Tom Heskes, Ph.D.
Affiliations: | Radboud University Nijmegen, Nijmegen, Gelderland, Netherlands |
Website:
http://www.cs.ru.nl/~tomh/Google:
"Tom Heskes"Mean distance: 16.04 (cluster 23) | S | N | B | C | P |
Children
Sign in to add trainee| Ali Bahramisharif | grad student | 2008-2012 | Radboud University Nijmegen |
| Marcel A. J. van Gerven | post-doc | 2008-2010 | Radboud University Nijmegen |
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Publications
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| Shapovalova Y, Heskes T, Dijkstra T. (2022) Non-parametric synergy modeling of chemical compounds with Gaussian processes. Bmc Bioinformatics. 23: 14 |
| Wijayanto F, Mul K, Groot P, et al. (2020) Semi-automated Rasch analysis using in-plus-out-of-questionnaire log likelihood. The British Journal of Mathematical and Statistical Psychology |
| Lederer S, Heskes T, van Heeringen SJ, et al. (2020) Investigating the effect of dependence between conditions with Bayesian Linear Mixed Models for motif activity analysis. Plos One. 15: e0231824 |
| Lederer S, Dijkstra TMH, Heskes T. (2019) Additive Dose Response Models: Defining Synergy. Frontiers in Pharmacology. 10: 1384 |
| Piray P, Dezfouli A, Heskes T, et al. (2019) Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies. Plos Computational Biology. 15: e1007043 |
| Bucur IG, Claassen T, Heskes T. (2019) Inferring the direction of a causal link and estimating its effect via a Bayesian Mendelian randomization approach. Statistical Methods in Medical Research. 962280219851817 |
| Bucur IG, Claassen T, Heskes T. (2019) Large-scale local causal inference of gene regulatory relationships International Journal of Approximate Reasoning. 115: 50-68 |
| Cui R, Bucur IG, Groot P, et al. (2019) A novel Bayesian approach for latent variable modeling from mixed data with missing values Statistics and Computing. 29: 977-993 |
| Lederer S, Dijkstra TMH, Heskes T. (2018) Additive Dose Response Models: Explicit Formulation and the Loewe Additivity Consistency Condition. Frontiers in Pharmacology. 9: 31 |
| Cui R, Groot P, Heskes T. (2018) Learning causal structure from mixed data with missing values using Gaussian copula models Statistics and Computing. 29: 311-333 |