AIMC Topic: Genetic Predisposition to Disease

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Regression convolutional neural network models implicate peripheral immune regulatory variants in the predisposition to Alzheimer's disease.

PLoS computational biology
Alzheimer's disease (AD) involves aggregation of amyloid β and tau, neuron loss, cognitive decline, and neuroinflammatory responses. Both resident microglia and peripheral immune cells have been associated with the immune component of AD. However, th...

Explore key genes of Crohn's disease based on glycerophospholipid metabolism: A comprehensive analysis Utilizing Mendelian Randomization, Multi-Omics integration, Machine Learning, and SHAP methodology.

International immunopharmacology
BACKGROUND AND AIMS: Crohn's disease (CD) is a chronic, complex inflammatory condition with increasing incidence and prevalence worldwide. However, the causes of CD remain incompletely understood. We identified CD-related metabolites, inflammatory fa...

Family history of cancer and lung cancer: Utility of big data and artificial intelligence for exploring the role of genetic risk.

Lung cancer (Amsterdam, Netherlands)
OBJECTIVES: Lung Cancer (LC) is a multifactorial disease for which the role of genetic susceptibility has become increasingly relevant. Our aim was to use artificial intelligence (AI) to analyze differences between patients with LC based on family hi...

SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning.

Nature communications
Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for transcriptome-wide association studies (TWAS). To leverage expression imputa...

An assessment of the value of deep neural networks in genetic risk prediction for surgically relevant outcomes.

PloS one
INTRODUCTION: Postoperative complications affect up to 15% of surgical patients constituting a major part of the overall disease burden in a modern healthcare system. While several surgical risk calculators have been developed, none have so far been ...

Improving the Detection of Potential Cases of Familial Hypercholesterolemia: Could Machine Learning Be Part of the Solution?

Journal of the American Heart Association
BACKGROUND: Familial hypercholesterolemia (FH), while highly prevalent, is a significantly underdiagnosed monogenic disorder. Improved detection could reduce the large number of cardiovascular events attributable to poor case finding. We aimed to ass...

A deep learning framework for predicting disease-gene associations with functional modules and graph augmentation.

BMC bioinformatics
BACKGROUND: The exploration of gene-disease associations is crucial for understanding the mechanisms underlying disease onset and progression, with significant implications for prevention and treatment strategies. Advances in high-throughput biotechn...

Discovering the gene-brain-behavior link in autism via generative machine learning.

Science advances
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
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
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.