- Worked as a co-first author to completely re-code the previous R program in Python
- Optimized the system with parallel computing to improve calculating speed dramatically
- Applied statistics in the feature selection to rank the biomedical features with statistical association
evaluation algorithm T-test
- Compared common machine learning algorithms such as Support Vector Machine (SVM), K
Nearest Neighbors (KNN), Decision Tree (DTree) ,Naïve Bayesian classifier (NBayes), Logistic
Regression (LR)
- Proposed and tested a few novels ideas in the RIFS algorithm which outperforms the existing filter
and wrapper feature selection algorithm on both transcriptomic and methylomic datasets
- Published the paper on Scientific Reports