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2020 – today
- 2023
[j16]Dariush Salami
, Ramin Hasibi, Sameera Palipana, Petar Popovski, Tom Michoel, Stephan Sigg:
Tesla-Rapture: A Lightweight Gesture Recognition System From mmWave Radar Sparse Point Clouds. IEEE Trans. Mob. Comput. 22(8): 4946-4960 (2023)- 2022
- [i9]Muhammad Ammar Malik, Alexander Selvikvåg Lundervold, Tom Michoel:
rfPhen2Gen: A machine learning based association study of brain imaging phenotypes to genotypes. CoRR abs/2204.00067 (2022) - [i8]Tom Michoel, Jitao David Zhang:
Causal inference in drug discovery and development. CoRR abs/2209.14664 (2022) - 2021
- [j15]Ramin Hasibi, Tom Michoel:
A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks. BMC Bioinform. 22(3): 525 (2021) - [c5]Muhammad Ammar Malik, Adriaan-Alexander Ludl, Tom Michoel:
High-Dimensional Multi-trait GWAS By Reverse Prediction of Genotypes Using Machine Learning Methods. CIBB 2021: 79-93 - [i7]Dariush Salami, Ramin Hasibi, Sameera Palipana, Petar Popovski, Tom Michoel, Stephan Sigg:
Tesla-Rapture: A Lightweight Gesture Recognition System from mmWave Radar Point Clouds. CoRR abs/2109.06448 (2021) - [i6]Dariush Salami, Ramin Hasibi, Stefano Savazzi, Tom Michoel, Stephan Sigg:
Integrating Sensing and Communication in Cellular Networks via NR Sidelink. CoRR abs/2109.07253 (2021) - [i5]Muhammad Ammar Malik, Adriaan-Alexander Ludl, Tom Michoel:
High-dimensional multi-trait GWAS by reverse prediction of genotypes. CoRR abs/2111.00108 (2021) - 2020
- [j14]Pau Erola, Johan Björkegren, Tom Michoel:
Model-based clustering of multi-tissue gene expression data. Bioinform. 36(6): 1807-1813 (2020) - [i4]Muhammad Ammar Malik, Tom Michoel:
Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounders. CoRR abs/2005.02921 (2020) - [i3]Ramin Hasibi, Tom Michoel:
Predicting gene expression from network topology using graph neural networks. CoRR abs/2005.03961 (2020)
2010 – 2019
- 2018
- [c4]Tom Michoel:
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net. NeurIPS 2018: 2775-2785 - 2017
- [j13]Lingfei Wang, Tom Michoel:
Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data. PLoS Comput. Biol. 13(8) (2017) - [i2]Tom Michoel:
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net. CoRR abs/1709.08535 (2017) - 2016
- [j12]Tom Michoel:
Natural coordinate descent algorithm for L1-penalised regression in generalised linear models. Comput. Stat. Data Anal. 97: 60-70 (2016) - 2015
- [j11]Anagha Joshi, Yvonne Beck, Tom Michoel:
Multi-Species Network Inference Improves Gene Regulatory Network Reconstruction for Early Embryonic Development in Drosophila. J. Comput. Biol. 22(4): 253-265 (2015) - [j10]Eric Bonnet, Laurence Calzone, Tom Michoel:
Integrative Multi-omics Module Network Inference with Lemon-Tree. PLoS Comput. Biol. 11(2) (2015) - 2014
- [j9]Jianlong Qi, Hassan Asl, Johan Björkegren, Tom Michoel:
kruX: matrix-based non-parametric eQTL discovery. BMC Bioinform. 15: 11 (2014) - 2012
- [j8]Marleen Claeys, Valerie Storms, Hong Sun, Tom Michoel, Kathleen Marchal:
MotifSuite: workflow for probabilistic motif detection and assessment. Bioinform. 28(14): 1931-1932 (2012) - [j7]Jianlong Qi, Tom Michoel:
Context-specific transcriptional regulatory network inference from global gene expression maps using double two-way t-tests. Bioinform. 28(18): 2325-2332 (2012) - [i1]Tom Michoel, Bruno Nachtergaele:
Alignment and integration of complex networks by hypergraph-based spectral clustering. CoRR abs/1205.3630 (2012) - 2011
- [j6]Pieter Audenaert, Thomas Van Parys, Florian Brondel, Mario Pickavet, Piet Demeester, Yves Van de Peer, Tom Michoel:
CyClus3D: a Cytoscape plugin for clustering network motifs in integrated networks. Bioinform. 27(11): 1587-1588 (2011) - [c3]Jianlong Qi, Tom Michoel, Gregory Butler:
An integrative approach to infer regulation programs in a transcription regulatory module network. BCB 2011: 218-225 - [c2]Jianlong Qi, Tom Michoel, Gregory Butler:
Applying Linear Models to Learn Regulation Programs in a Transcription Regulatory Module Network. EvoBio 2011: 37-47 - 2010
- [j5]Eric Bonnet, Tom Michoel, Yves Van de Peer:
Prediction of a gene regulatory network linked to prostate cancer from gene expression, microRNA and clinical data. Bioinform. 26(18) (2010) - [c1]Jianlong Qi, Tom Michoel, Gregory Butler:
A regression tree-based Gibbs sampler to learn the regulation programs in a transcription regulatory module network. CIBCB 2010: 1-8
2000 – 2009
- 2009
- [j4]Anagha Joshi, Riet De Smet, Kathleen Marchal, Yves Van de Peer, Tom Michoel:
Module networks revisited: computational assessment and prioritization of model predictions. Bioinform. 25(4): 490-496 (2009) - [j3]Tom Michoel, Riet De Smet, Anagha Joshi, Yves Van de Peer, Kathleen Marchal:
Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks. BMC Syst. Biol. 3: 49 (2009) - 2008
- [j2]Anagha Joshi, Yves Van de Peer, Tom Michoel:
Analysis of a Gibbs sampler method for model-based clustering of gene expression data. Bioinform. 24(2): 176-183 (2008) - 2007
- [j1]Tom Michoel, Steven Maere, Eric Bonnet, Anagha Joshi, Yvan Saeys, Tim Van den Bulcke, Koenraad Van Leemput, Piet van Remortel, Martin Kuiper, Kathleen Marchal, Yves Van de Peer:
Validating module network learning algorithms using simulated data. BMC Bioinform. 8(S-2) (2007)
Coauthor Index
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