AIMC Topic: Phenotype
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Collective effects of long-range DNA methylations predict gene expressions and estimate phenotypes in cancer.
Scientific reports
Mar 3, 2020
DNA methylation of various genomic regions has been found to be associated with gene expression in diverse biological contexts. However, most genome-wide studies have focused on the effect of (1) methylation in cis, not in trans and (2) a single CpG,...
An Application of Machine Learning in Pharmacovigilance: Estimating Likely Patient Genotype From Phenotypical Manifestations of Fluoropyrimidine Toxicity.
Clinical pharmacology and therapeutics
Feb 27, 2020
Dihydropyrimidine dehydrogenase (DPD)-deficient patients might only become aware of their genotype after exposure to dihydropyrimidines, if testing is performed. Case reports to pharmacovigilance databases might only contain phenotypical manifestatio...
Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction.
Scientific reports
Feb 27, 2020
Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel netw...
GenEpi: gene-based epistasis discovery using machine learning.
BMC bioinformatics
Feb 24, 2020
BACKGROUND: Genome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with ...
Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes.
Genetics, selection, evolution : GSE
Feb 24, 2020
BACKGROUND: Transforming large amounts of genomic data into valuable knowledge for predicting complex traits has been an important challenge for animal and plant breeders. Prediction of complex traits has not escaped the current excitement on machine...
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.
Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp.
Scientific reports
Feb 11, 2020
Phenotypic heterogeneity is an important trait for the development and survival of many microorganisms including the yeast Cryptococcus spp., a deadly pathogen spread worldwide. Here, we have applied scanning electron microscopy (SEM) to define four ...
Machine learning models for net photosynthetic rate prediction using poplar leaf phenotype data.
PloS one
Feb 11, 2020
BACKGROUND: As an essential component in reducing anthropogenic CO2 emissions to the atmosphere, tree planting is the key to keeping carbon dioxide emissions under control. In 1992, the United Nations agreed to take action at the Earth Summit to stab...
Combining deep learning with token selection for patient phenotyping from electronic health records.
Scientific reports
Jan 29, 2020
Artificial intelligence provides the opportunity to reveal important information buried in large amounts of complex data. Electronic health records (eHRs) are a source of such big data that provide a multitude of health related clinical information a...
Depression phenotype identified by using single nucleotide exact amplicon sequence variants of the human gut microbiome.
Molecular psychiatry
Jan 27, 2020
Single nucleotide exact amplicon sequence variants (ASV) of the human gut microbiome were used to evaluate if individuals with a depression phenotype (DEPR) could be identified from healthy reference subjects (NODEP). Microbial DNA in stool samples o...
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