AIMC Topic: Phenotype

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MLDAAPP: machine learning data acquisition for assessing population phenotypes.

G3 (Bethesda, Md.)
Collecting phenotypic data from many individuals is critical to answering fundamental biological questions, particularly in genetics. Yet, whole organismal phenotypic data are still often collected manually; limiting the scale of data generation, pre...

PhenoLearn: a user-friendly toolkit for image annotation and deep learning-based phenotyping for biological datasets.

Journal of evolutionary biology
The digitization of natural history specimens has unlocked opportunities for large-scale phenotypic trait analysis. In recent years, deep learning has shown significant results in accurately predicting annotations on 2D specimen photographs. However,...

RhDnostics: A Machine Learning-Based Predictive Algorithm Model for RhD-Negative and DEL Blood Group Screening.

The journal of applied laboratory medicine
BACKGROUND: The D-elution (DEL) phenotype is serologically mislabeled as Rh-negative because of the very low amount of D antigen on red blood cells. The adsorption-elution test and genotyping are recommended tests for confirmation. However, turnaroun...

Prediabetes phenotypes: can aetiology and risk profile guide lifestyle strategies for diabetes prevention?

Expert review of endocrinology & metabolism
INTRODUCTION: Type 2 diabetes (T2D) continues to worsen globally alongside rise in obesity. Asymptomatic dysglycaemia, which precedes T2D, provides opportunities to identify those at risk and target prevention but prediabetes is highly variable. Not ...

Moving past multidisciplinary discussions and Gender-Age-Physiology model: precision medicine through biological phenotyping in interstitial lung disease.

Current opinion in pulmonary medicine
PURPOSE OF REVIEW: Interstitial lung disease (ILD) presents significant diagnostic and therapeutic challenges due to underlying biological heterogeneity and variable clinical course. Traditional diagnostic and prognostic tools are limited in their ab...

Machine learning-driven GWAS uncovers novel candidate genes for resistance to frosty pod rot and witches' broom disease in cacao.

The plant genome
Cacao (Theobroma cacao), the source of chocolate, is threatened by devastating diseases like frosty pod rot (FPR) and witches' broom disease (WBD), impacting global production and farmer livelihoods. Here, we employ a machine learning-driven genome-w...

An integrated approach for key gene selection and cancer phenotype classification: Improving diagnosis and prediction.

Computers in biology and medicine
The identification of key features and reliable phenotype classification remains pivotal in cancer research, with direct implications for early diagnosis, prognosis, treatment optimization, and cost reduction in healthcare. This study introduces a hy...

PhenoLinker: Phenotype-gene link prediction and explanation using heterogeneous graph neural networks.

Artificial intelligence in medicine
The association of a given human phenotype with a genetic variant remains a critical challenge in biomedical research. We present PhenoLinker, a novel graph-based system capable of associating a score to a phenotype-gene relationship by using heterog...

Interspecies predictions of growth traits from quantitative transcriptome data acquired during fruit development.

Journal of experimental botany
Linking genotype and phenotype is a fundamental challenge in biology. In this respect, machine learning is playing a pivotal role in systems biology. As central phenotypic traits, fruit development and relative growth rate (RGR) result from interacti...

protPheMut: An Interpretable Machine Learning Tool for Classification of Cancer and Neurodevelopmental Disorders in Human Missense Mutations.

Journal of chemical information and modeling
Recent advances in human genomics have revealed that missense mutations in a single protein can lead to distinctly different phenotypes. In particular, some mutations in oncoproteins like MEK1, MEK2, PI3Kα, PTEN, SHAP2, and RAS are linked various can...