AIMC Topic: Phenotype

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Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVES: Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE fram...

Learning from biomedical linked data to suggest valid pharmacogenes.

Journal of biomedical semantics
BACKGROUND: A standard task in pharmacogenomics research is identifying genes that may be involved in drug response variability, i.e., pharmacogenes. Because genomic experiments tended to generate many false positives, computational approaches based ...

EHR-based phenotyping: Bulk learning and evaluation.

Journal of biomedical informatics
In data-driven phenotyping, a core computational task is to identify medical concepts and their variations from sources of electronic health records (EHR) to stratify phenotypic cohorts. A conventional analytic framework for phenotyping largely uses ...

SNPPhenA: a corpus for extracting ranked associations of single-nucleotide polymorphisms and phenotypes from literature.

Journal of biomedical semantics
BACKGROUND: Single Nucleotide Polymorphisms (SNPs) are among the most important types of genetic variations influencing common diseases and phenotypes. Recently, some corpora and methods have been developed with the purpose of extracting mutations an...

How evolution learns to generalise: Using the principles of learning theory to understand the evolution of developmental organisation.

PLoS computational biology
One of the most intriguing questions in evolution is how organisms exhibit suitable phenotypic variation to rapidly adapt in novel selective environments. Such variability is crucial for evolvability, but poorly understood. In particular, how can nat...

Computer vision and machine learning for robust phenotyping in genome-wide studies.

Scientific reports
Traditional evaluation of crop biotic and abiotic stresses are time-consuming and labor-intensive limiting the ability to dissect the genetic basis of quantitative traits. A machine learning (ML)-enabled image-phenotyping pipeline for the genetic stu...

Phenomapping for the Identification of Hypertensive Patients with the Myocardial Substrate for Heart Failure with Preserved Ejection Fraction.

Journal of cardiovascular translational research
We sought to evaluate whether unbiased machine learning of dense phenotypic data ("phenomapping") could identify distinct hypertension subgroups that are associated with the myocardial substrate (i.e., abnormal cardiac mechanics) for heart failure wi...

The ontogeny of kin-recognition mechanisms in Belding's ground squirrels.

Physiology & behavior
Despite extensive research on the functions and mechanisms of kin recognition, little is known about developmental changes in the abilities mediating such recognition. Belding's ground squirrels, Urocitellus beldingi, use at least two mechanisms of k...

Tensor Factorization for Precision Medicine in Heart Failure with Preserved Ejection Fraction.

Journal of cardiovascular translational research
Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome that may benefit from improved subtyping in order to better characterize its pathophysiology and to develop novel targeted therapies. The United States Precis...

Thematic issue of the Second combined Bio-ontologies and Phenotypes Workshop.

Journal of biomedical semantics
This special issue covers selected papers from the 18th Bio-Ontologies Special Interest Group meeting and Phenotype Day, which took place at the Intelligent Systems for Molecular Biology (ISMB) conference in Dublin in 2015. The papers presented in th...