AIMC Topic: Genetic Markers

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Machine Learning to Predict Anti-Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers.

Arthritis & rheumatology (Hoboken, N.J.) Nov 4, 2019
OBJECTIVE: Accurate prediction of treatment responses in rheumatoid arthritis (RA) patients can provide valuable information on effective drug selection. Anti-tumor necrosis factor (anti-TNF) drugs are an important second-line treatment after methotr...
Severity of Illness Index Arthritis, Rheumatoid Polymorphism, Single Nucleotide Treatment Outcome Regression Analysis Female Reproducibility of Results Male Middle Aged Outcome Assessment, Health Care Antirheumatic Agents Normal Distribution Etanercept Certolizumab Pegol Predictive Value of Tests Infliximab Genetic Markers Methotrexate Adalimumab Humans Machine Learning Tumor Necrosis Factor-alpha
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A machine learning approach for the identification of population-informative markers from high-throughput genotyping data: application to several pig breeds.

Animal : an international journal of animal bioscience Oct 11, 2019
Single nucleotide polymorphisms (SNPs) able to describe population differences can be used for important applications in livestock, including breed assignment of individual animals, authentication of mono-breed products and parentage verification amo...
Genotype Animals Italy Machine Learning Breeding Male Phenotype Genetic Markers Swine Oligonucleotide Array Sequence Analysis Female Genotyping Techniques Genomics Polymorphism, Single Nucleotide
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Characterizing Alzheimer's Disease With Image and Genetic Biomarkers Using Supervised Topic Models.

IEEE journal of biomedical and health informatics Jul 31, 2019
Neuroimaging and genetic biomarkers have been widely studied from discriminative perspectives towards Alzheimer's disease (AD) classification, since neuroanatomical patterns and genetic variants are jointly critical indicators for AD diagnosis. Gener...
Humans Diagnosis, Computer-Assisted Magnetic Resonance Imaging Genetic Markers Supervised Machine Learning Alzheimer Disease Algorithms Female Aged Aged, 80 and over Male Neuroimaging
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Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.

Cardiovascular diabetology Jun 11, 2019
BACKGROUND: Diabetes mellitus is a chronic disease that impacts an increasing percentage of people each year. Among its comorbidities, diabetics are two to four times more likely to develop cardiovascular diseases. While HbA1c remains the primary dia...
CpG Islands Systems Integration Mitochondria, Heart Diabetic Cardiomyopathies DNA Methylation Glycated Hemoglobin Genetic Markers DNA, Mitochondrial Risk Assessment Disease Progression Diabetes Mellitus, Type 2 Genomics Genetic Predisposition to Disease Polymorphism, Single Nucleotide Epigenesis, Genetic Models, Genetic Support Vector Machine Prognosis Female Risk Factors Male Middle Aged Humans
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A directed learning strategy integrating multiple omic data improves genomic prediction.

Plant biotechnology journal Apr 14, 2019
Genomic prediction (GP) aims to construct a statistical model for predicting phenotypes using genome-wide markers and is a promising strategy for accelerating molecular plant breeding. However, current progress of phenotype prediction using genomic d...
Genetic Markers Supervised Machine Learning Phenotype Quantitative Trait Loci Transcriptome Genomics Polymorphism, Single Nucleotide Models, Genetic Models, Statistical Metabolome Oryza
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Gene ontology enrichment analysis of congenital diaphragmatic hernia-associated genes.

Pediatric research Sep 25, 2018
Congenital diaphragmatic hernia (CDH) is a commonly occurring major congenital anomaly with a profound impact on neonatal mortality. The etiology of CDH is poorly understood and is complicated by multiple clinical presentations, reflecting the locati...
Humans Phenotype Gene Regulatory Networks Genetic Predisposition to Disease Signal Transduction Genetic Markers Gene Expression Regulation Gene Ontology Hernias, Diaphragmatic, Congenital
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Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification.

BMC bioinformatics Sep 18, 2018
BACKGROUND: Quantitative traits or continuous outcomes related to complex diseases can provide more information and therefore more accurate analysis for identifying gene-gene and gene- environment interactions associated with complex diseases. Multif...
Genetic Markers Epistasis, Genetic Mice Polymorphism, Single Nucleotide Computational Biology Humans Models, Genetic Phenotype Fuzzy Logic Female Animals
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A heuristic method for fast and accurate phasing and imputation of single-nucleotide polymorphism data in bi-parental plant populations.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik Aug 4, 2018
Key message New fast and accurate method for phasing and imputation of SNP chip genotypes within diploid bi-parental plant populations. This paper presents a new heuristic method for phasing and imputation of genomic data in diploid plant species. Ou...
Plants Alleles Haplotypes Computer Heuristics Software Plant Breeding Computer Simulation Genomics Polymorphism, Single Nucleotide Genotype Genetic Markers
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Data Mining and Machine Learning Algorithms Using IL28B Genotype and Biochemical Markers Best Predicted Advanced Liver Fibrosis in Chronic Hepatitis C.

Japanese journal of infectious diseases Dec 26, 2017
IL28B single nucleotide polymorphism (rs12979860) is an etiology-independent predictor of hepatitis C virus (HCV)-related hepatic fibrosis. Data mining is a method of predictive analysis which can explore tremendous volumes of information from health...
Retrospective Studies Female Male Middle Aged Data Mining Genetic Predisposition to Disease Polymorphism, Single Nucleotide Humans Hepatitis C, Chronic Liver Cirrhosis Machine Learning Interferons Interleukins Algorithms Genetic Markers
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An algorithm for the classification of mRNA patterns in eosinophilic esophagitis: Integration of machine learning.

The Journal of allergy and clinical immunology Dec 19, 2017
BACKGROUND: Diagnostic evaluation of eosinophilic esophagitis (EoE) remains difficult, particularly the assessment of the patient's allergic status.
Immunohistochemistry Child, Preschool Child Decision Support Systems, Clinical Female RNA, Messenger Factor Analysis, Statistical Sensitivity and Specificity Single-Blind Method Male Humans Registries Infant Eosinophilic Esophagitis Machine Learning Adolescent Genetic Markers Algorithms Decision Support Techniques
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