Summary of Unified Representation Of Genomic and Biomedical Concepts Through Multi-task, Multi-source Contrastive Learning, by Hongyi Yuan et al.
Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning
by Hongyi Yuan, Suqi Liu, Kelly Cho, Katherine Liao, Alexandre Pereira, Tianxi Cai
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Applications (stat.AP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The GENomic Encoding REpresentation with Language Model (GENEREL) framework is designed to bridge genetic and biomedical knowledge bases by fine-tuning language models to infuse biological knowledge behind clinical concepts. This enables the model to capture complex biomedical relationships more effectively, enriching understanding of how genomic data connects to clinical outcomes. GENEREL constructs a unified embedding space for biomedical concepts and common SNPs from patient-level data, biomedical knowledge graphs, and GWAS summaries, using multi-task contrastive learning to adapt to diverse natural language representations. The framework demonstrates ability to capture nuanced relationships between SNPs and clinical concepts, potentially allowing for refined identification of concepts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GENEREL is a new way to help computers understand genetic and medical information better. It’s like a special dictionary that connects words from medicine and genetics together. This helps us make connections between different pieces of information, like how certain genes are connected to diseases. The model uses existing language models, but makes them smarter by adding biological knowledge. This makes it easier for computers to understand complex relationships between genetic data and medical outcomes. |
Keywords
» Artificial intelligence » Embedding space » Fine tuning » Language model » Multi task
