Summary of Does Your Model Understand Genes? a Benchmark Of Gene Properties For Biological and Text Models, by Yoav Kan-tor et al.
Does your model understand genes? A benchmark of gene properties for biological and text models
by Yoav Kan-Tor, Michael Morris Danziger, Eden Zohar, Matan Ninio, Yishai Shimoni
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| 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 proposed architecture-agnostic benchmarking approach leverages entity representation vectors from foundation models to evaluate their performance on various biological tasks. The focus is on gene properties collected from bioinformatics databases, categorized into five major groups: genomic properties, regulatory functions, localization, biological processes, and protein properties. Hundreds of tasks are defined based on these databases, including binary, multi-label, and multi-class classification tasks. The authors apply these benchmark tasks to evaluate expression-based models, large language models, protein language models, DNA-based models, and traditional baselines. Results show that text-based models and protein language models generally outperform expression-based models in certain tasks, while expression-based models demonstrate superior performance in others. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning methods are being used more often in biology to help understand genes and how they work. But it’s hard to compare the results of different models because they were trained on different data or did different tasks. To fix this problem, researchers developed a way to benchmark these models by using what they learned about genes and training simple models to do specific tasks. They looked at five types of gene properties: genomic, regulatory, localization, biological processes, and protein. This led to hundreds of tasks that the models were tested on. The results showed that some models did better than others in certain areas, like text-based models doing well with gene functions, while expression-based models did better with where genes are located. |
Keywords
» Artificial intelligence » Classification » Deep learning
