| Combining evidence from four immune cell types identifies DNA methylation patterns that implicate functionally distinct pathways during multiple sclerosis progression E Ewing, L Kular, SJ Fernandes, N Karathanasis, V Lagani, S Ruhrmann, ... EBioMedicine 43, 411-423, 2019 | 68 | 2019 |
| GluA2 mRNA distribution and regulation by miR-124 in hippocampal neurons VM Ho, LO Dallalzadeh, N Karathanasis, MF Keles, S Vangala, T Grogan, ... Molecular and Cellular Neuroscience 61, 1-12, 2014 | 58 | 2014 |
| Prediction of miRNA targets A Oulas, N Karathanasis, A Louloupi, GA Pavlopoulos, P Poirazi, ... RNA Bioinformatics, 207-229, 2015 | 50 | 2015 |
| STATegra: Multi-Omics data integration–a conceptual scheme with a bioinformatics pipeline N Planell, V Lagani, P Sebastian-Leon, F van der Kloet, E Ewing, ... Frontiers in genetics 12, 620453, 2021 | 40 | 2021 |
| MiRduplexSVM: a high-performing miRNA-duplex prediction and evaluation methodology N Karathanasis, I Tsamardinos, P Poirazi PloS one 10 (5), e0126151, 2015 | 32 | 2015 |
| A new microRNA target prediction tool identifies a novel interaction of a putative miRNA with CCND2 A Oulas, N Karathanasis, A Louloupi, I Iliopoulos, K Kalantidis, P Poirazi RNA biology 9 (9), 1196-1207, 2012 | 27 | 2012 |
| IsoMiRmap: fast, deterministic and exhaustive mining of isomiRs from short RNA-seq datasets P Loher, N Karathanasis, E Londin, P F. Bray, V Pliatsika, AG Telonis, ... Bioinformatics 37 (13), 1828-1838, 2021 | 23 | 2021 |
| Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data J Tanevski, T Nguyen, B Truong, N Karaiskos, ME Ahsen, X Zhang, C Shu, ... Life Science Alliance 3 (11), 2020 | 21 | 2020 |
| Non-parametric combination analysis of multiple data types enables detection of novel regulatory mechanisms in T cells of multiple sclerosis patients SJ Fernandes, H Morikawa, E Ewing, S Ruhrmann, RN Joshi, V Lagani, ... Scientific reports 9 (1), 11996, 2019 | 16 | 2019 |
| OmicsNPC: applying the non-parametric combination methodology to the integrative analysis of heterogeneous omics data N Karathanasis, I Tsamardinos, V Lagani PloS one 11 (11), e0165545, 2016 | 15 | 2016 |
| Reproducibility efforts as a teaching tool: A pilot study N Karathanasis, D Hwang, V Heng, R Abhimannyu, P Slogoff-Sevilla, ... PLOS Computational Biology 18 (11), e1010615, 2022 | 13 | 2022 |
| Finding cancer-associated miRNAs: methods and tools A Oulas, N Karathanasis, A Louloupi, P Poirazi Molecular biotechnology 49, 97-107, 2011 | 11 | 2011 |
| Computational identification of miRNAs involved in cancer A Oulas, N Karathanasis, P Poirazi MicroRNA and Cancer: Methods and Protocols, 23-41, 2011 | 7 | 2011 |
| Machine learning approaches identify genes containing spatial information from single-cell transcriptomics data P Loher, N Karathanasis Frontiers in Genetics 11, 612840, 2021 | 6 | 2021 |
| Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data J Tanevski, T Nguyen, B Truong, N Karaiskos, ME Ahsen, X Zhang, C Shu, ... bioRxiv, 796029, 2019 | 6 | 2019 |
| Don't use a cannon to kill the… miRNA mosquito N Karathanasis, I Tsamardinos, P Poirazi Bioinformatics 30 (7), 1047-1048, 2014 | 5 | 2014 |
| FAIR+ E pathogen data for surveillance and research: lessons from COVID-19 A Neves, I Cuesta, E Hjerde, T Klemetsen, D Salgado, J van Helden, ... Frontiers in Public Health 11, 1289945, 2023 | 2 | 2023 |
| SVM-based miRNA: MiRNA∗ duplex prediction K Nestoras, T Ioannis, PA Angelos, P Panayiota 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering …, 2012 | 2 | 2012 |
| Predicting the Progression from Asymptomatic to Symptomatic Multiple Myeloma and Stage Classification Using Gene Expression Data N Karathanasis, GM Spyrou Cancers 17 (2), 332, 2025 | | 2025 |
| Combining clinical and molecular data for personalized treatment in acute myeloid leukemia: A machine learning approach N Karathanasis, PL Papasavva, A Oulas, GM Spyrou Computer Methods and Programs in Biomedicine 257, 108432, 2024 | | 2024 |
