In this seminar, we study current research publications in biomedical data science. Master students either present a research article, or their master thesis, or they present about their research internship. Credit points can only be earned for the presentation of research articles.

Students who present on their research internship, please follow the guidelines outlined here:

https://www.mi.fu-berlin.de/en/bioinf/stud/master/forschungspraktikum/index.html

Schedule

References

How to structure and write research papers

Mensh B, Kording K (2017) Ten simple rules for structuring papers. PLoS Comput Biol 13(9): e1005619. https://doi.org/10.1371/journal.pcbi.1005619

Zhang W (2014) Ten Simple Rules for Writing Research Papers. PLoS Comput Biol 10(1): e1003453. https://doi.org/10.1371/journal.pcbi.1003453

Overview

Wysocka, Magdalena, et al. "A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data." BMC bioinformatics 24.1 (2023): 1-31.

Image analysis

Hägele, M.et al. Resolving challenges in deep learning-based analyses of histopathological images using explanation methods. Sci Rep 10, 6423 (2020).

Tumor microenvironment

Tran, K.A., Addala, V., Johnston, R.L. et al. Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures. Nat Commun 14, 5758 (2023). https://doi.org/10.1038/s41467-023-41385-5

Tumor cell identification

Dohmen, J., Baranovskii, A., Ronen, J. et al. Identifying tumor cells at the single-cell level using machine learning. Genome Biol 23, 123 (2022). https://doi.org/10.1186/s13059-022-02683-1

Cancer of unknown primary

Moiso, Enrico, et al. "Developmental deconvolution for classification of cancer origin." Cancer discovery 12.11 (2022): 2566-2585.

Moon, I., LoPiccolo, J., Baca, S.C. et al. Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary. Nat Med 29, 2057–2067 (2023). https://doi.org/10.1038/s41591-023-02482-6

Intra-tumor heterogeneity

Dentro, Stefan C., et al. "Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes." Cell 184.8 (2021): 2239-2254.

Wu, F., Fan, J., He, Y. et al. Single-cell profiling of tumor heterogeneity and the microenvironment in advanced non-small cell lung cancer. Nat Commun 12, 2540 (2021). https://doi.org/10.1038/s41467-021-22801-0

Morita, K., Wang, F., Jahn, K. et al. Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics. Nat Commun 11, 5327 (2020). https://doi.org/10.1038/s41467-020-19119-8

Levy-Jurgenson, A., Tekpli, X., Kristensen, V.N. et al. Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer. Sci Rep 10, 18802 (2020). https://doi.org/10.1038/s41598-020-75708-z

van Dijk, E., van den Bosch, T., Lenos, K.J. et al. Chromosomal copy number heterogeneity predicts survival rates across cancers. Nat Commun 12, 3188 (2021). https://doi.org/10.1038/s41467-021-23384-6