Pei Zhuang

This paper develops a high-throughput BTB assembloid model to study glioblastoma-neurovascular interactions and therapeutic responses, finding that BTB disruption correlates with increased tumor invasiveness and drug resistance.

ViPal improves influenza virulence prediction by integrating prior viral knowledge into machine learning models using genomic sequences.

This paper isolates exosomes from human perilymph to enable molecular diagnosis of sensorineural hearing loss by targeting sensory hair cells using myosin VII expression.

IAV-CNN predicts influenza A virus antigenic variants using a 2D convolutional neural network with a new amino acid representation, achieving state-of-the-art performance on three datasets.

VirPreNet predicts influenza A virus virulence using all 8 viral segments with a weighted ensemble CNN, achieving state-of-the-art performance and highlighting the importance of PB2 and HA segments.