Data Technology and Analysis Department - Data Technology AI Team

Responsibilities:

• Graph-based and language model development for Protein-protein Interaction (PPI) prediction and antibiotic discovery

Responsibilities:

• Conducted research on deep learning techniques in phenotypic drug discovery against glioblastoma cells.

• Developed and optimized a graph neural network for feature extraction, combining information from RDkit and features extracted from a large pre-trained language model (ChemBERTa).

• Improved model architecture and evaluation metrics, particularly for handling imbalanced data.

Effects of red phosphorus on metabolism of microorganisms. Supervised by Dr. Liang Feng.

Responsibilities:

• Investigated the effects of red phosphorus on the metabolism of microorganisms.

• Utilized Gradient Boosting Tree and Random Forest algorithms to predict the optimal concentration of different types of red phosphorus using transcriptomics data and visible light wavelength.

• Conducted data cleaning, analysis, and visualization using Python.

Neosinocalamus growth prediction using image data

Responsibilities:

• Analyzed the growth and development of Neosinocalamus affinis using image data.

• Collected and processed leaves from Neosinocalamus affinis.

• Applied K-means clustering to analyze the correlation between the size, number, and positions of phytoliths and the growth of Neosinocalamus affinis.

• Identified the connection between the cells’ structure and components at the molecular level and the growth of Neosinocalamus affinis.