Shaojun Pan

Shaojun was a phd student in th BDB-Lab at Fudan University, graduating in

1. Prior to joining Fudan, he had obtained his Bachelor's degree in computer science at Nanjing University of Science and Technology. His main interest is deep learning, machine learning and metagenomic data analysis.

Shaojun is mostly known for his work on deep learning and metagenomic data, namely the development of SemiBin

BDB-Lab Projects

1. GMGC: Global Microbial Gene Catalog

BDB-Lab Publications

1. Capturing global pet dog gut microbial diversity and hundreds of near-finished bacterial genomes by using long-read metagenomics in a Shanghai cohort
2. AEMB: a computationally efficient abundance estimation method for metagenomic binning
3. SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing
4. metaMIC: reference-free Misassembly Identification and Correction of de novo metagenomic assemblies
5. A deep siamese neural network improves metagenome-assembled genomes in microbiome datasets across different environments
6. Towards the biogeography of prokaryotic genes
7. Macrel: antimicrobial peptide screening in genomes and metagenomes