AIMC Topic: Models, Genetic

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Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes.

Genetics, selection, evolution : GSE Feb 24, 2020
BACKGROUND: Transforming large amounts of genomic data into valuable knowledge for predicting complex traits has been an important challenge for animal and plant breeders. Prediction of complex traits has not escaped the current excitement on machine...
Phenotype Animals Deep Learning Models, Genetic Bayes Theorem Quantitative Trait, Heritable Multifactorial Inheritance Cattle Genomics Polymorphism, Single Nucleotide Genotype
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ERAP1 polymorphisms interactions and their association with Behçet's disease susceptibly: Application of Model-Based Multifactor Dimension Reduction Algorithm (MB-MDR).

PloS one Feb 5, 2020
BACKGROUND: Behçet's disease (BD) is a chronic multi-systemic vasculitis with a considerable prevalence in Asian countries. There are many genes associated with a higher risk of developing BD, one of which is endoplasmic reticulum aminopeptidase-1 (E...
Male Gene Regulatory Networks Aminopeptidases Minor Histocompatibility Antigens Humans Algorithms Female Adult Behcet Syndrome Iran Gene Frequency Entropy Multifactor Dimensionality Reduction Genetic Predisposition to Disease Polymorphism, Single Nucleotide Genetic Association Studies Models, Genetic
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Polygenic risk scores outperform machine learning methods in predicting coronary artery disease status.

Genetic epidemiology Jan 10, 2020
Coronary artery disease (CAD) is the leading global cause of mortality and has substantial heritability with a polygenic architecture. Recent approaches of risk prediction were based on polygenic risk scores (PRS) not taking possible nonlinear effect...
Multifactorial Inheritance Bayes Theorem Coronary Artery Disease Benchmarking Probability Genetic Predisposition to Disease Models, Genetic Support Vector Machine Databases, Genetic Humans Machine Learning ROC Curve
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TSLRF: Two-Stage Algorithm Based on Least Angle Regression and Random Forest in genome-wide association studies.

Scientific reports Dec 2, 2019
One of the most important tasks in genome-wide association analysis (GWAS) is the detection of single-nucleotide polymorphisms (SNPs) which are related to target traits. With the development of sequencing technology, traditional statistical methods a...
Linear Models Genome, Plant Quantitative Trait Loci Arabidopsis Genomics Polymorphism, Single Nucleotide Models, Genetic Multigene Family Machine Learning Genome-Wide Association Study
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DualWMDR: Detecting epistatic interaction with dual screening and multifactor dimensionality reduction.

Human mutation Nov 25, 2019
Detecting epistatic interaction is a typical way of identifying the genetic susceptibility of complex diseases. Multifactor dimensionality reduction (MDR) is a decent solution for epistasis detection. Existing MDR-based methods still suffer from high...
Models, Genetic Databases, Genetic Genetic Loci Macular Degeneration Epistasis, Genetic Multifactor Dimensionality Reduction Algorithms Genetic Predisposition to Disease Polymorphism, Single Nucleotide Computational Biology Humans
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An improved fuzzy set-based multifactor dimensionality reduction for detecting epistasis.

Artificial intelligence in medicine Nov 22, 2019
OBJECTIVE: Epistasis identification is critical for determining susceptibility to human genetic diseases. The rapid development of technology has enabled scalability to make multifactor dimensionality reduction (MDR) measurements an effective calcula...
Humans Artificial Intelligence Algorithms Case-Control Studies Drug Resistance Genotype Epistasis, Genetic Models, Genetic Multifactor Dimensionality Reduction Fuzzy Logic Drug Resistance, Multiple
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A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine.

BMC genomics Nov 13, 2019
BACKGROUND: Recent studies have shown that long non-coding RNAs (lncRNAs) play a crucial role in the induction of cancer through epigenetic regulation, transcriptional regulation, post-transcriptional regulation and other aspects, thus participating ...
Disease-Free Survival Kaplan-Meier Estimate Prognosis Stomach Neoplasms Cohort Studies Models, Genetic Adenocarcinoma Humans Support Vector Machine RNA, Long Noncoding Microsatellite Instability
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Novel approaches for glioblastoma treatment: Focus on tumor heterogeneity, treatment resistance, and computational tools.

Cancer reports (Hoboken, N.J.) Nov 11, 2019
BACKGROUND: Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed by radiotherapy and treatment with the adjuvant temozolomide, a DNA alkylating agent. However, the a...
DNA Repair Enzymes Immune Checkpoint Inhibitors Drug Resistance, Neoplasm Precision Medicine Chemoradiotherapy, Adjuvant Microtubules Computational Biology Tumor Suppressor Proteins Humans Prognosis Clonal Evolution Cancer Vaccines Tumor Suppressor Protein p53 Antineoplastic Combined Chemotherapy Protocols Machine Learning Tumor Microenvironment DNA Modification Methylases Models, Genetic Glioblastoma Brain Brain Neoplasms DNA Mutational Analysis Mutation Genetic Heterogeneity
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Prediction of microbial communities for urban metagenomics using neural network approach.

Human genomics Oct 22, 2019
BACKGROUND: Microbes are greatly associated with human health and disease, especially in densely populated cities. It is essential to understand the microbial ecosystem in an urban environment for cities to monitor the transmission of infectious dise...
Metagenomics New York Boston Cities Models, Genetic Neural Networks, Computer Databases, Genetic Algorithms Microbiota Reproducibility of Results
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Human mitochondrial genome compression using machine learning techniques.

Human genomics Oct 22, 2019
BACKGROUND: In recent years, with the development of high-throughput genome sequencing technologies, a large amount of genome data has been generated, which has caused widespread concern about data storage and transmission costs. However, how to effe...
Databases, Genetic Base Sequence Genome, Mitochondrial Data Compression Neural Networks, Computer Machine Learning Algorithms Models, Genetic Humans
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