AIMC Topic: Phenotype

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Circulating Biomarkers Instead of Genotyping to Establish Metabolizer Phenotypes.

Annual review of pharmacology and toxicology
Pharmacogenomics (PGx) enables personalized treatment for the prediction of drug response and to avoid adverse drug reactions. Currently, PGx mainly relies on the genetic information of absorption, distribution, metabolism, and excretion (ADME) targe...

Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes.

Current rheumatology reports
PURPOSE OF REVIEW: Osteoarthritis (OA) is a complex heterogeneous disease with no effective treatments. Artificial intelligence (AI) and its subfield machine learning (ML) can be applied to data from different sources to (1) assist clinicians and pat...

PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework.

Nature genetics
Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals...

Improving the classification of cardinality phenotypes using collections.

Journal of biomedical semantics
MOTIVATION: Phenotypes are observable characteristics of an organism and they can be highly variable. Information about phenotypes is collected in a clinical context to characterize disease, and is also collected in model organisms and stored in mode...

deepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle.

Genetics, selection, evolution : GSE
BACKGROUND: Genomic prediction has become widespread as a valuable tool to estimate genetic merit in animal and plant breeding. Here we develop a novel genomic prediction algorithm, called deepGBLUP, which integrates deep learning networks and a geno...

Applications of artificial intelligence in clinical laboratory genomics.

American journal of medical genetics. Part C, Seminars in medical genetics
The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of "big data" in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately ev...

Machine learning applications to improve flavor and nutritional content of horticultural crops through breeding and genetics.

Current opinion in biotechnology
Over the last decades, significant strides were made in understanding the biochemical factors influencing the nutritional content and flavor profile of fruits and vegetables. Product differentiation in the produce aisle is the natural consequence of ...

Machine learning in photosynthesis: Prospects on sustainable crop development.

Plant science : an international journal of experimental plant biology
Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing chall...

Stratification of diabetes in the context of comorbidities, using representation learning and topological data analysis.

Scientific reports
Diabetes is a heterogenous, multimorbid disorder with a large variation in manifestations, trajectories, and outcomes. The aim of this study is to validate a novel machine learning method for the phenotyping of diabetes in the context of comorbiditie...

Intermediately synchronised brain states optimise trade-off between subject specificity and predictive capacity.

Communications biology
Functional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within the duration of a functional magnetic resonance imaging (fMRI) scanning session, researchers hav...