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Our group has developed prototypes of computerized tools for image cytometry and phenomics to study effects of drugs that inhibit DNA methylation in cancer cell lines. Observed spectra of drug-induced effects were very complex and may go far beyond the reactivation of abnormally silenced suppressor genes. Our proposed quantitative phenotyping has so far allowed certain features of cell structure and function to be correlated with drug treatment schedules. Tools developed and knowledge gained through this project can be used to better elucidate the biological effects of new drugs and optimize the timing and efficacy of compound screening.
Other ongoing projects involve numerical characterization of tumor growth patterns to aid with challenging histopathological diagnoses. Image data captured via high-resolution tissue microscopy is used to develop machine learning tools for an automated screening of cytology and histopathology preparations. Our particular focus is cancer grading by image analysis based on tissue morphometry with or without involvement of novel cancer biomarkers. Developed tools are tested and validated in collaboration with professionals in histopathology and molecular pathology.