no code implementations • 16 Jan 2024 • Manuel Tran, Amal Lahiani, Yashin Dicente Cid, Melanie Boxberg, Peter Lienemann, Christian Matek, Sophia J. Wagner, Fabian J. Theis, Eldad Klaiman, Tingying Peng
Vision Transformers (ViTs) and Swin Transformers (Swin) are currently state-of-the-art in computational pathology.
no code implementations • 13 Nov 2023 • Micaela E. Consens, Cameron Dufault, Michael Wainberg, Duncan Forster, Mehran Karimzadeh, Hani Goodarzi, Fabian J. Theis, Alan Moses, Bo wang
In the rapidly evolving landscape of genomics, deep learning has emerged as a useful tool for tackling complex computational challenges.
1 code implementation • 4 Nov 2023 • Jan P. Engelmann, Alessandro Palma, Jakub M. Tomczak, Fabian J. Theis, Francesco Paolo Casale
Predicting patient features from single-cell data can help identify cellular states implicated in health and disease.
no code implementations • 23 Oct 2023 • Alejandro Tejada-Lapuerta, Paul Bertin, Stefan Bauer, Hananeh Aliee, Yoshua Bengio, Fabian J. Theis
Advances in single-cell omics allow for unprecedented insights into the transcription profiles of individual cells.
no code implementations • 2 Jul 2023 • Hananeh Aliee, Ferdinand Kapl, Soroor Hediyeh-Zadeh, Fabian J. Theis
Specifically, the proposed approach helps to disentangle biological signals from data biases that are unrelated to the target task or the causal explanation of interest.
no code implementations • NeurIPS 2023 • Manuel Tran, Yashin Dicente Cid, Amal Lahiani, Fabian J. Theis, Tingying Peng, Eldad Klaiman
We introduce LoReTTa (Linking mOdalities with a tRansitive and commutativE pre-Training sTrAtegy) to address this understudied problem.
no code implementations • 14 May 2022 • Scott Gigante, Varsha G. Raghavan, Amanda M. Robinson, Robert A. Barton, Adeeb H. Rahman, Drausin F. Wulsin, Jacques Banchereau, Noam Solomon, Luis F. Voloch, Fabian J. Theis
Translating the relevance of preclinical models ($\textit{in vitro}$, animal models, or organoids) to their relevance in humans presents an important challenge during drug development.
no code implementations • 23 Jun 2021 • Hananeh Aliee, Fabian J. Theis, Niki Kilbertus
Spurred by tremendous success in pattern matching and prediction tasks, researchers increasingly resort to machine learning to aid original scientific discovery.
1 code implementation • 4 Oct 2019 • Mohammad Lotfollahi, Mohsen Naghipourfar, Fabian J. Theis, F. Alexander Wolf
While generative models have shown great success in generating high-dimensional samples conditional on low-dimensional descriptors (learning e. g. stroke thickness in MNIST, hair color in CelebA, or speaker identity in Wavenet), their generation out-of-sample poses fundamental problems.