bohao ZOU

Pre-binning correction of chimeric contigs reduces reference-discordant gene annotations in near-complete metagenome-assembled genomes

The paper introduces DeepMM, a deep learning method to correct chimeric contigs in MAGs, improving gene annotation accuracy and reducing reference-discordant genes and virulence-factor calls.

Decoding heterogeneous aging clocks and disease risk stratification using MetAgeFormer

MetAgeFormer is a transformer-based model pre-trained on metabolomic data to estimate biological age and identify metabolic subtypes associated with disease risk.

CompleteBin: A transformer-based framework unlocks microbial dark matter through improved short contig binning

CompleteBin improves short contig binning in complex microbial communities, recovering more complete genomes and novel genes, with applications in environmental metagenomics.

DeepMM: Identify and correct Metagenome Misassemblies with deep learning

DeepMM identifies and corrects metagenomic misassemblies using deep learning, improving downstream binning and increasing near-complete MAGs in a real dataset.

Deepurify: a multi-modal deep language model to remove contamination from metagenome-assembled genomes

Deepurify uses a multi-modal deep language model with contrastive learning to improve MAG decontamination by leveraging genomic sequences and taxonomic lineages.

Recognition Genes Which Are Relative With Cancer By Deep Learning Algorithm

This paper proposes a deep learning method to identify cancer-related genes using semi-supervised and unsupervised algorithms, showing improved performance over traditional statistical methods.