Buchumschlag

Enhancing Cardiovascular Disease Risk Prediction with Machine Learning Models

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Shishehbori, Farnoush, Awan, Zainab
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Genomics
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

How Effectively Can Large Language Models Connect SNP Variants and ECG Phenotypes for Cardiovascular Risk Prediction?
von: Menon, Niranjana Arun, et al.
Veröffentlicht: (2025)

A DNA Methylation Classification Model Predicts Organ and Disease Site
von: Lee, Keng-Jung, et al.
Veröffentlicht: (2025)

Predicting Gene Disease Associations in Type 2 Diabetes Using Machine Learning on Single-Cell RNA-Seq Data
von: Toledo, Maria De La Luz Lomboy, et al.
Veröffentlicht: (2026)

Machine Learning‐Driven Predictive Modeling and Multi‐Objective Exploration of Oxaliplatin‐Loaded Nanocarriers for Enhanced Loading and Encapsulation Efficiency
von: Abbas Rahdar, et al.
Veröffentlicht: (2026)

Whole-Genome Phenotype Prediction with Machine Learning: Open Problems in Bacterial Genomics
von: James, Tamsin, et al.
Veröffentlicht: (2025)

BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool
von: Ramos, Vicente, et al.
Veröffentlicht: (2025)

A Chromosome-level Assembly and Functional Genomic Resources for the Model Annelid Capitella teleta.
von: Davies, Billie E, et al.
Veröffentlicht: (2026)

Machine Learning-Based Prediction of Key Genes Correlated to the Subretinal Lesion Severity in a Mouse Model of Age-Related Macular Degeneration
von: Yan, Kuan, et al.
Veröffentlicht: (2024)

Machine Learning and Mendelian Randomization Identify Allergic Rhinitis as Nasopharyngeal Carcinoma Risk Factor With Validated Potential Candidate Biomarkers
von: Minqi Chen, et al.
Veröffentlicht: (2025)

Learnable Group Transform: Enhancing Genotype-to-Phenotype Prediction for Rice Breeding with Small, Structured Datasets
von: Dong, Yunxuan, et al.
Veröffentlicht: (2025)

A Versatile AI Agent for Rare Disease Diagnosis and Risk Gene Prioritization
von: Liu, Tianyu, et al.
Veröffentlicht: (2026)

Leveraging Gene Expression Data and Explainable Machine Learning for Enhanced Early Detection of Type 2 Diabetes
von: Roy, Aurora Lithe, et al.
Veröffentlicht: (2024)

The Genomics Revolution in Nonmodel Species: Predictions vs. Reality for Salmonids.
von: May, Samuel A, et al.
Veröffentlicht: (2025)

PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis
von: Wu, Yan, et al.
Veröffentlicht: (2024)

HybridQC: Machine Learning-Augmented Quality Control for Single-Cell RNA-seq Data
von: Lai, Kaitao
Veröffentlicht: (2025)

Machine Learning Assisted Prediction of Degradation Behavior of Poly(Lactic‐Co‐Glycolic Acid)
von: Shuai Wang, et al.
Veröffentlicht: (2026)

DuAL-Net: A Hybrid Framework for Alzheimer's Disease Prediction from Whole-Genome Sequencing via Local SNP Windows and Global Annotations
von: Lee, Eun Hye, et al.
Veröffentlicht: (2025)

A CNN Approach to Polygenic Risk Prediction of Kidney Stone Formation
von: Salem, Amr, et al.
Veröffentlicht: (2024)

RNAMunin: A Deep Machine Learning Model for Non-coding RNA Discovery
von: Lui, Lauren, et al.
Veröffentlicht: (2025)

Cross-Species Antimicrobial Resistance Prediction from Genomic Foundation Models
von: Tai, Huilin
Veröffentlicht: (2026)

Machine Learning and Weighted Gene Coexpression Network–Based Identification of Biomarkers Predicting Immune Profiling and Drug Resistance in Lung Adenocarcinoma
von: Tian Zhang, et al.
Veröffentlicht: (2025)

Prediction of the Mechanical Properties of the Additive Friction Stir‐Deposited Al2219 Using Machine Learning
von: Chan Wa Tam, et al.
Veröffentlicht: (2026)

Robust Machine Learning for Regulatory Sequence Modeling under Biological and Technical Distribution Shifts
von: Yang, Yiyao
Veröffentlicht: (2026)

Machine Learning Integration of Multiomics Data Reveals RNA Modification–Driven Inflammation–Immune Interplay and Predicts Immunotherapy Response in Cervical Cancer
von: Pengyan Zhang, et al.
Veröffentlicht: (2026)

Optimizing Sample Size for Supervised Machine Learning with Bulk Transcriptomic Sequencing: A Learning Curve Approach
von: Qi, Yunhui, et al.
Veröffentlicht: (2024)

Polygenic Risk Score in Africa Population: Progress and challenges
von: Adam, Yagoub, et al.
Veröffentlicht: (2021)

PhenoGnet: A Graph-Based Contrastive Learning Framework for Disease Similarity Prediction
von: Baminiwatte, Ranga, et al.
Veröffentlicht: (2025)

Advancing Prognosis Prediction and Immunotherapy Efficacy in Lung Adenocarcinoma Through Machine Learning: Novel Insights From Anoikis Regulator Patterns in Single‐Cell Multiomics
von: Shan Li, et al.
Veröffentlicht: (2026)

Engineering Spatial and Molecular Features from Cellular Niches to Inform Predictions of Inflammatory Bowel Disease
von: Tan, Myles Joshua Toledo, et al.
Veröffentlicht: (2025)

Advancing Risk Gene Discovery Across the Allele Frequency Spectrum
von: Caballero, Madison, et al.
Veröffentlicht: (2025)

An LLM-Driven Multi-Agent Debate System for Mendelian Diseases
von: Zhou, Xinyang, et al.
Veröffentlicht: (2025)

MLOmics: Cancer Multi-Omics Database for Machine Learning
von: Yang, Ziwei, et al.
Veröffentlicht: (2024)

Prediction of PLX-4720 Sensitivity in Cancer Cell Lines through Multi-Omics Integration and Attention-Based Fusion Modeling
von: Aman, La Ode, et al.
Veröffentlicht: (2025)

DYNA: Disease-Specific Language Model for Variant Pathogenicity
von: Zhan, Huixin, et al.
Veröffentlicht: (2024)

Deubiquitinase Genes as Prognostic Biomarkers in Osteosarcoma: Insights From Single‐Cell Sequencing and Machine Learning–Based Prognostic Modeling
von: Meng Zhang, et al.
Veröffentlicht: (2025)

Unraveling the PANoptosis Landscape in Osteosarcoma: A Single‐Cell Sequencing and Machine Learning Approach to Prognostic Modeling and Tumor Microenvironment Analysis
von: Xue-yang Gui, et al.
Veröffentlicht: (2025)

GP-DHT: A Dual-Head Transformer with Contras-tive Learning for Predicting Gene Regulatory Rela-tionships across Species from Single-Cell Data
von: Yan, Shuai, et al.
Veröffentlicht: (2026)

MBGD: Microbial Genome Database for Comparative Analysis Featuring Enhanced Functionality to Characterize Gene and Genome Functions Through Large-scale Orthology Analysis.
von: Uchiyama, Ikuo, et al.
Veröffentlicht: (2025)

Learning to Discover Regulatory Elements for Gene Expression Prediction
von: Su, Xingyu, et al.
Veröffentlicht: (2025)

The Mechanistic Invariance Test: Genomic Language Models Fail to Learn Positional Regulatory Logic
von: Cheng, Bryan, et al.
Veröffentlicht: (2026)