AIMC Topic: Genetic Predisposition to Disease
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Discovering the gene-brain-behavior link in autism via generative machine learning.
Science advances
Jun 12, 2024
Autism is traditionally diagnosed behaviorally but has a strong genetic basis. A genetics-first approach could transform understanding and treatment of autism. However, isolating the gene-brain-behavior relationship from confounding sources of variab...
Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease.
Nature genetics
Jun 11, 2024
Coronary artery disease (CAD) exists on a spectrum of disease represented by a combination of risk factors and pathogenic processes. An in silico score for CAD built using machine learning and clinical data in electronic health records captures disea...
Unraveling the genetic and molecular landscape of sepsis and acute kidney injury: A comprehensive GWAS and machine learning approach.
International immunopharmacology
Jun 8, 2024
OBJECTIVES: This study aimed to explore the underlying mechanisms of sepsis and acute kidney injury (AKI), including sepsis-associated AKI (SA-AKI), a frequent complication in critically ill sepsis patients.
Prediction of adverse drug reactions due to genetic predisposition using deep neural networks.
Molecular informatics
Jun 8, 2024
Drug development is a long and costly process, often limited by the toxicity and adverse drug reactions (ADRs) caused by drug candidates. Even on the market, some drugs can cause strong ADRs that can vary depending on an individual polymorphism. The ...
A miRNA-disease association prediction model based on tree-path global feature extraction and fully connected artificial neural network with multi-head self-attention mechanism.
BMC cancer
Jun 5, 2024
BACKGROUND: MicroRNAs (miRNAs) emerge in various organisms, ranging from viruses to humans, and play crucial regulatory roles within cells, participating in a variety of biological processes. In numerous prediction methods for miRNA-disease associati...
A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions.
IEEE/ACM transactions on computational biology and bioinformatics
Jun 5, 2024
MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease pat...
Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement.
Genome medicine
Jun 4, 2024
BACKGROUND: Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic da...
Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.
Scientific reports
May 30, 2024
We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baselin...
Deep learning of left atrial structure and function provides link to atrial fibrillation risk.
Nature communications
May 21, 2024
Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to asse...
AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.
Nature communications
May 18, 2024
Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particu...
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