| Proj No. | A3210-251 |
| Title | Clustering of Bacterial Cytological Profiles to Uncover Mechanisms of Antibiotic Action |
| Summary | Building upon the high-resolution bacterial cytological profiling (BCP) data available from the study "High-Resolution Bacterial Cytological Profiling Reveals Subtle Changes in Cell Morphology" (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9933734/), this project focuses on applying unsupervised machine learning techniques to cluster bacterial cell images. The student will utilize image processing methods to extract morphological features from the dataset and then apply clustering algorithms such as k-means, hierarchical clustering, and DBSCAN to group images based on morphological similarities. The goal is to identify patterns correlating with specific antibiotic treatments or mechanisms of action. By analyzing these clusters, the project aims to enhance the understanding of how different antibiotics affect bacterial cell morphology, potentially aiding in the development of new antimicrobial strategies. |
| 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?: |