cv

Master

University of Electronic Science and Technology of China, Chengdu, China

Computer Science and Technology

• Bioinformatics
• Sequence Classification

Bachelor

Donghua University, Shanghai, China

Software Engineering

First Prize in CBC2023 Data Challenge

Bioinformatics Committee of the China Computer Federation

The CBC2023 Data Challenge aims to promote innovation and application in data science and bioinformatics. Our project excelled in the classification of protein superfamilies, earning the first prize.

Application and Comparison of Machine Learning and Database-Based Methods in Taxonomic Classification of High-Throughput Sequencing Data

Genome Biology and Evolution

Our study compared database-based and machine learning methods for taxonomic classification of high-throughput sequencing data. We found that database-based methods excel with comprehensive databases, while machine learning methods perform better with sparse references. Integrating multiple database-based methods enhances accuracy. These findings are crucial for computational biology. Source code and additional resources are available at https://github.com/LoadStar822/Genome-Classifier-Performance-Evaluator and http://lab.malab.cn/~tqz/project/taxonomic/.

FMAlign2: a novel fast multiple nucleotide sequence alignment method for ultralong datasets

Bioinformatics

Our project, FMAlign2, addresses the challenge of aligning ultralong sequences in bioinformatics. Utilizing a vertical division strategy and the suffix array, FMAlign2 segments and aligns sequences in parallel, significantly reducing time while maintaining accuracy. This method enhances existing MSA techniques, efficiently handling sequences of billions in length. Source code and datasets are available at https://github.com/malabz/FMAlign2