Summary of Geneverse: a Collection Of Open-source Multimodal Large Language Models For Genomic and Proteomic Research, by Tianyu Liu et al.
Geneverse: A collection of Open-source Multimodal Large Language Models for Genomic and Proteomic Research
by Tianyu Liu, Yijia Xiao, Xiao Luo, Hua Xu, W. Jim Zheng, Hongyu Zhao
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Quantitative Methods (q-bio.QM)
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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 Geneverse collection includes a set of fine-tuned Large Language Models (LLMs) and multimodal LLMs for three novel tasks in genomic and proteomic research. The LLMs are trained on domain-specific datasets using advanced parameter-efficient finetuning techniques, allowing for the adaptation of models for tasks such as generating descriptions for gene functions, inferring protein function from structure, and selecting marker genes from spatial transcriptomic data. Evaluations demonstrate that these adapted LLMs perform well for these tasks and may outperform closed-source large-scale models in terms of both truthfulness and structural correctness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Geneverse is a collection of special language models that can help with important tasks in biomedical research. These models are trained on specific data sets and can be used to generate descriptions of gene functions, figure out what proteins do based on their structure, and select genes that can be used as markers for diseases. The researchers tested these models and found that they work well and might even be better than larger, more complex models. |
Keywords
* Artificial intelligence * Parameter efficient
