Single-Cell Analysis and Deep Learning for
Cancer Precision Medicine
Due to genomic and epigenetic instability of cancer cells, inter-patient and intra-patient heterogeneity in tumors creates formidable challenges in identifying optimal treatments. To address the challenges, we aim to establish comprehensive high-throughput multi-omics single-cell analysis including genome, epigenome, transcriptome, proteome, functional, and morphological methods. With large amounts of data collected from high-throughput single-cell multi-omics analysis, machine learning techniques can predict patient prognosis and suggest treatments for precision medicine. The integrated approach will change how we understand and treat cancer and ultimately improve outcomes for patients.
Single-Cell Isolation |
Cancer-Stromal Engulfment |
Single-Cell Retrieval |
Robotic Operation of a Microfluidic Cell Migration Platform
Yu-Chih Chen, Ph.D.
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