| Guidance for RNA-seq co-expression network construction and analysis: safety in numbers S Ballouz, W Verleyen, J Gillis Bioinformatics 31 (13), 2123-2130, 2015 | 233 | 2015 |
| Measuring the wisdom of the crowds in network-based gene function inference W Verleyen, S Ballouz, J Gillis Bioinformatics 31 (5), 745-752, 2015 | 25 | 2015 |
| An analytical approach differentiates between individual and collective cancer invasion E Katz, W Verleyen, CG Blackmore, M Edward, VA Smith, DJ Harrison Analytical cellular pathology 34 (1-2), 35-48, 2011 | 14 | 2011 |
| Positive and negative forms of replicability in gene network analysis W Verleyen, S Ballouz, J Gillis Bioinformatics 32 (7), 1065-1073, 2016 | 11 | 2016 |
| Framework for disruptive AI/ML Innovation W Verleyen, W McGinnis arXiv preprint arXiv:2204.12641, 2022 | 1 | 2022 |
| Providence-a Deep Learning Framework for Time-to-Event Prediction S Fox, E Zimmerman, T Daly, M O'Keeffe, W Verleyen 2022 IEEE Aerospace Conference (AERO), 1-10, 2022 | | 2022 |
| SAPLING: A TOOL FOR CUSTOMIZED NETWORK ANALYSIS FOCUSING ON PSYCHIATRIC GENETICS W Verleyen, J Gillis European Neuropsychopharmacology 27, S352-S353, 2017 | | 2017 |
| Novel Monte Carlo approach quantifies data assemblage utility and reveals power of integrating molecular and clinical information for cancer prognosis W Verleyen, SP Langdon, D Faratian, DJ Harrison, VA Smith Scientific Reports 5 (1), 15563, 2015 | | 2015 |
| Machine learning for systems pathology W Verleyen University of St Andrews, 2013 | | 2013 |
