Quantitative Trait Loci
Showing 61 to 70 of 98 articles
Improving eQTL Analysis Using a Machine Learning Approach for Data Integration: A Logistic Model Tree Solution.
Journal of computational biology : a journal of computational molecular cell biology
Jul 27, 2018
Expression quantitative trait loci (eQTL) analysis is an emerging method for establishing the impact of genetic variations (such as single nucleotide polymorphisms) on the expression levels of genes. Although different methods for evaluating the impa...
Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.
Nature genetics
Jul 16, 2018
Key challenges for human genetics, precision medicine and evolutionary biology include deciphering the regulatory code of gene expression and understanding the transcriptional effects of genome variation. However, this is extremely difficult because ...
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...
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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...
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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...
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