Jieping Ye

LucaCell is a sequence-centric foundation model that improves cross-species single-cell analysis by using mRNA sequence embeddings instead of fixed gene identifiers.

LucaVirus predicts viral evolution and function using a unified nucleotide-protein language model, achieving state-of-the-art results in key virology tasks.

The paper introduces a brain-inspired framework for domain adaptation in AI, using recurrent neural networks to create transferable representations that enable rapid few-shot adaptation with fewer parameters.

This paper introduces LucaOne, a foundation model trained on nucleic acid and protein sequences, demonstrating understanding of biological principles and performing well on related tasks.

Generanno is a genomic foundation model optimized for metagenomic annotation, achieving superior accuracy in coding region identification and enabling pseudogene prediction and taxonomic classification without reference databases.

InstructPLM aligns a protein language model with structure instructions to generate diverse proteins, improving performance over existing models in sequence recovery and activity.

This paper introduces FMFS, a framework for selecting hippocampal morphometry features linked to Aβ/tau pathology, showing it outperforms existing methods and identifies key subregions for biomarker development.

This paper develops a method to predict brain amyloid burden using MRI-based morphometry statistics and sparse coding, achieving high accuracy in ADNI and OASIS cohorts.