Huaiqiu Zhu

Tripleknock predicts lethal effects of three-gene knockouts in bacteria using deep learning, achieving faster predictions than FBA with high cross-species accuracy.

KuafuPrimer uses machine learning to design 16S rRNA gene primers with minimal bias, improving taxonomic accuracy and detecting rare taxa missed by universal primers.

eBiota designs microbial communities from large strain pools to generate target products using optimization and deep learning, showing accurate predictions and experimental validation.

This paper introduces VirHRanger, a foundation model for predicting viral host range using genomic and protein data, achieving high accuracy across diverse viruses and host categories.

IPEV identifies and classifies prokaryotic and eukaryotic virus-derived sequences in viromes using deep learning, achieving higher precision and efficiency than existing methods.

DeePhage is a deep learning tool that classifies virulent and temperate phage-derived sequences in metavirome data with high accuracy and speed.

HoPhage identifies phage hosts from metavirome short reads using deep learning and a Markov chain model, achieving good performance on short fragments across a wide host range.

The paper analyzes micro-inversions in human and nonhuman primate genomes to explore their role in population genetics and evolutionary history.

NanoReviser improves nanopore sequencing accuracy using deep learning, reducing error rates by over 5% in E. coli and human datasets.

The paper re-evaluates the performance of LncADeep using its default settings and finds it outperforms other tools on the same datasets.