1. Application of LLM in Microbiome Taxonomy
- Description: Surprisingly, Claude trained the LLM using the entire NCBI database. When provided with FASTQ sequences, it can directly return taxonomic information. I believe comparing it with traditional classification methods offers a significant innovative touch.
2. Performance of Microbiome DA Methods in Other Omics
- Description: Currently, for different omics, there are DA methods tailored to their specific characteristics. A study in Genome Biology in 2021 experimented with DA methods from other omics in microbiome analysis. It's intriguing to explore how microbiome DA methods, which usually process the compositional nature of the microbiome data, perform on other omics.
3. Generative AI in Microbiome Data Analysis and Accuracy Assessment
- Description: The LLM model can act as an expert in microbiome data analysis, conceptualizing schemes. We can evaluate the differences between its data analysis schemes and those designed by actual experts, as well as assess whether it can replace real experts.
4. PI_find
- Description: PI_find assists users in searching for PI names published in specific professional journals. It's a handy tool for graduate school applicants to find matching PIs for their research interests.
5. Fine-Tuning Platform for Bioinformatics Repositories
- Description: With the rapid development of numerous bioinformatics tools, the pace often surpasses the update speed of tools like ChatGPT and Claude. As a result, inquiries about newer tools might not yield satisfactory results from ChatGPT. I aim to develop a platform that allows develops to effortlessly fine-tune ChatGPT with their bioinformatics repositories. This can then be deployed in the Issues section of GitHub to assist users with their queries.
