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Computer Science
Netherlands
2025

D-Index & Metrics

Computer Science

D-Index
59
Citations
14154
World Ranking
3417
National Ranking
36

Research.com Recognitions

  • 2025 - Research.com Computer Science in Netherlands Leader Award
  • 2022 - Research.com Computer Science in Netherlands Leader Award

Overview

Tom Heskes is affiliated with Radboud University in the Netherlands. Their research predominantly spans the field of computer science, with a focus on specialized subfields such as artificial intelligence, neurology, computer vision and pattern recognition, genetics, and molecular biology.

The main topics covered in Tom Heskes's work include:

  • Balance, Gait, and Falls Prevention
  • Adversarial Robustness in Machine Learning
  • Parkinson's Disease Mechanisms and Treatments
  • Genetic and phenotypic traits in livestock
  • Bayesian Modeling and Causal Inference
  • Machine Learning and Data Classification
  • Fault Detection and Control Systems

Frequent co-authors collaborating with Tom Heskes include:

  • Ioan Gabriel Bucur (14 joint publications)
  • Tom Claassen (10 joint publications)
  • Laurens Sluijterman (10 joint publications)
  • Eric Cator (10 joint publications)
  • Mariëlle Stoelinga (9 joint publications)

The preferred publication venues where Tom Heskes has contributed are:

  • arXiv (Cornell University) - 23 publications
  • Zenodo (CERN European Organization for Nuclear Research) - 4 publications
  • European Neuropsychopharmacology - 3 publications
  • bioRxiv (Cold Spring Harbor Laboratory) - 3 publications
  • Movement Disorders - 2 publications

Recent papers authored or co-authored by Tom Heskes highlight a blend of machine learning, causal inference, and biomedical applications:

  • Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models (2020), arXiv (Cornell University)
  • Understanding the assumptions underlying Mendelian randomization (2022), European Journal of Human Genetics
  • Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study (2020), Journal of Medical Internet Research
  • How to evaluate uncertainty estimates in machine learning for regression? (2024), Neural Networks

Tom Heskes's research integrates advanced computational techniques with clinical and biological aspects, focusing on both theoretical and practical challenges.

Best Publications

  • MAGMA: Generalized Gene-Set Analysis of GWAS Data

    Christiaan A. de Leeuw;Joris M. Mooij;Tom Heskes;Danielle Posthuma

  • Task clustering and gating for bayesian multitask learning

    Bart Bakker;Tom Heskes

  • Practical confidence and prediction intervals for prediction tasks

    T. Heskes;W.A.J.J. Wiegerinck;H.J. Kappen

  • The statistical properties of gene-set analysis

    Christiaan A. de Leeuw;Christiaan A. de Leeuw;Benjamin M. Neale;Benjamin M. Neale;Tom Heskes;Danielle Posthuma

  • Practical Confidence and Prediction Intervals

    Tom Heskes

  • Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities.

    Mohsen Ghafoorian;Nico Karssemeijer;Tom Heskes;Inge W. M. van Uden

  • Energy functions for self-organizing maps

    Tom Heskes

  • Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy

    Tom Heskes

  • Self-organizing maps, vector quantization, and mixture modeling

    T. Heskes

  • On the uniqueness of loopy belief propagation fixed points

    Tom Heskes

  • Learning processes in neural networks

    Tom M. Heskes;Bert Kappen

  • Clustering ensembles of neural network models

    Bart Bakker;Tom Heskes

  • Exact p -values for pairwise comparison of Friedman rank sums, with application to comparing classifiers

    Rob Eisinga;Tom Heskes;Ben Pelzer;Manfred Te Grotenhuis

  • Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin

    Mohsen Ghafoorian;Nico Karssemeijer;Tom Heskes;Mayra Bergkamp

  • A Causal and Mediation Analysis of the Comorbidity Between Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD)

    Elena Sokolova;Anoek M. Oerlemans;Anoek M. Oerlemans;Nanda N. Rommelse;Perry Groot

  • Linear reconstruction of perceived images from human brain activity

    Sanne Schoenmakers;Markus Barth;Tom Heskes;Marcel van Gerven

  • Expectation propagation for approximate inference in dynamic bayesian networks

    Tom Heskes;Onno Zoeter

  • RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets.

    Francesco Del Carratore;Andris Jankevics;Rob Eisinga;Tom Heskes

  • Premise Selection for Mathematics by Corpus Analysis and Kernel Methods

    Jesse Alama;Tom Heskes;Daniel Kühlwein;Evgeni Tsivtsivadze

  • Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies.

    Payam Piray;Amir Dezfouli;Tom Heskes;Michael J. Frank

  • Empirical Bayes for Learning to Learn

    Tom Heskes

  • Expectation Propogation for approximate inference in dynamic Bayesian networks

    Tom Heskes;Onno Zoeter

  • Expectation propagation for approximate inference m dynamic Bayesian networks

    Tom Heskes;Onno Zoeter

Frequent Co-Authors

Marcel A. J. van Gerven
Marcel A. J. van Gerven Radboud University
Hilbert J. Kappen
Hilbert J. Kappen Radboud University
Jan K. Buitelaar
Jan K. Buitelaar Radboud University
Elena Marchiori
Elena Marchiori Radboud University
Barbara Franke
Barbara Franke Radboud University
Danielle Posthuma
Danielle Posthuma Vrije Universiteit Amsterdam
Hans Knoop
Hans Knoop Amsterdam University Medical Centers
Nico Karssemeijer
Nico Karssemeijer Radboud University
Josef Urban
Josef Urban Czech Technical University in Prague
Lodewyk F. A. Wessels
Lodewyk F. A. Wessels Antoni van Leeuwenhoek Hospital

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