De Novo Drug Design with Joint Transformers

no code implementations • 3 Oct 2023 • Adam Izdebski, Ewelina Weglarz-Tomczak, Ewa Szczurek, Jakub M. Tomczak

To address this, we propose Joint Transformer that combines a Transformer decoder, Transformer encoder, and a predictor in a joint generative model with shared weights.

A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients

1 code implementation • 14 Sep 2021 • Adam Izdebski, Patrick J. Thoral, Robbert C. A. Lalisang, Dean M. McHugh, Diederik Gommers, Olaf L. Cremer, Rob J. Bosman, Sander Rigter, Evert-Jan Wils, Tim Frenzel, Dave A. Dongelmans, Remko de Jong, Marco A. A. Peters, Marlijn J. A Kamps, Dharmanand Ramnarain, Ralph Nowitzky, Fleur G. C. A. Nooteboom, Wouter de Ruijter, Louise C. Urlings-Strop, Ellen G. M. Smit, D. Jannet Mehagnoul-Schipper, Tom Dormans, Cornelis P. C. de Jager, Stefaan H. A. Hendriks, Sefanja Achterberg, Evelien Oostdijk, Auke C. Reidinga, Barbara Festen-Spanjer, Gert B. Brunnekreef, Alexander D. Cornet, Walter van den Tempel, Age D. Boelens, Peter Koetsier, Judith Lens, Harald J. Faber, A. Karakus, Robert Entjes, Paul de Jong, Thijs C. D. Rettig, Sesmu Arbous, Lucas M. Fleuren, Tariq A. Dam, Michele Tonutti, Daan P. de Bruin, Paul W. G. Elbers, Giovanni Cinà

Despite the recent progress in the field of causal inference, to date there is no agreed upon methodology to glean treatment effect estimation from observational data.

Causal Inference

Improved Quantum Boosting

no code implementations • 17 Sep 2020 • Adam Izdebski, Ronald de Wolf

Boosting is a general method to convert a weak learner (which generates hypotheses that are just slightly better than random) into a strong learner (which generates hypotheses that are much better than random).