Lizhen Shi

This study evaluates FP8 quantization in GenomeOcean, finding it preserves biological fidelity while improving efficiency, but throughput gains fall short of theoretical limits.

The paper introduces GenomeOcean, a large-scale generative model trained on metagenomic data, to explore the genomic manifold hypothesis and demonstrates its alignment with another model.

The paper evaluates metagenomic software tools using realistic datasets, highlighting improvements in assembly and taxon profiling while identifying remaining challenges in virus/archaea detection and reproducibility.

This paper introduces a hybrid clustering algorithm for metagenome assembly that improves accuracy and scalability by combining long and short-read clustering.

The paper improves short read clustering for metagenome assembly by reducing under-clustering through sample-based statistics, showing effectiveness on mouse gut microbiome data.

This paper develops a method to represent DNA sequences using k-mer embeddings with Locality Sensitive Hashing, improving efficiency and accuracy in metagenomic analysis.

SpaRC clusters large-scale sequence data using Apache Spark, achieving scalability and performance across transcriptomics and metagenomics datasets.