AIMC Topic: Quantitative Trait Loci
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A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets.
BMC bioinformatics
Mar 27, 2018
BACKGROUND: Testing predefined gene categories has become a common practice for scientists analyzing high throughput transcriptome data. A systematic way of testing gene categories leads to testing hundreds of null hypotheses that correspond to nodes...
The R-package GenomicTools for multifactor dimensionality reduction and the analysis of (exploratory) Quantitative Trait Loci.
Computer methods and programs in biomedicine
Aug 30, 2017
BACKGROUND AND OBJECTIVES: We introduce the R-package GenomicTools to perform, among others, a Multifactor Dimensionality Reduction (MDR) for the identification of SNP-SNP interactions. The package further provides a new class of tests for an (explor...
Multiple Trait Covariance Association Test Identifies Gene Ontology Categories Associated with Chill Coma Recovery Time in Drosophila melanogaster.
Scientific reports
May 25, 2017
The genomic best linear unbiased prediction (GBLUP) model has proven to be useful for prediction of complex traits as well as estimation of population genetic parameters. Improved inference and prediction accuracy of GBLUP may be achieved by identify...
Computer vision and machine learning for robust phenotyping in genome-wide studies.
Scientific reports
Mar 8, 2017
Traditional evaluation of crop biotic and abiotic stresses are time-consuming and labor-intensive limiting the ability to dissect the genetic basis of quantitative traits. A machine learning (ML)-enabled image-phenotyping pipeline for the genetic stu...
phenoSeeder - A Robot System for Automated Handling and Phenotyping of Individual Seeds.
Plant physiology
Sep 23, 2016
The enormous diversity of seed traits is an intriguing feature and critical for the overwhelming success of higher plants. In particular, seed mass is generally regarded to be key for seedling development but is mostly approximated by using scanning ...
Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster.
Genetics
May 27, 2016
Predicting individual quantitative trait phenotypes from high-resolution genomic polymorphism data is important for personalized medicine in humans, plant and animal breeding, and adaptive evolution. However, this is difficult for populations of unre...
DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.
Nucleic acids research
Apr 15, 2016
Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of fun...
Identification of active transcriptional regulatory elements from GRO-seq data.
Nature methods
Mar 23, 2015
Modifications to the global run-on and sequencing (GRO-seq) protocol that enrich for 5'-capped RNAs can be used to reveal active transcriptional regulatory elements (TREs) with high accuracy. Here, we introduce discriminative regulatory-element detec...
RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease.
Science (New York, N.Y.)
Dec 18, 2014
To facilitate precision medicine and whole-genome annotation, we developed a machine-learning technique that scores how strongly genetic variants affect RNA splicing, whose alteration contributes to many diseases. Analysis of more than 650,000 intron...
Colorectal Neoplasms, Hereditary Nonpolyposis
Nuclear Proteins
MutL Protein Homolog 1
Muscular Atrophy, Spinal
Quantitative Trait Loci
Adaptor Proteins, Signal Transducing
Genome-Wide Association Study
RNA Splice Sites
Exons
RNA Splicing
DNA
Polymorphism, Single Nucleotide
Genetic Variation
Genetic Code
Child Development Disorders, Pervasive
Genetic Markers
Humans
RNA-Binding Proteins
Molecular Sequence Annotation
Models, Genetic
Artificial Intelligence
Introns
Computer Simulation
Mutation, Missense
Integrative exome sequencing and machine learning identify MICB and interferon pathway genes as contributors to SSc risk.
Annals of the rheumatic diseases
Aug 1, 2025
OBJECTIVES: Systemic sclerosis (SSc) is a complex autoimmune disease with both known and unidentified genetic contributors. While genome-wide association studies (GWAS) have implicated multiple loci, many reside in noncoding regions. We aimed to iden...
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