Zobrazeno 1 - 10
of 289
pro vyhledávání: '"Claassen Manfred"'
We develop a framework for derivative Gaussian process latent variable models (DGP-LVM) that can handle multi-dimensional output data using modified derivative covariance functions. The modifications account for complexities in the underlying data ge
Externí odkaz:
http://arxiv.org/abs/2404.04074
Publikováno v:
ITM Web of Conferences, Vol 5, p 00010 (2015)
Single cell sequencing and proteome profiling efforts in the past few years have revealed widespread genetic and proteomic heterogeneity among tumor cells. However, sensible cell-type definition of such heterogeneous cell populations has so far been
Externí odkaz:
https://doaj.org/article/ea194ac5994b45fcb0b4ab3e87a7315d
Autor:
Garrido, Quentin, Damrich, Sebastian, Jäger, Alexander, Cerletti, Dario, Claassen, Manfred, Najman, Laurent, Hamprecht, Fred
Publikováno v:
Bioinformatics, Oxford University Press (OUP), In press
Motivation: Single cell RNA sequencing (scRNA-seq) data makes studying the development of cells possible at unparalleled resolution. Given that many cellular differentiation processes are hierarchical, their scRNA-seq data is expected to be approxima
Externí odkaz:
http://arxiv.org/abs/2102.05892
Autor:
Babaei, Sepideh, Christ, Jonathan, Sehra, Vivek, Makky, Ahmad, Zidane, Mohammed, Wistuba-Hamprecht, Kilian, Schürch, Christian, Claassen, Manfred
Publikováno v:
In Patterns 8 September 2023 4(9)
Clustering high-dimensional data, such as images or biological measurements, is a long-standingproblem and has been studied extensively. Recently, Deep Clustering has gained popularity due toits flexibility in fitting the specific peculiarities of co
Externí odkaz:
http://arxiv.org/abs/1910.07763
Autor:
Ruf, Benjamin, Bruhns, Matthias, Babaei, Sepideh, Kedei, Noemi, Ma, Lichun, Revsine, Mahler, Benmebarek, Mohamed-Reda, Ma, Chi, Heinrich, Bernd, Subramanyam, Varun, Qi, Jonathan, Wabitsch, Simon, Green, Benjamin L., Bauer, Kylynda C., Myojin, Yuta, Greten, Layla T., McCallen, Justin D., Huang, Patrick, Trehan, Rajiv, Wang, Xin, Nur, Amran, Murphy Soika, Dana Qiang, Pouzolles, Marie, Evans, Christine N., Chari, Raj, Kleiner, David E., Telford, William, Dadkhah, Kimia, Ruchinskas, Allison, Stovroff, Merrill K., Kang, Jiman, Oza, Kesha, Ruchirawat, Mathuros, Kroemer, Alexander, Wang, Xin Wei, Claassen, Manfred, Korangy, Firouzeh, Greten, Tim F.
Publikováno v:
In Cell 17 August 2023 186(17):3686-3705
Autor:
Kotsiliti, Elena, Leone, Valentina, Schuehle, Svenja, Govaere, Olivier, Li, Hai, Wolf, Monika J., Horvatic, Helena, Bierwirth, Sandra, Hundertmark, Jana, Inverso, Donato, Zizmare, Laimdota, Sarusi-Portuguez, Avital, Gupta, Revant, O’Connor, Tracy, Giannou, Anastasios D., Shiri, Ahmad Mustafa, Schlesinger, Yehuda, Beccaria, Maria Garcia, Rennert, Charlotte, Pfister, Dominik, Öllinger, Rupert, Gadjalova, Iana, Ramadori, Pierluigi, Rahbari, Mohammad, Rahbari, Nuh, Healy, Marc E., Fernández-Vaquero, Mirian, Yahoo, Neda, Janzen, Jakob, Singh, Indrabahadur, Fan, Chaofan, Liu, Xinyuan, Rau, Monika, Feuchtenberger, Martin, Schwaneck, Eva, Wallace, Sebastian J., Cockell, Simon, Wilson-Kanamori, John, Ramachandran, Prakash, Kho, Celia, Kendall, Timothy J., Leblond, Anne-Laure, Keppler, Selina J., Bielecki, Piotr, Steiger, Katja, Hofmann, Maike, Rippe, Karsten, Zitzelsberger, Horst, Weber, Achim, Malek, Nisar, Luedde, Tom, Vucur, Mihael, Augustin, Hellmut G., Flavell, Richard, Parnas, Oren, Rad, Roland, Pabst, Olivier, Henderson, Neil C., Huber, Samuel, Macpherson, Andrew, Knolle, Percy, Claassen, Manfred, Geier, Andreas, Trautwein, Christoph, Unger, Kristian, Elinav, Eran, Waisman, Ari, Abdullah, Zeinab, Haller, Dirk, Tacke, Frank, Anstee, Quentin M., Heikenwalder, Mathias
Publikováno v:
In Journal of Hepatology August 2023 79(2):296-313
Autor:
Wietecha, Mateusz S., Lauenstein, David, Cangkrama, Michael, Seiler, Sybille, Jin, Juyoung, Goppelt, Andreas, Claassen, Manfred, Levesque, Mitchell P., Dummer, Reinhard, Werner, Sabine
Publikováno v:
In Matrix Biology May 2023 119:19-56
Autor:
Arvaniti, Eirini, Claassen, Manfred
Automated grading of prostate cancer histopathology images is a challenging task, with one key challenge being the scarcity of annotations down to the level of regions of interest (strong labels), as typically the prostate cancer Gleason score is kno
Externí odkaz:
http://arxiv.org/abs/1811.07013
Publikováno v:
In Cell Reports Methods 19 December 2022 2(12)