Machine learning for computational biology
We develop and apply machine learning (ML) and artificial intelligence (AI) methods for the analysis of large biomedical datasets. For example, we work on interpretable deep learning to make machine learning more useful for biomedical research.
Machine learning has emerged as a versatile approach for predicting a wide range of biological phenomena. However, its utility for biological discovery has been limited, given that machine learning methods (including the popular and powerful deep neural networks) typically provide little insight into the biological mechanisms that underlie a successful prediction.
We have a long-standing interest in making machine-learning algorithms more interpretable and useful for biological discovery and biomedical applications. Most recently, we have proposed the concept of “knowledge-primed neural networks” for interpretable deep learning on gene-regulatory networks which allows us to infer the activity of key regulatory proteins from single-cell sequencing data, including signaling pathways and protein activity states that are normally hidden to sequencing-based methods.
The power of interpretable machine learning for advancing biomedical research is further illustrated by a series of studies in which we applied machine learning to patient cohorts, for example for time series modelling of the drug response in patients with chronic lymphocytic leukemia and for the identification of drug new combinations for targeted therapy. Moreover, we successfully used machine learning to reconstruct normal and aberrant stem cell differentiation in the blood.
Publications
Single-cell and spatial transcriptomics reveal aberrant lymphoid developmental programs driving granuloma formation
Krausgruber T, Redl A#, Barreca D#, Doberer K, Romanovskaia D, Dobnikar L, Guarini M, Unterluggauer L, Kleissl L, Atzmüller D, Mayerhofer C, Kopf A, Saluzzo S, Lim CX, Rexie P, Weichhart T, Bock C*, Stary G*
Immunity 56, 289-306 (2023). DOI: 10.1016/j.immuni.2023.01.014
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Comparative analysis of genome-scale, base-resolution DNA methylation profiles across 580 animal species
Klughammer J, Romanovskaia D#, Nemc A, Posautz A, Seid C, Linda, Schuster, Keinath M, Ramos JSL, Kosak L, Evankow A, Prinz D, Kirchberger S, Datlinger P, Fortelny N, Schmidl C, Farlik M, Kaja, Skjærven, Bergthaler A, Liedvogel M, Thaller D, Burger PA, Hermann0 M, Distel M, Distel DL, Kübber-Heiss A, Bock C
Nature Communications 14, 232 (2023). DOI: 10.1038/s41467-022-34828-y
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Multimodal analysis of cell-free DNA whole-genome sequencing for pediatric cancers with low mutational burden
Peneder P, Stütz AM#, Surdez D, Krumbholz M, Semper S, Chicard M, Sheffield NC, Pierron G, Lapouble E, Totzl M, Erguner B, Barreca D, Rendeiro AF, Agaimy A, Boztug H, Engstler G, Dworzak M, Bernkopf M, Taschner-Mandl S, Ambros IM, Myklebost O, Marec-Bérard P, Burchill SA, Brennan B, Strauss SJ, Whelan J, Schleiermacher G, Schaefer C, Dirksen U, Hutter C, Boye K, Ambros PF, Delattre O, Metzler M, Bock C*, Tomazou EM*
Nature Communications 12, 3230 (2021). DOI: 10.1038/s41467-021-23445-w
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Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data
Fortelny N, Bock C*
Genome Biology 21, 190 (2020). DOI: 10.1186/s13059-020-02100-5
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Chromatin mapping and single-cell immune profiling defines the temporal dynamics of ibrutinib drug response in chronic lymphocytic leukemia
Rendeiro AF, Krausgruber T#, Fortelny N, Zhao F, Penz T, Farlik M, Schuster LC, Kuchler A, Tasnády S, Réti M, Zoltán M, Alpar D#, Bödör C#, Schmidl C#, Bock C*
Nature Communications 11, 577 (2020). DOI: 10.1038/s41467-019-14081-6
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Combined chemosensitivity and chromatin profiling prioritizes drug combinations in CLL
Schmidl C, Vladimer GI#, Rendeiro AF#, Schnabl S#, Krausgruber T, Taubert C, Krall N, Pemovska T, Araghi M, Snijder B, Hubmann R, Ringler A, Runggatscher K, Demirtas D, de la Fuente OL, Hilgarth M, Skrabs C, Porpaczy E, Gruber M, Hoermann G, Kubicek S, Staber PB, Shehata M#, Superti-Furga G#, Jäger U#, Bock C*
Nature Chemical Biology 15, 232-240 (2019). DOI: 10.1038/s41589-018-0205-2
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DNA methylation dynamics of human hematopoietic stem cell differentiation
Farlik M, Halbritter F#, Müller F#, Choudry FA, Ebert P, Klughammer J, Farrow S, Santoro A, Ciaurro V, Mathur A, Uppal R, Stunnenberg HG, Ouwehand WH, Laurenti E, Lengauer T, Frontini M*, Bock C*
Cell Stem Cell 19, 808-822 (2016). DOI: 10.1016/j.stem.2016.10.019
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Chromatin accessibility maps of chronic lymphocytic leukaemia identify subtype-specific epigenome signatures and transcription regulatory networks
Rendeiro AF, Schmidl C#, Strefford JC#, Walewska R, Davis Z, Farlik M, Oscier D, Bock C*
Nature Communications 7, 11938 (2016). DOI: 10.1038/ncomms11938
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Managing drug resistance in cancer: Lessons from HIV therapy
Bock C*, Lengauer T
Nature Reviews Cancer 12, 494-501 (2012). DOI: 10.1038/nrc3297
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Project website
Press release
* shared first or shared senior authorship