Max Horn
Max Horn
PhD Student in Machine Learning and Computational Biology, ETH Zürich
Verified email at bsse.ethz.ch - Homepage
Title
Cited by
Cited by
Year
Post-antibiotic gut mucosal microbiome reconstitution is impaired by probiotics and improved by autologous FMT
J Suez, N Zmora, G Zilberman-Schapira, U Mor, M Dori-Bachash, ...
Cell 174 (6), 1406-1423. e16, 2018
4772018
Early prediction of circulatory failure in the intensive care unit using machine learning
SL Hyland, M Faltys, M Hüser, X Lyu, T Gumbsch, C Esteban, C Bock, ...
Nature medicine 26 (3), 364-373, 2020
512020
Neural persistence: A complexity measure for deep neural networks using algebraic topology
B Rieck, M Togninalli, C Bock, M Moor, M Horn, T Gumbsch, K Borgwardt
International Conference on Learning Representations, 2019
452019
Early recognition of sepsis with Gaussian process temporal convolutional networks and dynamic time warping
M Moor, M Horn, B Rieck, D Roqueiro, K Borgwardt
Machine Learning for Healthcare Conference, 2-26, 2019
33*2019
Topological autoencoders
M Moor, M Horn, B Rieck, K Borgwardt
International Conference on Machine Learning, 7045-7054, 2020
292020
TRAPP webserver: predicting protein binding site flexibility and detecting transient binding pockets
A Stank, DB Kokh, M Horn, E Sizikova, R Neil, J Panecka, S Richter, ...
Nucleic acids research 45 (W1), W325-W330, 2017
262017
Set functions for time series
M Horn, M Moor, C Bock, B Rieck, K Borgwardt
International Conference on Machine Learning, 4353-4363, 2020
152020
Backbone circularization of Bacillus subtilis family 11 xylanase increases its thermostability and its resistance against aggregation
MC Waldhauer, SN Schmitz, C Ahlmann-Eltze, JG Gleixner, CC Schmelas, ...
Molecular BioSystems 11 (12), 3231-3243, 2015
112015
Path Imputation Strategies for Signature Models of Irregular Time Series
M Moor, M Horn, C Bock, K Borgwardt, B Rieck
arXiv preprint arXiv:2005.12359, 2020
4*2020
Supervised learning on synthetic data for reverse engineering gene regulatory networks from experimental time-series
S Ganscha, V Fortuin, M Horn, E Arvaniti, M Claassen
bioRxiv, 356477, 2018
32018
Early prediction of sepsis in the ICU using machine learning: a systematic review
M Moor, B Rieck, M Horn, CR Jutzeler, K Borgwardt
Frontiers in medicine 8, 348, 2021
22021
Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra
C Weis, M Horn, B Rieck, A Cuénod, A Egli, K Borgwardt
Bioinformatics 36 (Supplement_1), i30-i38, 2020
22020
Topological graph neural networks
M Horn, E De Brouwer, M Moor, Y Moreau, B Rieck, K Borgwardt
arXiv preprint arXiv:2102.07835, 2021
12021
Challenging euclidean topological autoencoders
M Moor, M Horn, K Borgwardt, B Rieck
NeurIPS 2020 Workshop on Topological Data Analysis and Beyond, 2020
12020
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning
M Moor, N Bennet, D Plecko, M Horn, B Rieck, N Meinshausen, ...
arXiv preprint arXiv:2107.05230, 2021
2021
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions
L O'Bray, M Horn, B Rieck, K Borgwardt
arXiv preprint arXiv:2106.01098, 2021
2021
Translational Equivariance in Kernelizable Attention
M Horn, K Shridhar, E Groenewald, PFM Baumann
arXiv preprint arXiv:2102.07680, 2021
2021
Exploring target flexibility for drug design
D Kokh, A Stank, M Horn, E Sizikova, R Neil, J Panecka, S Richter, ...
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 254, 2017
2017
BBF RFC 105: The Intein standard-a universal way to modify proteins after translation
C Ahlmann-Eltze, C Bunne, M Büscher, J Gleixner, M Horn, A Huhn, ...
2015
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Articles 1–19