Billy Zeng

OCTAD: an open workplace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features

OCTAD enables virtual screening of therapeutics for cancer patient groups using gene expression features, prioritizing potent drug candidates for experimental testing.

Evaluating cell lines as models for metastatic cancer through integrative analysis of open genomic data

This study evaluates the genomic similarity between breast cancer cell lines and metastatic samples, finding significant differences and highlighting organoids as better models.

Selecting precise reference normal tissue samples for cancer research using a deep learning approach

The paper evaluates using GTEx normal tissue samples as references for cancer research and finds that a deep learning approach can effectively select appropriate normal samples for disease signature creation.