I'm working on applying Machine Learning for microbial phenotype prediction from mass spectrometry data. My goal is to improve phenotyping of clinical infections early on and improving patient care by the reducing time to an informed personalized treatment decision.
MALDI-TOF mass spectrometry has been adopted in routine clinical microbiology to identify bacterial and funghi specimen. This technology is inexpensive and delivers MALDI-TOF mass spectra rapidly after sample collection. The spectra also harbour the potential to contain information beyond microbial indentity. My research focuses on using Machine Learning method to extract this additional information and make it accessible to patient care.
Recent work of mine on this topic includes the pre-print for Direct Antimicrobial Resistance Prediction from MALDI-TOF mass spectra profile in clinical isolates through Machine Learning on bioRxiv, Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review published in Clinical Microbiology and Infection and an article on Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra accepted at ISMB 2020, in press at OUP Bioinformatics.
With an interdisciplinary background in microbiology, structural physics and bioinformatics, I enjoy solving real-world problems at the interphase of Machine Learning and Healthcare. Beyond my core PhD project, I like to stay up-to-date on related topics, such as computational epidemiology and protein function prediction.