AIMC Topic: Mendelian Randomization Analysis
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Reconstructing Molecular Networks by Causal Diffusion Do-Calculus Analysis with Deep Learning.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Oct 23, 2024
Quantifying molecular regulations between genes/molecules causally from observed data is crucial for elucidating the molecular mechanisms underlying biological processes at the network level. Presently, most methods for inferring gene regulatory and ...
Integrated approach of machine learning, Mendelian randomization and experimental validation for biomarker discovery in diabetic nephropathy.
Diabetes, obesity & metabolism
Oct 6, 2024
AIM: To identify potential biomarkers and explore the mechanisms underlying diabetic nephropathy (DN) by integrating machine learning, Mendelian randomization (MR) and experimental validation.
Screening COPD-Related Biomarkers and Traditional Chinese Medicine Prediction Based on Bioinformatics and Machine Learning.
International journal of chronic obstructive pulmonary disease
Sep 24, 2024
PURPOSE: To employ bioinformatics and machine learning to predict the characteristics of immune cells and genes associated with the inflammatory response and ferroptosis in chronic obstructive pulmonary disease (COPD) patients and to aid in the devel...
Databases, Genetic
Phenotype
Gene Regulatory Networks
Genetic Markers
Medicine, Chinese Traditional
Biomarkers
Genetic Predisposition to Disease
Predictive Value of Tests
Pulmonary Disease, Chronic Obstructive
Ferroptosis
Computational Biology
Humans
Mendelian Randomization Analysis
Lung
MicroRNAs
Machine Learning
Drugs, Chinese Herbal
Molecular Docking Simulation
Gene Expression Profiling
Transcriptome
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
Aug 21, 2024
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...
Identification of programmed cell death-related genes and diagnostic biomarkers in endometriosis using a machine learning and Mendelian randomization approach.
Frontiers in endocrinology
Aug 1, 2024
BACKGROUND: Endometriosis (EM) is a prevalent gynecological disorder frequently associated with irregular menstruation and infertility. Programmed cell death (PCD) is pivotal in the pathophysiological mechanisms underlying EM. Despite this, the preci...
Screening of Secretory Proteins Linking Major Depressive Disorder with Heart Failure Based on Comprehensive Bioinformatics Analysis and Machine Learning.
Biomolecules
Jul 4, 2024
BACKGROUND: Major depressive disorder (MDD) plays a crucial role in the occurrence of heart failure (HF). This investigation was undertaken to explore the possible mechanism of MDD's involvement in HF pathogenesis and identify candidate biomarkers fo...
Association between machine learning-assisted heavy metal exposures and diabetic kidney disease: a cross-sectional survey and Mendelian randomization analysis.
Frontiers in public health
Jun 14, 2024
BACKGROUND AND OBJECTIVE: Heavy metals, ubiquitous in the environment, pose a global public health concern. The correlation between these and diabetic kidney disease (DKD) remains unclear. Our objective was to explore the correlation between heavy me...
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...
Integrating Machine Learning and Mendelian Randomization Determined a Functional Neurotrophin-Related Gene Signature in Patients with Lower-Grade Glioma.
Molecular biotechnology
Jan 23, 2024
Recent researches reported that neurotrophins can promote glioma growth/invasion but the relevant model for predicting patients' survival in Lower-Grade Gliomas (LGGs) lacked. In this study, we adopted univariate Cox analysis, LASSO regression, and m...
Proportional Hazards Models
Homeodomain Proteins
Nerve Growth Factors
Sulfotransferases
DNA Copy Number Variations
Mendelian Randomization Analysis
Neoplasm Grading
Machine Learning
Humans
Prognosis
Male
Female
Polymorphism, Single Nucleotide
Brain Neoplasms
Gene Expression Regulation, Neoplastic
Glioma
Deep mendelian randomization: Investigating the causal knowledge of genomic deep learning models.
PLoS computational biology
Oct 20, 2022
Multi-task deep learning (DL) models can accurately predict diverse genomic marks from sequence, but whether these models learn the causal relationships between genomic marks is unknown. Here, we describe Deep Mendelian Randomization (DeepMR), a meth...
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