AIMC Topic: Phenotype

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Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study.

Journal of medical Internet research
BACKGROUND: Virtually, all organisms on Earth have their own circadian rhythm, and humans are no exception. Circadian rhythms are associated with various human states, especially mood disorders, and disturbance of the circadian rhythm is known to be ...

Interactive biomedical ontology matching.

PloS one
Due to continuous evolution of biomedical data, biomedical ontologies are becoming larger and more complex, which leads to the existence of many overlapping information. To support semantic inter-operability between ontology-based biomedical systems,...

A directed learning strategy integrating multiple omic data improves genomic prediction.

Plant biotechnology journal
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...

Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO.

BMC systems biology
BACKGROUND: Improving efficiency of disease diagnosis based on phenotype ontology is a critical yet challenging research area. Recently, Human Phenotype Ontology (HPO)-based semantic similarity has been affectively and widely used to identify causati...

Automated feature selection of predictors in electronic medical records data.

Biometrics
The use of Electronic Health Records (EHR) for translational research can be challenging due to difficulty in extracting accurate disease phenotype data. Historically, EHR algorithms for annotating phenotypes have been either rule-based or trained wi...

Rare disease knowledge enrichment through a data-driven approach.

BMC medical informatics and decision making
BACKGROUND: Existing resources to assist the diagnosis of rare diseases are usually curated from the literature that can be limited for clinical use. It often takes substantial effort before the suspicion of a rare disease is even raised to utilize t...

DeepPVP: phenotype-based prioritization of causative variants using deep learning.

BMC bioinformatics
BACKGROUND: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity ...

Severe Dengue Prognosis Using Human Genome Data and Machine Learning.

IEEE transactions on bio-medical engineering
UNLABELLED: Dengue has become one of the most important worldwide arthropod-borne diseases. Dengue phenotypes are based on laboratorial and clinical exams, which are known to be inaccurate.

Contribution of Cardiovascular Reserve to Prognostic Categories of Heart Failure With Preserved Ejection Fraction: A Classification Based on Machine Learning.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
BACKGROUND: The authors used cluster analysis of data from cardiovascular domains associated with exercise intolerance to help define prognostic phenotypes of patients with heart failure with preserved ejection fraction (HFpEF).