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Hi, I'm James Leo

I'm a Machine Learning Researcher/Cheminformatics Scientist at Centre for Eye and Vision Research (CEVR, Hong Kong Science Park.

👋 These are the projects I have completed and currently working on at CEVR.

June 2021 -Dec 2021

 Project-1 -Using Machine Learning To Predict Partition Coefficient (Log P) and Distribution Coefficient (Log D) with Molecular Descriptors and Liquid Chromatography Retention Time

• Curated datasets from a variety of public databases, like PubChem and CheMBL
• Developed a machine learning models and deep learning models to rank molecules for the drug candidate.
• Patented the innovation at U.S. Patent and Trademark Office (USPTO) and CNIP.
• Published in American Chemical Society (ACS) recently. More details can be found in here https://pubs.acs.org/doi/10.1021/acs.jcim.2c01373
• GitHub Repository: https://github.com/jamesleocodes/p_chem_CEVR

On-going

 Project-1.1 – Developing iMoleQ which is a cloud-based web application designed to predict molecular properties.

• The frontend development of iMoleQ has been completed, and you can access it at www.imoleq.com.
• Currently, we are in the process of integrating the backend with the API and server-side components.
• GitHub Repository: https://github.com/jamesleocodes/iMoleQ?tab=readme-ov-file

Jan 2022 -Dec 2022

 Project-2 - AI-Enabled Diagnosis of Parkinson’s Disease from Eye Movement Data

• Fitted with analytical wavefunction to simplify the eye movement data.
• Developed and implemented signal processing algorithms to extract meaningful features from raw eye movement data (i.e. in waveforms).
• Built and fine-tuned machine learning models to differentiate Parkinson’s disease patients from healthy controls using eye movement data.
• The innovation from this research is preparing for patent to submit to the U.S. Patent and Trademark Office (USPTO).
• GitHub Repository: https://github.com/jamesleocodes/eye_movement

Jan 2023 -Aug 2023

 Project-3- Diagnosing Diabetic Retinopathy: A Machine Learning Approach to CIL LC-MS Analysis of Tear Sample Metabolites

• Implemented a project on biomarker discovery for diabetic retinopathy disease using metabolomic data processing and machine learning.
• Managed data cleanup, feature engineering, and utilized tree-based algorithms (Gradient Boosting, XGBoost, Random Forest) for accurate disease classification, with potential for real-world clinical diagnostic application. The paper related to the research will be published in American Chemical Society (ACS) soon.
• GitHub link: https://github.com/jamesleocodes/Diabetic_Retinopathy_ML

Sep 2023 -Present

 Project-4 Machine Learning-Assisted Identification of Potential Metabolite Biomarkers for Glaucoma Diagnosis through Serum Metabolomic Analysis

• Developed machine learning models to identify biomarkers for glaucoma from metabolomic data, enhancing diagnostic accuracy.
• Data acquired from analyzing serum samples of participants using high-resolution mass spectrometry.
• Utilized Random Forest, Gradient Boosting, and XGBoost algorithms, achieving up to 0.87 accuracy.
• Identified significant metabolite linked to glaucoma through SHAP analysis.
• The paper related to the research will be published in American Chemical Society (ACS) soon.
• GitHub link: https://github.com/jamesleocodes/glauSerum

👀 I’m interested in coding(code for fun) with python,R, C/C++, SQL and matlab.