Author

Emmanuel Rivière

University of AntwerpORCID 0000-0003-3410-218XUnclaimed 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:drug response / drug discovery· 1 Approach:graph neural network· 1 Approach:machine learning (classical)· 1 Assay:protein sequence· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Annelies Van RieiD· 1 shared Danh Bui-ThiiD· 1 shared Kris LaukensiD· 1 shared M. FuertesiD· 1 shared Mahdi SafarpouriD· 1 shared Pieter MeysmaniD· 1 shared Vincent RennieiD· 1 shared Wilma Ursula Quispe-RojasiD· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 13 mutual co-author links

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1 shared paperBioinformatics

Annelies Van RieORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintFeb 14, 2026

Feature-based in-silico model to predict the Mycobacterium tuberculosis bedaquiline phenotype associated with Rv0678 variants

Wilma Ursula Quispe-Rojas, M. Fuertes, Vincent Rennie…·Annelies Van Rie

The paper develops a machine learning model to predict bedaquiline resistance in Mycobacterium tuberculosis based on Rv0678 variants, achieving high accuracy in internal validation.

DOI 10.64898/2026.02.12.705543

machine learning (classical)variant effect prediction

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BioinformaticsPLoS ONEPeer-reviewedJul 21, 2022

Predicting compound-protein interaction using hierarchical graph convolutional networks

Danh Bui-Thi, Emmanuel Rivière, Pieter Meysman, Kris Laukens

This paper introduces a hierarchical graph convolutional network for predicting compound-protein interactions using molecular structure and protein sequences, showing improved performance over existing methods.

6 citations

protein sequencegraph neural network

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