Summary of Patholm: Identifying Pathogenicity From the Dna Sequence Through the Genome Foundation Model, by Sajib Acharjee Dip et al.
PathoLM: Identifying pathogenicity from the DNA sequence through the Genome Foundation Model
by Sajib Acharjee Dip, Uddip Acharjee Shuvo, Tran Chau, Haoqiu Song, Petra Choi, Xuan Wang, Liqing Zhang
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Genomics (q-bio.GN)
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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 Machine learning educators writing for technical audiences can assume that readers have a general understanding of machine learning but not the specific subfield. This paper introduces PathoLM, a cutting-edge pathogen language model optimized for identifying pathogenicity in bacterial and viral sequences. Leveraging pre-trained DNA models like the Nucleotide Transformer, PathoLM requires minimal data for fine-tuning, enhancing pathogen detection capabilities. The model captures a broader genomic context, improving identification of novel and divergent pathogens. A comprehensive dataset was developed, comprising approximately 30 species of viruses and bacteria, including ESKAPEE pathogens and seven virulent bacterial strains resistant to antibiotics. Comparative assessments demonstrate PathoLM outperforms existing models like DciPatho, showing robust zero-shot and few-shot capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For curious learners or non-technical adults, this paper is about developing a new way to identify diseases caused by germs. Traditional methods are not very good at detecting new types of germs because they require a lot of data and are prone to mistakes. The researchers created a special computer model called PathoLM that can detect germs more accurately and quickly. This model uses a pre-trained DNA model and requires minimal data for fine-tuning, making it better than other existing models. The team also developed a large dataset with many types of viruses and bacteria, including those that are resistant to antibiotics. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Language model » Machine learning » Transformer » Zero shot
