Xiaowen Hu

Nov 4, 2024 | biorxiv.org | Changhao Ge |Xiaowen Hu |Lin Zhang |Hongzhe Li

AbstractTranscriptome sequencing (RNA-seq) is widely used in cancer research to study the transcriptome and its role in disease progression. Somatic copy number aberrations (SCNAs) are key drivers of cancer development, and inferring SCNAs from RNA-seq data can provide critical insights for disease classification and treatment prediction. We introduce RCANE, a deep learning-based method designed to predict genome-wide SCNAs across various cancer types using RNA-seq data.

Sep 22, 2023 | mdpi.com | Liangliang Zhang |Ming Chen |Xiaowen Hu |Lei Deng

2.6. Case StudiesThis study incorporated comprehensive case studies to substantiate the efficacy of our prediction model, GCLSDA, in forecasting plausible snoRNA–disease associations. The focus was directed towards two specific case studies: colorectal carcinoma and osteosarcoma. For the colorectal carcinoma [26] case study, a meticulous approach was adopted.