Yuhu Liang

GeneBag: training a cell foundation model for broad-spectrum cancer diagnosis and prognosis with bulk RNA-seq data

GeneBag is a cell foundation model trained on single-cell data and fine-tuned on bulk RNA-seq to diagnose and predict cancer prognosis with high accuracy.

N-of-one differential gene expression without control samples using a deep generative model

This paper introduces a deep generative model to perform differential gene expression analysis on single cancer samples without control samples, outperforming DESeq2 in detecting differentially expressed genes.

Prediction of DNA from context using neural networks

The paper trains a neural network to predict DNA bases from context, achieving 54% accuracy on the human genome, outperforming Markov models and revealing periodic signals linked to nucleosome positioning.

Context dependency of nucleotide probabilities and variants in human DNA

The paper develops context-dependent nucleotide models for the human genome, showing they can predict bases with over 50% accuracy and fit somatic variants better than simpler models.