AIMC Topic: Phenotype

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Analysis of Causal Relationships in Integrated Ontologies of Diseases, Phenotypes, and Radiological Diagnosis.

Studies in health technology and informatics
Biomedical ontologies encode knowledge in a form that makes it computable. The current study used the integration of three large biomedical ontologies-the Disease Ontology (DO), Human Phenotype Ontology (HPO), and Radiology Gamuts Ontology (RGO)-to e...

GraphGONet: a self-explaining neural network encapsulating the Gene Ontology graph for phenotype prediction on gene expression.

Bioinformatics (Oxford, England)
MOTIVATION: Medical care is becoming more and more specific to patients' needs due to the increased availability of omics data. The application to these data of sophisticated machine learning models, in particular deep learning (DL), can improve the ...

A split-and-merge deep learning approach for phenotype prediction.

Frontiers in bioscience (Landmark edition)
BACKGROUND: Phenotype prediction with genome-wide markers is a critical but difficult problem in biomedical research due to many issues such as nonlinearity of the underlying genetic mapping and high-dimensionality of marker data. When using the deep...

DeepSVP: integration of genotype and phenotype for structural variant prioritization using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Structural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of multiple genes, or affect the functions of genomic regions in differe...

Machine Learning Identifies Digital Phenotyping Measures Most Relevant to Negative Symptoms in Psychotic Disorders: Implications for Clinical Trials.

Schizophrenia bulletin
BACKGROUND: Digital phenotyping has been proposed as a novel assessment tool for clinical trials targeting negative symptoms in psychotic disorders (PDs). However, it is unclear which digital phenotyping measurements are most appropriate for this pur...

Neural network-based integration of polygenic and clinical information: development and validation of a prediction model for 10-year risk of major adverse cardiac events in the UK Biobank cohort.

The Lancet. Digital health
BACKGROUND: In primary cardiovascular disease prevention, early identification of high-risk individuals is crucial. Genetic information allows for the stratification of genetic predispositions and lifetime risk of cardiovascular disease. However, tow...

Advances in Deep Neuropathological Phenotyping of Alzheimer Disease: Past, Present, and Future.

Journal of neuropathology and experimental neurology
Alzheimer disease (AD) is a neurodegenerative disorder characterized pathologically by the presence of neurofibrillary tangles and amyloid beta (Aβ) plaques in the brain. The disease was first described in 1906 by Alois Alzheimer, and since then, the...

Automated Classification of Cellular Phenotypes Using Machine Learning in Cellprofiler and CellProfiler Analyst.

Methods in molecular biology (Clifton, N.J.)
Cell images provide a multitude of phenotypic information, which in its entirety the human eye can hardly perceive. Automated image analysis and machine learning approaches enable the unbiased identification and analysis of cellular mechanisms and as...

Machine Learning for Image Analysis: Leaf Disease Segmentation.

Methods in molecular biology (Clifton, N.J.)
Plant phenomics field has seen a great increase in scalability in the last decade mainly due to technological advances in remote sensors and phenotyping platforms. These are capable of screening thousands of plants many times throughout the day, gene...

Technical and practical aspects of artificial intelligence in cardiology.

Bratislavske lekarske listy
Artificial intelligence (AI) is here to stay. It is not a future anymore, and there are many particular problems in cardiology that are already being solved via machine learning (ML), and many more are to come. AI cannot solve complex tasks yet, and ...