Genomics

Robust Multi-Omics Prediction for RNA Expression & Protein Surface Levels
Developed and optimized machine learning models to predict RNA expression from chromatin accessibility data and protein levels from RNA expression in individual cells. Preprocessed large single-cell genomics datasets, implemented regression algorithms, and tuned hyperparameters. Achieved high accuracy on test data, demonstrating the feasibility of predicting across modalities. Overall, this project showcases expertise in genomics, predictive modeling, and working with big data.
GitHub Paper Presentation
Robust Multi-Omics Prediction for RNA Expression & Protein Surface Levels
Machine Learning Prediction of CITE-seq Protein Expression from scRNA-seq Data
Our project, based on a Kaggle competition, was to work on the prediction of cell surface protein expression (CITE-seq) from single-cell RNA expression data (scRNA-seq).
GitHub Report
Machine Learning Prediction of CITE-seq Protein Expression from scRNA-seq Data