AIMC Topic: Phenotype

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Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Phenotyping algorithms applied to electronic health record (EHR) data enable investigators to identify large cohorts for clinical and genomic research. Algorithm development is often iterative, depends on fallible investigator intuition, a...

Design of Biomedical Robots for Phenotype Prediction Problems.

Journal of computational biology : a journal of computational molecular cell biology
Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer and neurodegenerative and rare diseases in the quest for orphan drugs. To improve its utility, accur...

Machine learning based methodology to identify cell shape phenotypes associated with microenvironmental cues.

Biomaterials
Cell morphology has been identified as a potential indicator of stem cell response to biomaterials. However, determination of cell shape phenotype in biomaterials is complicated by heterogeneous cell populations, microenvironment heterogeneity, and m...

Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.

Artificial intelligence in medicine
OBJECTIVE: The combination of phenomic data from electronic health records (EHR) and clinical data repositories with dense biological data has enabled genomic and pharmacogenomic discovery, a first step toward precision medicine. Computational method...

Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster.

Genetics
Predicting individual quantitative trait phenotypes from high-resolution genomic polymorphism data is important for personalized medicine in humans, plant and animal breeding, and adaptive evolution. However, this is difficult for populations of unre...

The cellular microscopy phenotype ontology.

Journal of biomedical semantics
BACKGROUND: Phenotypic data derived from high content screening is currently annotated using free-text, thus preventing the integration of independent datasets, including those generated in different biological domains, such as cell lines, mouse and ...

Learning statistical models of phenotypes using noisy labeled training data.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Traditionally, patient groups with a phenotype are selected through rule-based definitions whose creation and validation are time-consuming. Machine learning approaches to electronic phenotyping are limited by the paucity of labeled traini...

Extracting a stroke phenotype risk factor from Veteran Health Administration clinical reports: an information content analysis.

Journal of biomedical semantics
BACKGROUND: In the United States, 795,000 people suffer strokes each year; 10-15 % of these strokes can be attributed to stenosis caused by plaque in the carotid artery, a major stroke phenotype risk factor. Studies comparing treatments for the manag...

Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants.

Sensors (Basel, Switzerland)
Phenomics is a technology-driven approach with promising future to obtain unbiased data of biological systems. Image acquisition is relatively simple. However data handling and analysis are not as developed compared to the sampling capacities. We pre...

Artificial intelligence in the selection of common bean genotypes with high phenotypic stability.

Genetics and molecular research : GMR
Artificial neural networks have been used for various purposes in plant breeding, including use in the investigation of genotype x environment interactions. The aim of this study was to use artificial neural networks in the selection of common bean g...