| Proj No. | A3213-251 |
| Title | Comparative Analysis of Genomic Language Models for Bacterial Identification |
| Summary | This project investigates the application of Genomic Language Models (GLMs) to classify bacterial species and identify genetic modifications. The student will compare different AI-based models, such as GenSLMs, ESM-2, and Evo, to evaluate their ability to detect genetic signatures in microbial genomes. The project involves curating bacterial genome datasets, fine-tuning pre-trained models, and analyzing their predictive accuracy. By performing benchmark comparisons against traditional bioinformatics tools like BLAST and IslandViewer, the student will assess the advantages of deep learning-based approaches. The expected outcome is a comparative performance report detailing the strengths and limitations of GLMs in microbial forensic applications, contributing to advancements in AI-driven pathogen detection. |
| Supervisor | A/P Wang Lipo (Loc:S1 > S1 B1C > S1 B1C 98, Ext: +65 67906372) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Computer Engineering I (Loc: S2-B4c-15) |
| Single/Group: | Single |
| Area: | Digital Media Processing and Computer Engineering |
| ISP/RI/SMP/SCP?: |