Drugst.One -- A plug-and-play solution for online systems medicine and network-based drug repurposing

no code implementations • 24 May 2023 • Andreas Maier, Michael Hartung, Mark Abovsky, Klaudia Adamowicz, Gary D. Bader, Sylvie Baier, David B. Blumenthal, Jing Chen, Maria L. Elkjaer, Carlos Garcia-Hernandez, Mohamed Helmy, Markus Hoffmann, Igor Jurisica, Max Kotlyar, Olga Lazareva, Hagai Levi, Markus List, Sebastian Lobentanzer, Joseph Loscalzo, Noel Malod-Dognin, Quirin Manz, Julian Matschinske, Miles Mee, Mhaned Oubounyt, Alexander R. Pico, Rudolf T. Pillich, Julian M. Poschenrieder, Dexter Pratt, Nataša Pržulj, Sepideh Sadegh, Julio Saez-Rodriguez, Suryadipto Sarkar, Gideon Shaked, Ron Shamir, Nico Trummer, Ugur Turhan, Ruisheng Wang, Olga Zolotareva, Jan Baumbach

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood.

Drug Discovery

HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning

2 code implementations • 21 May 2021 • Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Julian Matschinske, Jan Baumbach, Daniel Rueckert, Georgios Kaissis

Federated learning (FL) enables multiple clients to jointly train a global model under the coordination of a central server.

BIG-bench Machine Learning Federated Learning +1

The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond

1 code implementation • 12 May 2021 • Julian Matschinske, Julian Späth, Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Anne Hartebrodt, Balázs Orbán, Sándor Fejér, Olga Zolotareva, Mohammad Bakhtiari, Béla Bihari, Marcus Bloice, Nina C Donner, Walid Fdhila, Tobias Frisch, Anne-Christin Hauschild, Dominik Heider, Andreas Holzinger, Walter Hötzendorfer, Jan Hospes, Tim Kacprowski, Markus Kastelitz, Markus List, Rudolf Mayer, Mónika Moga, Heimo Müller, Anastasia Pustozerova, Richard Röttger, Anna Saranti, Harald HHW Schmidt, Christof Tschohl, Nina K Wenke, Jan Baumbach

Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas and are driven by the increasing amount of available data.

Federated Learning Privacy Preserving

Flimma: a federated and privacy-preserving tool for differential gene expression analysis

1 code implementation • 30 Oct 2020 • Olga Zolotareva, Reza Nasirigerdeh, Julian Matschinske, Reihaneh Torkzadehmahani, Tobias Frisch, Julian Späth, David B. Blumenthal, Amir Abbasinejad, Paolo Tieri, Nina K. Wenke, Markus List, Jan Baumbach

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights.

Privacy Preserving

Privacy-preserving Artificial Intelligence Techniques in Biomedicine

no code implementations • 22 Jul 2020 • Reihaneh Torkzadehmahani, Reza Nasirigerdeh, David B. Blumenthal, Tim Kacprowski, Markus List, Julian Matschinske, Julian Späth, Nina Kerstin Wenke, Béla Bihari, Tobias Frisch, Anne Hartebrodt, Anne-Christin Hausschild, Dominik Heider, Andreas Holzinger, Walter Hötzendorfer, Markus Kastelitz, Rudolf Mayer, Cristian Nogales, Anastasia Pustozerova, Richard Röttger, Harald H. H. W. Schmidt, Ameli Schwalber, Christof Tschohl, Andrea Wohner, Jan Baumbach

Artificial intelligence (AI) has been successfully applied in numerous scientific domains.

Privacy Preserving

Exploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing

no code implementations • 26 Apr 2020 • Sepideh Sadegh, Julian Matschinske, David B. Blumenthal, Gihanna Galindez, Tim Kacprowski, Markus List, Reza Nasirigerdeh, Mhaned Oubounyt, Andreas Pichlmair, Tim Daniel Rose, Marisol Salgado-Albarrán, Julian Späth, Alexey Stukalov, Nina K. Wenke, Kevin Yuan, Josch K. Pauling, Jan Baumbach

The web interface allows user-friendly visual exploration of the virus-host interactome and implements systems medicine algorithms for network-based prediction of drugs.