AIMC Topic: Genetic Predisposition to Disease
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A random forest-based predictive model for classifying BRCA1 missense variants: a novel approach for evaluating the missense mutations effect.
Journal of human genetics
Apr 18, 2025
The right classification of variants is the key to pre-symptomatic detection of disease and conducting preventive actions. Since BRCA1 has a high incidence and penetrance in breast and ovarian cancers, a high-performance predictive tool can be employ...
Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning.
Nature communications
Apr 15, 2025
Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains limited. This gap in knowledge poses challenges to the effective diagnosis and treatment of ccRCC. To a...
Prediction of new-onset migraine using clinical-genotypic data from the HUNT Study: a machine learning analysis.
The journal of headache and pain
Apr 7, 2025
BACKGROUND: Migraine is associated with a range of symptoms and comorbid disorders and has a strong genetic basis, but the currently identified risk loci only explain a small portion of the heritability, often termed the "missing heritability". We ai...
GONNMDA: A Ordered Message Passing GNN Approach for miRNA-Disease Association Prediction.
Genes
Apr 1, 2025
Small non-coding molecules known as microRNAs (miRNAs) play a critical role in disease diagnosis, treatment, and prognosis evaluation. Traditional wet-lab methods for validating miRNA-disease associations are often time-consuming and inefficient. Wit...
Exploring new drug treatment targets for immune related bone diseases using a multi omics joint analysis strategy.
Scientific reports
Mar 27, 2025
In the field of treatment and prevention of immune-related bone diseases, significant challenges persist, necessitating the urgent exploration of new and effective treatment methods. However, most existing Mendelian randomization (MR) studies are con...
Analysis of shared pathogenic mechanisms and drug targets in myocardial infarction and gastric cancer based on transcriptomics and machine learning.
Frontiers in immunology
Mar 21, 2025
BACKGROUND: Recent studies have suggested a potential association between gastric cancer (GC) and myocardial infarction (MI), with shared pathogenic factors. This study aimed to identify these common factors and potential pharmacologic targets.
Multitask learning model for predicting non-coding RNA-disease associations: Incorporating local and global context.
Methods (San Diego, Calif.)
Mar 18, 2025
Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are crucial non-coding RNAs involved in various diseases. Understanding these interactions is vital for advancing diagnostic, preventive, and therapeutic strategies. Existing computational methods...
Exploring the application of deep learning methods for polygenic risk score estimation.
Biomedical physics & engineering express
Mar 13, 2025
. Polygenic risk scores (PRS) summarise genetic information into a single number with clinical and research uses. Deep learning (DL) has revolutionised multiple fields, however, the impact of DL on PRSs has been less significant. We explore how DL ca...
Trans-ancestral rare variant association study with machine learning-based phenotyping for metabolic dysfunction-associated steatotic liver disease.
Genome biology
Mar 10, 2025
BACKGROUND: Genome-wide association studies (GWAS) have identified common variants associated with metabolic dysfunction-associated steatotic liver disease (MASLD). However, rare coding variant studies have been limited by phenotyping challenges and ...
Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.
International journal of molecular sciences
Feb 27, 2025
Complex diseases pose challenges in prediction due to their multifactorial and polygenic nature. This study employed machine learning (ML) to analyze genomic data from the UK Biobank, aiming to predict the genomic predisposition to complex diseases l...
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