AIMC Topic: Genome-Wide Association Study

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Porcine single nucleotide polymorphisms and their functional effect: an update.

BMC research notes Dec 4, 2018
OBJECTIVE: To aid in the development of a comprehensive list of functional variants in the swine genome, single nucleotide polymorphisms (SNP) were identified from whole genome sequence of 240 pigs. Interim data from 72 animals in this study was publ...
Genome-Wide Association Study Polymorphism, Single Nucleotide DNA Animals Male Swine Gene Ontology Genome Tail Untranslated Regions Semen High-Throughput Nucleotide Sequencing Molecular Sequence Annotation
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Deep learning identifies genome-wide DNA binding sites of long noncoding RNAs.

RNA biology Nov 28, 2018
Long noncoding RNAs (lncRNAs) can exert their function by interacting with the DNA via triplex structure formation. Even though this has been validated with a handful of experiments, a genome-wide analysis of lncRNA-DNA binding is needed. In this pap...
RNA, Long Noncoding DNA Gene Expression Regulation Binding Sites Deep Learning Humans Genome-Wide Association Study Reproducibility of Results Mice Animals
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Introducing Heuristic Information Into Ant Colony Optimization Algorithm for Identifying Epistasis.

IEEE/ACM transactions on computational biology and bioinformatics Nov 5, 2018
Epistasis learning, which is aimed at detecting associations between multiple Single Nucleotide Polymorphisms (SNPs) and complex diseases, has gained increasing attention in genome wide association studies. Although much work has been done on mapping...
Algorithms Genome-Wide Association Study Macular Degeneration Humans Computational Biology Epistasis, Genetic Computer Heuristics
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Novel Neural Network Approach to Predict Drug-Target Interactions Based on Drug Side Effects and Genome-Wide Association Studies.

Human heredity Oct 22, 2018
AIMS: We propose a novel machine learning approach to expand the knowledge about drug-target interactions. Our method may help to develop effective, less harmful treatment strategies and to enable the detection of novel indications for existing drugs...
Drug-Related Side Effects and Adverse Reactions Machine Learning Genome-Wide Association Study Drug Interactions Humans Neural Networks, Computer
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Data mining and machine learning approaches for the integration of genome-wide association and methylation data: methodology and main conclusions from GAW20.

BMC genetics Sep 17, 2018
BACKGROUND: Multiple layers of genetic and epigenetic variability are being simultaneously explored in an increasing number of health studies. We summarize here different approaches applied in the Data Mining and Machine Learning group at the GAW20 t...
Humans Machine Learning Phenotype Genome-Wide Association Study Data Mining DNA Methylation CpG Islands Cluster Analysis
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Using recursive feature elimination in random forest to account for correlated variables in high dimensional data.

BMC genetics Sep 17, 2018
BACKGROUND: Random forest (RF) is a machine-learning method that generally works well with high-dimensional problems and allows for nonlinear relationships between predictors; however, the presence of correlated predictors has been shown to impact it...
Genotype Epigenomics Hypoglycemic Agents Triglycerides Hypertriglyceridemia DNA Methylation CpG Islands Humans Machine Learning Genome-Wide Association Study Polymorphism, Single Nucleotide
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Utilizing Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women.

IEEE/ACM transactions on computational biology and bioinformatics Sep 3, 2018
Genome-Wide Association Studies (GWAS) are used to identify statistically significant genetic variants in case-control studies. The main objective is to find single nucleotide polymorphisms (SNPs) that influence a particular phenotype (i.e., disease ...
Deep Learning Genome-Wide Association Study Polymorphism, Single Nucleotide Computational Biology Black or African American Epistasis, Genetic Female Infant, Newborn Premature Birth Humans Algorithms Pregnancy
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Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls.

Scientific reports Sep 3, 2018
We propose an effective machine learning approach to identify group of interacting single nucleotide polymorphisms (SNPs), which contribute most to the breast cancer (BC) risk by assuming dependencies among BCAC iCOGS SNPs. We adopt a gradient tree b...
Breast Neoplasms Case-Control Studies Genome-Wide Association Study Humans Prognosis Female Gene Expression Regulation, Neoplastic Support Vector Machine Base Sequence Genome, Human Gene Regulatory Networks Genetic Predisposition to Disease Polymorphism, Single Nucleotide Protein Interaction Mapping Estrogen Receptor alpha Risk Ubiquitin-Protein Ligases Finland
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MCRiceRepGP: a framework for the identification of genes associated with sexual reproduction in rice.

The Plant journal : for cell and molecular biology Aug 16, 2018
Rice is an important cereal crop, being a staple food for over half of the world's population, and sexual reproduction resulting in grain formation underpins global food security. However, despite considerable research efforts, many of the genes, esp...
Reproduction Genome, Plant Genes, Plant Transcriptome Genome-Wide Association Study Oryza Machine Learning
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A deep convolutional neural network approach for predicting phenotypes from genotypes.

Planta Aug 12, 2018
Deep learning is a promising technology to accurately select individuals with high phenotypic values based on genotypic data. Genomic selection (GS) is a promising breeding strategy by which the phenotypes of plant individuals are usually predicted b...
Neural Networks, Computer Plants Machine Learning Genome-Wide Association Study Selection, Genetic Genetic Association Studies Models, Genetic
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