Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 23, 2025Deleted JournalOpen Access

Artificial Intelligence and Multi-Omics Integration in Obesity: A Review of Computational Models for Predicting Metabolic Comorbidities

View Full Paper
Ask AI
Bookmark
Share

Authors

ASAnkur Pan SaikiaAKAnanya Kalita

Discussion

Loading...

Member takes

Overview

Review highlights AI's role in enhancing predictive models for metabolic comorbidities in obesity, suggesting pathways for future research.

Key Points

  • AI-driven multi-omics integration can boost prediction accuracy for obesity-related conditions, improving it by 5 to 15%.
  • The review emphasizes the challenges faced by deep learning in harmonizing diverse omics data for effective analysis.
  • Data standardization and privacy concerns are crucial for translating AI models into clinical applications for obesity research.
  • Machine learning techniques effectively identify biomarkers associated with metabolic disorders linked to obesity.

Cite This Study

Saikia et al. (2025) studied this question.

synapsesocial.com/papers/68d4419f713b0b5dfea805d6https://doi.org/10.1007/s12018-025-09310-0
View Full Paper
Ask AI
Bookmark
Share