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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

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Yu-Chih Chen, Ph.D.
UPMC Hillman Cancer Center, Department of Computational and Systems Biology,
University of Pittsburgh School of Medicine
CMU-Pitt Ph.D. Program in Computational Biology
Department of Bioengineering, Swanson School of Engineering,  University of Pittsburgh

5115 Centre Avenue Pittsburgh, PA 15232
Email: cheny25@upmc.edu


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