AIMC Topic: Phenotype

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Valid inference for machine learning-assisted genome-wide association studies.

Nature genetics
Machine learning (ML) has become increasingly popular in almost all scientific disciplines, including human genetics. Owing to challenges related to sample collection and precise phenotyping, ML-assisted genome-wide association study (GWAS), which us...

European cross-cultural neuropsychological test battery (CNTB) for the assessment of cognitive impairment in multiple sclerosis: Cognitive phenotyping and classification supported by machine learning techniques.

Multiple sclerosis and related disorders
BACKGROUND: The European Cross-Cultural Neuropsychological Test Battery (CNTB) has been proposed as a comprehensive battery for cognitive assessment, reducing the potential impact of cultural variables. In this validation study, we aimed to evaluate ...

Abnormal phenotypic defects detection of jujube using explainable machine learning enhanced computer vision.

Journal of food science
Jujube is susceptible to biotic and abiotic adversity stresses resulting in abnormal phenotypic defects. Therefore, abnormal phenotype fruits should be removed during postharvest sorting to increase added value. An improved maximum horizontal diamete...

Identifying biological markers and sociodemographic factors that influence the gap between phenotypic and chronological ages.

Informatics for health & social care
INTRODUCTION: The world's population is aging rapidly, leading to increased public health and economic burdens due to age-related cardiovascular and neurodegenerative diseases. Early risk detection is essential for prevention and to improve the quali...

Characterizing daily physical activity patterns with unsupervised learning via functional mixture models.

Journal of behavioral medicine
Physical inactivity is a significant public health concern. Consideration of inter-individual variations in physical activity (PA) trends can provide additional information about the groups under study to aid intervention design. This study aims to i...

Methods for phenotyping adult patients with acute kidney injury: a systematic review.

Journal of nephrology
BACKGROUND: Acute kidney injury (AKI) is a multifaceted disease characterized by diverse clinical presentations and mechanisms. Advances in artificial intelligence have propelled the identification of AKI subphenotypes, enhancing our capacity to cust...

Disease prediction with multi-omics and biomarkers empowers case-control genetic discoveries in the UK Biobank.

Nature genetics
The emergence of biobank-level datasets offers new opportunities to discover novel biomarkers and develop predictive algorithms for human disease. Here, we present an ensemble machine-learning framework (machine learning with phenotype associations, ...

Exploring genomic feature selection: A comparative analysis of GWAS and machine learning algorithms in a large-scale soybean dataset.

The plant genome
The surge in high-throughput technologies has empowered the acquisition of vast genomic datasets, prompting the search for genetic markers and biomarkers relevant to complex traits. However, grappling with the inherent complexities of high dimensiona...

Machine learning phenotyping and GWAS reveal genetic basis of Cd tolerance and absorption in jute.

Environmental pollution (Barking, Essex : 1987)
Cadmium (Cd) is a dangerous environmental contaminant. Jute (Corchorus sp.) is an important natural fiber crop with strong absorption and excellent adaptability to metal-stressed environments, used in the phytoextraction of heavy metals. Understandin...