AIMC Topic: Genetic Predisposition to Disease

Clear Filters Showing 261 to 270 of 315 articles

AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.

NAR genomics and bioinformatics
The convergence of artificial intelligence (AI) and biomedical data is transforming precision medicine by enabling the use of genetic risk factors (GRFs) for customized healthcare services based on individual needs. Although GRFs play an essential ro...

GDReCo: Fine-grained gene-disease relationship extraction corpus.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Understanding gene-disease relationships is crucial for medical research, drug discovery, clinical diagnosis, and other fields. However, there is currently no high-quality, fine-grained corpus available for training Natural ...

Predicting Mutation-Disease Associations Through Protein Interactions Via Deep Learning.

IEEE journal of biomedical and health informatics
Disease is one of the primary factors affecting life activities, with complex etiologies often influenced by gene expression and mutation. Currently, wet lab experiments have analyzed the mechanisms of mutations, but these are usually limited by the ...

iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations.

BMC biology
BACKGROUND: Exploring piRNA-disease associations can help discover candidate diagnostic or prognostic biomarkers and therapeutic targets. Several computational methods have been presented for identifying associations between piRNAs and diseases. Howe...

ASiDentify (ASiD): a machine learning model to predict new autism spectrum disorder risk genes.

Genetics
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects nearly 3% of children and has a strong genetic component. While hundreds of ASD risk genes have been identified through sequencing studies, the genetic heterogeneity of ASD ...

MUTATE: a human genetic atlas of multiorgan artificial intelligence endophenotypes using genome-wide association summary statistics.

Briefings in bioinformatics
Artificial intelligence (AI) has been increasingly integrated into imaging genetics to provide intermediate phenotypes (i.e. endophenotypes) that bridge the genetics and clinical manifestations of human disease. However, the genetic architecture of t...

[Prophylactic surgery and genetic counselling: What impact of the artificial intelligence?].

Bulletin du cancer
In the area of cancer predisposition, certain situations may lead to the discussion of prophylactic surgery. This is rarely strictly recommended and depends on the patient's choice. The advantages and disadvantages must be weighed up. The main advant...

Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research...

PNL: a software to build polygenic risk scores using a super learner approach based on PairNet, a Convolutional Neural Network.

Bioinformatics (Oxford, England)
SUMMARY: Polygenic risk scores (PRSs) hold promise for early disease diagnosis and personalized treatment, but their overall discriminative power remains limited for many diseases in the general population. As a result, numerous novel PRS modeling te...

Predicting Diabetic Retinopathy Using a Machine Learning Approach Informed by Whole-Exome Sequencing Studies.

Biomedical and environmental sciences : BES
OBJECTIVE: To establish and validate a novel diabetic retinopathy (DR) risk-prediction model using a whole-exome sequencing (WES)-based machine learning (ML) method.