AIMC Topic: Gene Ontology
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Database Mining of Genes of Prognostic Value for the Prostate Adenocarcinoma Microenvironment Using the Cancer Gene Atlas.
BioMed research international
May 18, 2020
BACKGROUND: Prostate adenocarcinoma (PRAD) is a common malignant tumor in elderly men. Our research uses The Cancer Gene Atlas (TCGA) database to find potential related genes for predicting the prognosis of patients with PRAD.
Fold-Change-Specific Enrichment Analysis (FSEA): Quantification of Transcriptional Response Magnitude for Functional Gene Groups.
Genes
Apr 17, 2020
Gene expression profiling data contains more information than is routinely extracted with standard approaches. Here we present Fold-Change-Specific Enrichment Analysis (FSEA), a new method for functional annotation of differentially expressed genes f...
GOMCL: a toolkit to cluster, evaluate, and extract non-redundant associations of Gene Ontology-based functions.
BMC bioinformatics
Apr 10, 2020
BACKGROUND: Functional enrichment of genes and pathways based on Gene Ontology (GO) has been widely used to describe the results of various -omics analyses. GO terms statistically overrepresented within a set of a large number of genes are typically ...
Weighted gene co-expression network analysis reveals specific modules and biomarkers in Parkinson's disease.
Neuroscience letters
Apr 8, 2020
BACKGROUND: Parkinson's disease (PD) ranks as the second most frequently occurring neurodegenerative disease. The precise pathogenic mechanism of this disease remains unknown. The aim of the present study was to identify the biomarkers in PD and clas...
Improving lipid mapping in Genome Scale Metabolic Networks using ontologies.
Metabolomics : Official journal of the Metabolomic Society
Mar 25, 2020
INTRODUCTION: To interpret metabolomic and lipidomic profiles, it is necessary to identify the metabolic reactions that connect the measured molecules. This can be achieved by putting them in the context of genome-scale metabolic network reconstructi...
Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning.
Scientific reports
Mar 5, 2020
Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients. However, despite considerable efforts and progress, there is a need ...
Supervised Machine Learning
Datasets as Topic
Female
Carcinoma, Ductal, Breast
Reproducibility of Results
RNA, Neoplasm
Breast Neoplasms
Humans
Microarray Analysis
Transcriptome
Machine Learning
Gene Ontology
Early Detection of Cancer
Algorithms
Neoplasm Staging
Protein Interaction Maps
Models, Biological
RNA-Seq
Databases, Genetic
Computational Identification and Analysis of Ubiquinone-Binding Proteins.
Cells
Feb 24, 2020
Ubiquinone is an important cofactor that plays vital and diverse roles in many biological processes. Ubiquinone-binding proteins (UBPs) are receptor proteins that dock with ubiquinones. Analyzing and identifying UBPs via a computational approach will...
A genome-wide multiphenotypic association analysis identified candidate genes and gene ontology shared by four common risky behaviors.
Aging
Feb 22, 2020
BACKGROUND: Risky behaviors can lead to huge economic and health losses. However, limited efforts are paid to explore the genetic mechanisms of risky behaviors.
Genome-wide inference of the Camponotus floridanus protein-protein interaction network using homologous mapping and interacting domain profile pairs.
Scientific reports
Feb 11, 2020
Apart from some model organisms, the interactome of most organisms is largely unidentified. High-throughput experimental techniques to determine protein-protein interactions (PPIs) are resource intensive and highly susceptible to noise. Computational...
Combined Use of Three Machine Learning Modeling Methods to Develop a Ten-Gene Signature for the Diagnosis of Ventilator-Associated Pneumonia.
Medical science monitor : international medical journal of experimental and clinical research
Feb 7, 2020
BACKGROUND This study aimed to use three modeling methods, logistic regression analysis, random forest analysis, and fully-connected neural network analysis, to develop a diagnostic gene signature for the diagnosis of ventilator-associated pneumonia ...
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