It has always demanded from biological assays to provide single-cell level, quantitative, high-throughput data to investigate fundamental questions, to identify rare cells, or to cooperate biological assays with downstream computational methods. Therefore, microfluidic approaches in biological sciences have been emerged as adequate solutions to overcome these limitations. Recently, it will be more obvious that deep learning approaches will significantly contribute thanks to obtained accuracy, precision, and increased sampling rate of biological data by microfluidics. This project will cover analysis of proliferation, morphology, motility, migration, and deformation properties of mammalian cells at single-cell level.
About Project Supervisors
Faculty of Engineering and Natural Science