AIMC Topic: Phenotype

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Integrated phenotypic and transcriptomic characterization of desmin-related cardiomyopathy in hiPSC-derived cardiomyocytes and machine learning-based classification of disease features.

European journal of cell biology
Desmin-related diseases are characterized by skeletal muscle weakness, cardiomyopathy, and respiratory dysfunction due to mutations in the desmin gene (DES), which encodes a protein essential for muscle cell integrity. This study investigates the eff...

SSMT-PANBERT: A single-stage multitask model for phenotype extraction and assertion negation detection in unstructured clinical text.

Computers in biology and medicine
Automatic phenotype extraction and assertion negation detection from large-scale accessible Electronic Health Records (EHRs), including discharge summaries and radiology reports, is a crucial task for various healthcare applications, such as disease ...

Enhancing rare disease detection with deep phenotyping from EHR narratives: evaluation on Jeune syndrome.

International journal of medical informatics
BACKGROUND: Patients with rare diseases frequently experience misdiagnoses and long diagnostic delays. Accelerating their diagnosis is essential to ensure timely access to appropriate care. Given the increasing availability of EHRs, combining artific...

Artificial intelligence and perspective for rare genetic kidney diseases.

Kidney international
The integration of big data and artificial intelligence (AI) has revolutionized biomedicine, enhancing our understanding of diseases and health care practices. Although AI has shown remarkable success in some medical fields, its application in nephro...

Applying multimodal AI to physiological waveforms improves genetic prediction of cardiovascular traits.

American journal of human genetics
Electronic health records, biobanks, and wearable biosensors enable the collection of multiple health modalities from many individuals. Access to multimodal health data provides a unique opportunity for genetic studies of complex traits because diffe...

Machine learning to predict mitochondrial diseases by phenotypes.

Mitochondrion
Diagnosing mitochondrial diseases remains challenging because of the heterogeneous symptoms. This study aims to use machine learning to predict mitochondrial diseases from phenotypes to reduce genetic testing costs. This study included patients who u...

Morphological traits and machine learning for genetic lineage prediction of two reef-building corals.

PloS one
Integrating multiple lines of evidence that support molecular taxonomy analysis has proven to be a robust method for species delimitation in scleractinian corals. However, morphology often conflicts with genetic approaches due to high phenotypic plas...

Moving beyond the syndrome: how can acute kidney injury phenotypes help?

Current opinion in nephrology and hypertension
PURPOSE OF REVIEW: The purpose of this review is to describe recent and important updates in acute kidney injury (AKI) phenotyping that help us to move beyond the clinical syndrome of AKI.

Generative prediction of causal gene sets responsible for complex traits.

Proceedings of the National Academy of Sciences of the United States of America
The relationship between genotype and phenotype remains an outstanding question for organism-level traits because these traits are generally . The challenge arises from complex traits being determined by a combination of multiple genes (or loci), whi...

Toward a general framework for AI-enabled prediction in crop improvement.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
A theoretical framework for AI and ensembled prediction for crop improvement is introduced and demonstrated using the logistic map. Symbolic/sub-symbolic AI-based prediction can increase predictive skill with increase in system complexity. The curse ...