| An expanded evaluation of protein function prediction methods shows an improvement in accuracy Y Jiang, TR Oron, WT Clark, AR Bankapur, D D’Andrea, R Lepore, ... Genome biology 17, 1-19, 2016 | 428 | 2016 |
| The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens N Zhou, Y Jiang, TR Bergquist, AJ Lee, BZ Kacsoh, AW Crocker, ... Genome biology 20, 1-23, 2019 | 390 | 2019 |
| Ensemble methods M Re, G Valentini Advances in machine learning and data mining for astronomy, 563-593, 2012 | 151 | 2012 |
| Imbalance-aware machine learning for predicting rare and common disease-associated non-coding variants M Schubach, M Re, PN Robinson, G Valentini Scientific reports 7 (1), 2959, 2017 | 112 | 2017 |
| Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference N Cesa-Bianchi, M Re, G Valentini Machine Learning 88, 209-241, 2012 | 102 | 2012 |
| An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods G Valentini, A Paccanaro, H Caniza, AE Romero, M Re Artificial Intelligence in Medicine 61 (2), 63-78, 2014 | 69 | 2014 |
| A neural network algorithm for semi-supervised node label learning from unbalanced data M Frasca, A Bertoni, M Re, G Valentini Neural Networks 43, 84-98, 2013 | 66 | 2013 |
| A fast ranking algorithm for predicting gene functions in biomolecular networks M Re, M Mesiti, G Valentini IEEE/ACM Transactions on Computational Biology and Bioinformatics 9 (6 …, 2012 | 56 | 2012 |
| Network-based drug ranking and repositioning with respect to DrugBank therapeutic categories M Re, G Valentini IEEE/ACM Transactions on Computational Biology and Bioinformatics 10 (6 …, 2013 | 52 | 2013 |
| Ensembles in machine learning applications O Okun, G Valentini, M Re Springer Science & Business Media, 2011 | 51 | 2011 |
| Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction M Re, G Valentini Machine Learning in Systems Biology, 98-111, 2009 | 47 | 2009 |
| RANKS: a flexible tool for node label ranking and classification in biological networks G Valentini, G Armano, M Frasca, J Lin, M Mesiti, M Re Bioinformatics 32 (18), 2872-2874, 2016 | 43 | 2016 |
| Regeneration-associated WNT signaling is activated in long-term reconstituting AC133bright acute myeloid leukemia cells A Beghini, F Corlazzoli, L Del Giacco, M Re, F Lazzaroni, M Brioschi, ... Neoplasia 14 (12), 1236-IN45, 2012 | 34 | 2012 |
| Cancer module genes ranking using kernelized score functions M Re, G Valentini BMC bioinformatics 13, 1-16, 2012 | 34 | 2012 |
| Weighted True Path Rule: a multilabel hierarchical algorithm for gene function prediction G Valentini, M Re | 32 | 2009 |
| Integration of heterogeneous data sources for gene function prediction using decision templates and ensembles of learning machines M Re, G Valentini Neurocomputing 73 (7-9), 1533-1537, 2010 | 29 | 2010 |
| A hierarchical ensemble method for dag-structured taxonomies PN Robinson, M Frasca, S Köhler, M Notaro, M Re, G Valentini Multiple Classifier Systems: 12th International Workshop, MCS 2015, Günzburg …, 2015 | 20 | 2015 |
| Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction M Mesiti, M Re, G Valentini GigaScience 3 (1), 2047-217X-3-5, 2014 | 17 | 2014 |
| Prediction of human gene-phenotype associations by exploiting the hierarchical structure of the human phenotype ontology G Valentini, S Köhler, M Re, M Notaro, PN Robinson Bioinformatics and Biomedical Engineering: Third International Conference …, 2015 | 15 | 2015 |
| Random walking on functional interaction networks to rank genes involved in cancer M Re, G Valentini IFIP International Conference on Artificial Intelligence Applications and …, 2012 | 15 | 2012 |
