| Advanced methods and tools for ECG data analysis GD Clifford, F Azuaje Artech house, 2006 | 1213 | 2006 |
| Multiple SVM-RFE for gene selection in cancer classification with expression data KB Duan, JC Rajapakse, H Wang, F Azuaje IEEE transactions on nanobioscience 4 (3), 228-234, 2005 | 536 | 2005 |
| Cluster validation techniques for genome expression data N Bolshakova, F Azuaje Signal processing 83 (4), 825-833, 2003 | 517 | 2003 |
| Stem cell-associated heterogeneity in Glioblastoma results from intrinsic tumor plasticity shaped by the microenvironment A Dirkse, A Golebiewska, T Buder, PV Nazarov, A Muller, S Poovathingal, ... Nature communications 10 (1), 1787, 2019 | 477 | 2019 |
| Use of circulating microRNAs to diagnose acute myocardial infarction Y Devaux, M Vausort, E Goretti, PV Nazarov, F Azuaje, G Gilson, ... Clinical chemistry 58 (3), 559-567, 2012 | 337 | 2012 |
| ECG statistics, noise, artifacts, and missing data GD Clifford, F Azuaje, P Mcsharry Advanced methods and tools for ECG data analysis 6 (1), 18, 2006 | 253 | 2006 |
| Single‐cell transcriptomics reveals distinct inflammation‐induced microglia signatures C Sousa, A Golebiewska, SK Poovathingal, T Kaoma, Y Pires‐Afonso, ... EMBO reports 19 (11), e46171, 2018 | 251 | 2018 |
| An assessment of recently published gene expression data analyses: reporting experimental design and statistical factors P Jafari, F Azuaje BMC Medical Informatics and Decision Making 6, 1-8, 2006 | 217 | 2006 |
| Gene expression correlation and gene ontology-based similarity: an assessment of quantitative relationships H Wang, F Azuaje, O Bodenreider, J Dopazo 2004 Symposium on Computational Intelligence in Bioinformatics and …, 2004 | 199 | 2004 |
| Computational models for predicting drug responses in cancer research F Azuaje Briefings in bioinformatics 18 (5), 820-829, 2017 | 194 | 2017 |
| Solutions to instability problems with sequential wrapper-based approaches to feature selection K Dunne, P Cunningham, F Azuaje Journal of Machine Learning Research 1, 22, 2002 | 192 | 2002 |
| Ontology-driven similarity approaches to supporting gene functional assessment F Azuaje, H Wang, O Bodenreider Proceedings of the ISMB’2005 SIG meeting on Bio-ontologies 2005, 9-10, 2005 | 136 | 2005 |
| Artificial intelligence for precision oncology: beyond patient stratification F Azuaje NPJ precision oncology 3 (1), 6, 2019 | 135 | 2019 |
| A cluster validity framework for genome expression data F Azuaje Bioinformatics 18 (2), 319-320, 2002 | 132 | 2002 |
| Permutation–based statistical tests for multiple hypotheses A Camargo, F Azuaje, H Wang, H Zheng Source code for biology and medicine 3, 1-8, 2008 | 126 | 2008 |
| Databases for lncRNAs: a comparative evaluation of emerging tools S Fritah, SP Niclou, F Azuaje Rna 20 (11), 1655-1665, 2014 | 106 | 2014 |
| Patient-derived organoids and orthotopic xenografts of primary and recurrent gliomas represent relevant patient avatars for precision oncology A Golebiewska, AC Hau, A Oudin, D Stieber, YA Yabo, V Baus, ... Acta Neuropathologica 140, 919-949, 2020 | 99 | 2020 |
| Molecular crosstalk between tumour and brain parenchyma instructs histopathological features in glioblastoma S Bougnaud, A Golebiewska, A Oudin, O Keunen, PN Harter, L Mäder, ... Oncotarget 7 (22), 31955, 2016 | 95 | 2016 |
| Witten IH, Frank E: Data Mining: Practical Machine Learning Tools and Techniques 2nd edition: San Francisco: Morgan Kaufmann Publishers; 2005: 560. ISBN 0-12-088407-0,£ 34.99 F Azuaje BioMedical Engineering OnLine 5, 1-2, 2006 | 94 | 2006 |
| A knowledge-driven approach to cluster validity assessment N Bolshakova, F Azuaje, P Cunningham Bioinformatics 21 (10), 2546-2547, 2005 | 87 | 2005 |
