AIMC Topic: Phenotype

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Application of machine learning approaches for predicting hemophilia A severity.

Journal of thrombosis and haemostasis : JTH
BACKGROUND: Hemophilia A (HA) is an X-linked congenital bleeding disorder, which leads to deficiency of clotting factor (F) VIII. It mostly affects males, and females are considered carriers. However, it is now recognized that variants of F8 in femal...

Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data.

The journal of allergy and clinical immunology. In practice
BACKGROUND: Asthma classification into different subphenotypes is important to guide personalized therapy and improve outcomes.

Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes.

The Canadian journal of cardiology
Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particularly among young individuals. Recent advances in our understanding of these syndromes have improved patient diagnosis and care, yet certain clinical ...

Integrated machine learning and multimodal data fusion for patho-phenotypic feature recognition in iPSC models of dilated cardiomyopathy.

Biological chemistry
Integration of multiple data sources presents a challenge for accurate prediction of molecular patho-phenotypic features in automated analysis of data from human model systems. Here, we applied a machine learning-based data integration to distinguish...

Machine learning models reveal distinct disease subgroups and improve diagnostic and prognostic accuracy for individuals with pathogenic SCN8A gain-of-function variants.

Biology open
Distinguishing clinical subgroups for patients suffering with diseases characterized by a wide phenotypic spectrum is essential for developing precision therapies. Patients with gain-of-function (GOF) variants in the SCN8A gene exhibit substantial cl...

The gut microbiome associates with phenotypic manifestations of post-acute COVID-19 syndrome.

Cell host & microbe
The mechanisms underlying the many phenotypic manifestations of post-acute COVID-19 syndrome (PACS) are poorly understood. Herein, we characterized the gut microbiome in heterogeneous cohorts of subjects with PACS and developed a multi-label machine ...

Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults.

Journal of cachexia, sarcopenia and muscle
BACKGROUND: Computed tomography (CT) body compositions reflect age-related metabolic derangements. We aimed to develop a multi-outcome deep learning model using CT multi-level body composition parameters to detect metabolic syndrome (MS), osteoporosi...

Microscopic computed tomography with AI-CNN-powered image analysis: the path to phenotype bleomycin-induced lung injury.

American journal of physiology. Cell physiology
Bleomycin (BLM)-induced lung injury in mice is a valuable model for investigating the molecular mechanisms that drive inflammation and fibrosis and for evaluating potential therapeutic approaches to treat the disease. Given high variability in the BL...

Identifying Bladder Phenotypes After Spinal Cord Injury With Unsupervised Machine Learning: A New Way to Examine Urinary Symptoms and Quality of Life.

The Journal of urology
PURPOSE: Patients with spinal cord injuries (SCIs) experience variable urinary symptoms and quality of life (QOL). Our objective was to use machine learning to identify bladder-relevant phenotypes after SCI and assess their association with urinary s...