Author

Paul C. Scherer

University of CambridgeORCID 0000-0002-2240-7501Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:batch correction / integration· 1 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:deconvolution· 1 Research Focus:differential expression· 1 Approach:graph neural network· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Mateja JamnikiD· 3 shared Nikola SimidjievskiiD· 3 shared Píetro LióiD· 3 shared Helena Andrés-TerréiD· 2 shared Zohreh ShamsiD· 2 shared Botty Dimanov· 1 shared Cristian BodnariD· 1 shared Ifrah TariqiD· 1 shared

Activity by Year

2019-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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3 shared papersBioinformatics

Mateja JamnikORCID

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3 papers

Recent·Most cited

Spatio-relational inductive biases in spatial cell-type deconvolution

Ramón Viñas, Paul C. Scherer, Nikola Simidjievski, Mateja Jamnik, Píetro Lió

This paper introduces GNN-C2L, a spatial cell-type deconvolution method that improves performance by incorporating spatial inductive biases, outperforming spatial-agnostic variants on simulated datasets.

bioRxiv · May 19, 2023 · PreprintBioinformatics

REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare

Zohreh Shams, Botty Dimanov, Sumaiyah Kola, Nikola Simidjievski, Helena Andrés-Terré, Paul C. Scherer, Urška Matjašec, Jean Abraham, Mateja Jamnik, Píetro Lió

REM extracts interpretable rules from deep learning and non-deep learning models to enhance explainability in healthcare decision-making.

bioRxiv · Jan 22, 2021 · PreprintBioinformatics

Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice

Nikola Simidjievski, Cristian Bodnar, Ifrah Tariq, Paul C. Scherer, Helena Andrés-Terré, Zohreh Shams, Mateja Jamnik, Píetro Lió

This paper explores autoencoder architectures for integrating cancer patient data and demonstrates their effectiveness in analyzing heterogeneous breast cancer data for accurate diagnosis.

Frontiers in Genetics · Dec 11, 2019Peer-reviewedBioinformatics