AIMC Topic: Polymorphism, Single Nucleotide

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Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing.

Clinical chemistry Jan 1, 2020
BACKGROUND: Molecular profiling has become essential for tumor risk stratification and treatment selection. However, cancer genome complexity and technical artifacts make identification of real variants a challenge. Currently, clinical laboratories r...
Neoplasms Polymorphism, Single Nucleotide High-Throughput Nucleotide Sequencing False Positive Reactions Humans Machine Learning Sensitivity and Specificity
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Convolutional neural network model to predict causal risk factors that share complex regulatory features.

Nucleic acids research Dec 16, 2019
Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of >2000 functional fe...
Neural Networks, Computer Genome-Wide Association Study Regulatory Sequences, Nucleic Acid Autoimmune Diseases Mental Disorders Humans Genetic Predisposition to Disease Quantitative Trait Loci Risk Factors Polymorphism, Single Nucleotide
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Identification of disease-associated loci using machine learning for genotype and network data integration.

Bioinformatics (Oxford, England) Dec 15, 2019
MOTIVATION: Integration of different omics data could markedly help to identify biological signatures, understand the missing heritability of complex diseases and ultimately achieve personalized medicine. Standard regression models used in Genome-Wid...
Gene Regulatory Networks Polymorphism, Single Nucleotide Genotype Machine Learning Phenotype Genome-Wide Association Study Humans
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High-throughput multimodal automated phenotyping (MAP) with application to PheWAS.

Journal of the American Medical Informatics Association : JAMIA Nov 1, 2019
OBJECTIVE: Electronic health records linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. The objective of this study was to develop ...
Electronic Health Records Polymorphism, Single Nucleotide Natural Language Processing Area Under Curve Humans Algorithms Phenotype International Classification of Diseases Unified Medical Language System
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Integration of machine learning and pharmacogenomic biomarkers for predicting response to antidepressant treatment: can computational intelligence be used to augment clinical assessments?

Pharmacogenomics Sep 1, 2019
Polymorphism, Single Nucleotide Antidepressive Agents Depressive Disorder Algorithms Biomarkers Pharmacogenetics Humans Artificial Intelligence Machine Learning
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TAGOOS: genome-wide supervised learning of non-coding loci associated to complex phenotypes.

Nucleic acids research Aug 22, 2019
Genome-wide association studies (GWAS) associate single nucleotide polymorphisms (SNPs) to complex phenotypes. Most human SNPs fall in non-coding regions and are likely regulatory SNPs, but linkage disequilibrium (LD) blocks make it difficult to dist...
Quantitative Trait Loci Linkage Disequilibrium Regulatory Sequences, Nucleic Acid Phenotype Internet Genome-Wide Association Study Supervised Machine Learning Humans Gene Expression Regulation Genetic Predisposition to Disease Polymorphism, Single Nucleotide Computational Biology
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EnsembleCNV: an ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data.

Nucleic acids research Apr 23, 2019
The associations between diseases/traits and copy number variants (CNVs) have not been systematically investigated in genome-wide association studies (GWASs), primarily due to a lack of robust and accurate tools for CNV genotyping. Herein, we propose...
Quality Control Datasets as Topic Genome, Human Genotyping Techniques Machine Learning Humans Polymorphism, Single Nucleotide DNA Copy Number Variations
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Machine Learning as an Effective Method for Identifying True Single Nucleotide Polymorphisms in Polyploid Plants.

The plant genome Mar 1, 2019
Single nucleotide polymorphisms (SNPs) have many advantages as molecular markers since they are ubiquitous and codominant. However, the discovery of true SNPs in polyploid species is difficult. Peanut ( L.) is an allopolyploid, which has a very low r...
Models, Genetic Datasets as Topic Machine Learning Polymorphism, Single Nucleotide Arachis Polyploidy
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Outgroup Machine Learning Approach Identifies Single Nucleotide Variants in Noncoding DNA Associated with Autism Spectrum Disorder.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Autism spectrum disorder (ASD) is a heritable neurodevelopmental disorder affecting 1 in 59 children. While noncoding genetic variation has been shown to play a major role in many complex disorders, the contribution of these regions to ASD susceptibi...
MicroRNAs DNA Whole Genome Sequencing Genetic Association Studies Computational Biology Logistic Models Genetic Variation Microsatellite Repeats Autism Spectrum Disorder Humans Case-Control Studies CpG Islands Supranuclear Palsy, Progressive Female Machine Learning RNA, Untranslated Cohort Studies Male Phenotype Gene Regulatory Networks Child Genetic Predisposition to Disease Polymorphism, Single Nucleotide
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Res2s2aM: Deep residual network-based model for identifying functional noncoding SNPs in trait-associated regions.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Noncoding single nucleotide polymorphisms (SNPs) and their target genes are important components of the heritability of diseases and other polygenic traits. Identifying these SNPs and target genes could potentially reveal new molecular mechanisms and...
Sequence Analysis, DNA Molecular Sequence Annotation Databases, Nucleic Acid Polymorphism, Single Nucleotide Humans Computational Biology Neural Networks, Computer Models, Genetic Algorithms Deep Learning Genome-Wide Association Study
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