Paul Scherer

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	All	Since 2019
Citations	512	511
h-index	6	6
i10-index	5	5

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Pietro LiòProfessor, University of CambridgeVerified email at cam.ac.uk
Helena Andres TerreAssociate Director, UCB PharmaVerified email at ucb.com
Nikola SimidjievskiUniversity of Cambridge, United KingdomVerified email at cam.ac.uk
Zohreh ShamsML Researcher, University of Cambridge | ML Scientist, Builder.aiVerified email at cam.ac.uk
Rik SarkarReader (Associate Professor), University of EdinburghVerified email at inf.ed.ac.uk
Benedek RozemberczkiSenior Research Scientist, Isomorphic LabsVerified email at isomorphiclabs.com
Olivér KissSenior Data Scientist, SpreadmonitorVerified email at spreadmonitor.com
Maria AstefanoaeiIT University of CopenhagenVerified email at itu.dk
George PanagopoulosUniversity of LuxembourgVerified email at polytechnique.edu
Yixuan HeArizona State UniversityVerified email at stats.ox.ac.uk
Nicolas CollignonKale AIVerified email at kale.ai
Ferenc BéresInstitute for Computer Science and Control (SZTAKI)Verified email at sztaki.hu
Alexander RiedelErnst-Abbe University of Applied Sciences JenaVerified email at eah-jena.de
Cristian BodnarSilurianVerified email at silurian.ai
Ifrah TariqMassachusetts Institute of TechnologyVerified email at mit.edu
Tamás FerenciAssociate professor, Obuda UniversityVerified email at nik.uni-obuda.hu
Maja TrębaczOpenAIVerified email at openai.com
Botty DimanovUniversity of CambridgeVerified email at cl.cam.ac.uk
Sumaiyah KolaUniversity of CambridgeVerified email at cam.ac.uk
Ramon Viñas TornéUniversity of CambridgeVerified email at cam.ac.uk

Paul Scherer

University of Cambridge

Verified email at cam.ac.uk - Homepage

machine learning graph-structured data bioinformatics


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Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models B Rozemberczki, P Scherer, Y He, G Panagopoulos, A Riedel, ... Proceedings of the 30th ACM international conference on information …, 2021	229	2021
Variational autoencoders for cancer data integration: design principles and computational practice N Simidjievski, C Bodnar, I Tariq, P Scherer, HA Terre, Z Shams, ... Frontiers in Genetics 10, 2019	133	2019
Associations between maternal physical activity in early and late pregnancy and offspring birth size: remote federated individual level meta‐analysis from eight cohort studies S Pastorino, T Bishop, SR Crozier, C Granström, K Kordas, LK Küpers, ... BJOG: An International Journal of Obstetrics & Gynaecology 126 (4), 459-470, 2019	80	2019
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks B Rozemberczki, P Scherer, O Kiss, R Sarkar, T Ferenci WWW’21: Graph Learning Benchmarks Workshop, 2021	39	2021
REM: An integrative rule extraction methodology for explainable data analysis in healthcare Z Shams, B Dimanov, S Kola, N Simidjievski, HA Terre, P Scherer, ... medRxiv, 2021.01. 25.21250459, 2021	12	2021
Learning distributed representations of graphs with Geo2DR P Scherer, P Lio Graph Representation Learning and Beyond Workshop (ICML'20), 2020	6	2020
Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases P Scherer, M Trębacz, N Simidjievski, R Viñas, Z Shams, HA Terre, ... Bioinformatics 38 (5), 1320-1327, 2022	4	2022
Discrete Lagrangian neural networks with automatic symmetry discovery Y Lishkova, P Scherer, S Ridderbusch, M Jamnik, P Liò, S Ober-Blöbaum, ... IFAC-PapersOnLine 56 (2), 3203-3210, 2023	3	2023
Using ontology embeddings for structural inductive bias in gene expression data analysis M Trębacz, Z Shams, M Jamnik, P Scherer, N Simidjievski, HA Terre, ... 15th Machine Learning in Computational Biology (MLCB'20), 2020	3	2020
Distributed representations of graphs for drug pair scoring P Scherer, P Liò, M Jamnik Learning on Graphs Conference, 22: 1-22: 17, 2022	2	2022
Decoupling feature propagation from the design of graph auto-encoders P Scherer, H Andres-Terre, P Lio, M Jamnik arXiv preprint arXiv:1910.08589, 2019	1	2019
Distributional and relational inductive biases for graph representation learning in biomedicine PM Scherer University of Cambridge, Computer Laboratory, 2024		2024
Spatio-relational inductive biases in spatial cell-type deconvolution R Viñas, P Scherer, N Simidjievski, M Jamnik, P Liò bioRxiv, 2023.05. 19.541474, 2023		2023
PyRelationAL: a python library for active learning research and development P Scherer, T Gaudelet, A Pouplin, A Del Vecchio, O Bolton, J Soman, ... arXiv preprint arXiv:2205.11117, 2022		2022
PyRelationAL: A Library for Active Learning Research and Development. P Scherer, T Gaudelet, A Pouplin, MS Suraj, J Soman, L Edwards, ... CoRR, 2022		2022
Incorporating network based protein complex discovery into automated model construction P Scherer, M Trȩbacz, N Simidjievski, Z Shams, H Andres Terre, P Liò, ... 15th Machine Learning in Computational Biology (MLCB'20), 2020		2020

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