mental disorders worldwide. The aim of this study was to identify potential pathological genes in MDD.
Construction of arming Yarrowia lipolytica surface-displaying soybean seed coat peroxidase for use as whole-cell biocatalyst.
Enzyme and microbial technology
Dec 20, 2019
Whole-cell biocatalysts could be used in wide-ranging applications. In this study, a new kind of whole-cell biocatalyst was successfully constructed by genetically immobilizing soybean seed coat peroxidase (SBP) on the cell surface of Yarrowia lipoly...
High-level expression of Thermobifida fusca glucose isomerase for high fructose corn syrup biosynthesis.
Enzyme and microbial technology
Dec 13, 2019
Glucose isomerase (GIase), an efficient enzyme in the isomerization of d-glucose to d-fructose, has been widely used in food processing. In this study, an efficient expression system for a Thermobifida fusca GIase (GIase) in Escherichia coli was firs...
Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization.
Nature communications
Nov 28, 2019
Gene expression is controlled by many simultaneous interactions, frequently measured collectively in biology and medicine by high-throughput technologies. It is a highly challenging task to infer from these data the generating effects and cooperating...
Ranking of non-coding pathogenic variants and putative essential regions of the human genome.
Nature communications
Nov 20, 2019
A gene is considered essential if loss of function results in loss of viability, fitness or in disease. This concept is well established for coding genes; however, non-coding regions are thought less likely to be determinants of critical functions. H...
D-GPM: A Deep Learning Method for Gene Promoter Methylation Inference.
Genes
Oct 14, 2019
Whole-genome bisulfite sequencing generates a comprehensive profiling of the gene methylation levels, but is limited by a high cost. Recent studies have partitioned the genes into landmark genes and target genes and suggested that the landmark gene e...
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...
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...
C-HMOSHSSA: Gene selection for cancer classification using multi-objective meta-heuristic and machine learning methods.
Computer methods and programs in biomedicine
Jun 29, 2019
BACKGROUND AND OBJECTIVE: Over the last two decades, DNA microarray technology has emerged as a powerful tool for early cancer detection and prevention. It helps to provide a detailed overview of disease complex microenvironment. Moreover, online ava...
Oligonucleotide Array Sequence Analysis
Predatory Behavior
Social Behavior
Support Vector Machine
Software
Early Detection of Cancer
Algorithms
Gene Expression
Databases, Factual
Heuristics
Computer Simulation
Biomarkers, Tumor
Humans
Animals
Machine Learning
Male
Neoplasms
Female
Computational Biology
Reproducibility of Results
Sensitivity and Specificity
Tumor Microenvironment
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 ...
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