AIMC Topic: Phenotype

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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
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...

Identifying patients with familial hypercholesterolemia using data mining methods in the Northern Great Plain region of Hungary.

Atherosclerosis
BACKGROUND AND AIMS: Familial hypercholesterolemia (FH) is one of the most frequent diseases with monogenic inheritance. Previous data indicated that the heterozygous form occurred in 1:250 people. Based on these reports, around 36,000-40,000 people ...

The anatomy of phenotype ontologies: principles, properties and applications.

Briefings in bioinformatics
The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large...

Multivariate Pattern Analysis of Genotype-Phenotype Relationships in Schizophrenia.

Schizophrenia bulletin
Genetic risk variants for schizophrenia have been linked to many related clinical and biological phenotypes with the hopes of delineating how individual variation across thousands of variants corresponds to the clinical and etiologic heterogeneity wi...

[From symptom to syndrome using modern software support].

Der Internist
Diagnosing rare diseases can be challenging for clinicians. This article gives an overview on novel approaches, which enable automated phenotype-driven analyses of differential diagnoses for rare diseases as well as genomic variation data of affected...

pBRIT: gene prioritization by correlating functional and phenotypic annotations through integrative data fusion.

Bioinformatics (Oxford, England)
MOTIVATION: Computational gene prioritization can aid in disease gene identification. Here, we propose pBRIT (prioritization using Bayesian Ridge regression and Information Theoretic model), a novel adaptive and scalable prioritization tool, integrat...

Improving Interpretation of Cardiac Phenotypes and Enhancing Discovery With Expanded Knowledge in the Gene Ontology.

Circulation. Genomic and precision medicine
BACKGROUND: A systems biology approach to cardiac physiology requires a comprehensive representation of how coordinated processes operate in the heart, as well as the ability to interpret relevant transcriptomic and proteomic experiments. The Gene On...

Know When You Don't Know: A Robust Deep Learning Approach in the Presence of Unknown Phenotypes.

Assay and drug development technologies
Deep convolutional neural networks show outstanding performance in image-based phenotype classification given that all existing phenotypes are presented during the training of the network. However, in real-world high-content screening (HCS) experimen...