Daniel Paysan

PhD Student at ETH Zurich and the Paul Scherrer Institute | Machine Learning, Computational Biology


(*Co-first author)

Linking Chromatin Images to Gene Regulation using Genetic Perturbation Screens

Paysan, D.* and Radhakrishnan, A.* et al.

In review, 2024

Imaging and AI based Chromatin Biomarkers for Diagnoses and Therapy evaluation from Liquid Biopsies

Challa, K.* and Paysan, D.* et al.

npj Precision Oncology, 2023

Unsupervised representation learning of chromatin images identifies changes in cell state and tissue organization during DCIS progression.

Zhang X., Venkatachalapathy S., Paysan D. et al.

In review, 2024

Detecting radio- and chemoresistant cells in 3D cancer co-cultures using chromatin biomarkers

Pekec T.* , Venkatachalapathy S.* , Shim A., Paysan, D. et al.

Scientific Reports, 2023

Germinal Center Dark Zone enshrines determinants of T-cell depletion that are traced in lymphomas.

Cancila, V.*, Morello, G.*, Bertolazzi, G.*, Chan A., Bastianello, G., Paysan, D. et al.

In preparation, 2024

Self-supervised representation learning for surgical activity recognition

Daniel Paysan, Luis Haug, Michael Bajka, Markus Oelhafen, Joachim M. Buhmann

International Journal of Computer Assisted Radiology and Surgery, 2021