AIMC Topic: Phenotype

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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...
CpG Islands RNA, Untranslated Microsatellite Repeats Supranuclear Palsy, Progressive Humans Machine Learning Genetic Predisposition to Disease Case-Control Studies Phenotype Polymorphism, Single Nucleotide Cohort Studies Genetic Association Studies Child Autism Spectrum Disorder Computational Biology Gene Regulatory Networks DNA Female Logistic Models Whole Genome Sequencing Male MicroRNAs Genetic Variation
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The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Electronic phenotyping is the task of ascertaining whether an individual has a medical condition of interest by analyzing their medical record and is foundational in clinical informatics. Increasingly, electronic phenotyping is performed via supervis...
Phenotype Databases, Factual Electronic Health Records Deep Learning Computational Biology Logistic Models Medical Informatics Humans Neural Networks, Computer Machine Learning Algorithms
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Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Biomedical association studies are increasingly done using clinical concepts, and in particular diagnostic codes from clinical data repositories as phenotypes. Clinical concepts can be represented in a meaningful, vector space using word embedding mo...
Computational Biology Natural Language Processing Medical Informatics Semantics Machine Learning Phenotype Databases, Factual Deep Learning International Classification of Diseases Humans
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Ontology based text mining of gene-phenotype associations: application to candidate gene prediction.

Database : the journal of biological databases and curation Jan 1, 2019
Gene-phenotype associations play an important role in understanding the disease mechanisms which is a requirement for treatment development. A portion of gene-phenotype associations are observed mainly experimentally and made publicly available throu...
Data Mining Gene Ontology Phenotype Genetic Association Studies Databases, Genetic
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Cancer Phenotype Development: A Literature Review.

Studies in health technology and informatics Jan 1, 2019
EHR-based, computable phenotypes can be leveraged by healthcare organizations and researchers to improve the cohort identification process. The ability to identify patient cohorts using aspects of care and outcomes based on clinical characteristics o...
Humans Phenotype Natural Language Processing Delivery of Health Care Electronic Health Records Neoplasms Cohort Studies
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Computational aspects underlying genome to phenome analysis in plants.

The Plant journal : for cell and molecular biology Jan 1, 2019
Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high-throughput plant phenotyping is becoming widely adopted in the plant community, promisi...
Genetic Association Studies Plants Genome, Plant Quantitative Trait Loci Phenomics Machine Learning Phenotype Genomics
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Application of machine learning algorithms for the differential diagnosis of peroxisomal disorders.

Journal of biochemistry Jan 1, 2019
We have established diagnostic thresholds of very long-chain fatty acids (VLCFA) for the differential diagnosis of peroxisomal disorders using the machine learning tools. The plasma samples of 131 controls and 90 cases were tested for VLCFA using gas...
Adult Male Infant Case-Control Studies Phenotype Female Infant, Newborn Young Adult Fatty Acids Humans Gas Chromatography-Mass Spectrometry Machine Learning Genetic Diseases, X-Linked Adolescent Algorithms Peroxisomal Disorders Child, Preschool Phytanic Acid Child Diagnosis, Differential
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Support vector machine-based assessment of the T-wave morphology improves long QT syndrome diagnosis.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology Nov 1, 2018
AIMS: Diagnosing long QT syndrome (LQTS) is challenging due to a considerable overlap of the QTc-interval between LQTS patients and healthy controls. The aim of this study was to investigate the added value of T-wave morphology markers obtained from ...
Action Potentials Predictive Value of Tests Signal Processing, Computer-Assisted Long QT Syndrome Heart Conduction System Heart Rate Time Factors Genetic Predisposition to Disease Electrocardiography Support Vector Machine Phenotype Retrospective Studies Reproducibility of Results Risk Factors Humans
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Machine-Assisted Genotype Update System (MAGUS) for Inherited Cardiomyopathies.

Circulation. Cardiovascular quality and outcomes Oct 1, 2018
Arrhythmogenic Right Ventricular Dysplasia Cardiomyopathy, Hypertrophic, Familial Heredity Reproducibility of Results Computational Biology Genetic Variation Humans Risk Factors Predictive Value of Tests Machine Learning High-Throughput Nucleotide Sequencing Risk Assessment Genetic Testing Phenotype Pedigree Genetic Predisposition to Disease Data Mining Prognosis Cardiomyopathy, Dilated
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Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2018
We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to i...
Machine Learning Phenotype Blood Glucose Insulin Diabetes Mellitus, Type 2 Regression Analysis Bayes Theorem Models, Biological Humans Data Mining Blood Glucose Self-Monitoring Normal Distribution
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