Research interets

1. Deep Learning for Genomics
   • Variants score
   • Regulatory element predict
   • Gene expression prediction model
2. Genotype and phenotype data simulation and software comparison
3. Association analysis and fine mapping
   • molQTL
   • statistical fine-mapping
4. Pan-genomics

Projects

1. PorcineAI-enhancer: Prediction of Pig Enhancer Sequences Using Convolutional Neural Networks

Supervisor: Prof. Liangke Wu

June 2022 — May. 2023, China Agricultural University

In this research, I propose a method of integrating CNN architecture to predict pig enhancer sequences, achieving satisfactory performance. The prediction accuracy is around 90% for general enhancer sequences and over 70% for tissue-specific enhancers.

2. Comparison for association analysis and fine mapping software

Supervisor: Prof. Lingzhao Fang

September 2022 — Now, Aarhus University

In this study, I first simulated genotype and phenotype data, then divided association analysis software and fine mapping software based on statistical principles, and studied their performance differences.