Publications

Journals

  1. F Torgano, M Soto-Gomez, M Zignani, J Gliozzo, E Cavalleri, M Mesiti, E Casiraghi, G Valentini, RNA Knowledge-Graph analysis through homogeneous embedding methods, Bioinformatics Advances, vol.5, issue 1, 2025
  2. E Niyonkuru, J Caufield, L Carmody, M Gargano, S Toro, P Whetzel, H Blau, M Soto Gomez, E Casiraghi, L Chimirri, J Reese, G Valentini, M Haendel, C Mungall, P Robinson, Leveraging generative AI to assist biocuration of medical actions for rare disease, Bioinformatics Advances, vol.5, issue 1, vbaf141, 2025
  3. J Gliozzo, M Soto-Gomez, A Bonometti, A Patak, E Casiraghi, G Valentini, miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources, Bioinformatics, Volume 41, Issue 4, April 2025
  4. M Nicolini, E Saitto, RE Jimenez Franco, E Cavalleri, AJ Galeano, D Malchiodi, A Paccanaro, PN Robinson, E Casiraghi and G Valentini, Fine-tuning of conditional Transformers improves in silico enzyme prediction and generation, Computational and Structural Biotechnology Journal, vol. 27, pp. 1318-1334, 2025
  5. E Niyonkuru, M Soto-Gomez, E Casiraghi, S Antogiovanni, H Blau, J Reese, G Valentini and PN Robinson, Replacing non-biomedical concepts improves embedding of biomedical concepts, PLoS One 20(5): e0322498, 2025
  6. J Gliozzo, M Soto-Gomez, V Guarino, A Bonometti, A Cabri, E Cavalleri, J Reese, PN Robinson, M Mesiti, G Valentini, E Casiraghi, Intrinsic-Dimension Analysis for Guiding Dimensionality Reduction and Data Fusion in Multi-Omics Data Processing, Artificial Intelligence in Medicine, vol. 160, 2025
  7. E Cavalleri, A Cabri, M Soto-Gomez, S Bonfitto, P Perlasca, J Gliozzo, TJ Callahan, J Reese, PN Robinson, E Casiraghi, G Valentini, M Mesiti, An ontology-based knowledge graph for representing interactions involving RNA molecules, Scientific Data, Nature Publishing, 11, 906, 2024
  8. B Coleman, E Casiraghi, TJ Callahan, H Blau, L Chan, B Laraway, K Clark, Y Re’em, K Gersing, KJ Wilkins, N Harris, G Valentini, M Haendel, J Reese, PN Robinson, Association of post-COVID phenotypic manifestations with new-onset psychiatric disease, Transl Psychiatry, Nature Publishing, 14, 246, 2024
  9. E. Esenkova, T. Koeck, G. Valentini, P. Wild, E. Casiraghi, E. Araldi, Unbiased clustering and molecular characterisation of novel metabolic phenotypes in a heart failure cohort, Cardiovascular Research, 120:Suppl.1, 2024
  10. TJ Callahan, IJ Tripodi, AL Stefanski, L Cappelletti, SB Taneja, JM Wyrwa, E Casiraghi, NA Matentzoglu, J Reese, JC Silverstein, CT Hoyt, RD Boyce, SA Malec, DR Unni, MP Joachimiak, PN Robinson, CJ Mungall, E Cavalleri, T Fontana, G Valentini, M Mesiti, LA Gillenwater, B Santangelo, NA Vasilevsky, R Hoehndorf, TD Bennett, PB Ryan, G Hripcsak, MG Kahn, M Bada, WA Baumgartner Jr, An open source knowledge graph ecosystem for the life sciences, Scientific Data, Nature Publishing, 11, 363, 2024
  11. L Chan, E Casiraghi, J Reese, Q Harmon, K Schaper, H Hegde, G Valentini, C Schmitt, A Motsinger-Reif, J Hall, C Mungall, P Robinson, M Haendel, Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests, International Journal of Medical Informatics, 187, 105461, 2024
  12. L Cappelletti, L Rekerle, To Fontana, Pr Hansen, E Casiraghi, V Ravanmehr, CJ Mungall, JJ Yang, L Spranger, G Karlebach, JH Caufield, L Carmody, B Coleman, TI Oprea, J Reese, G Valentini, PN Robinson, Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning, Bioinformatics Advances, Volume 4, Issue 1, Oxford University Press, 2024
  13. G. Valentini, Exploring the similarity between genetic diseases improves their differential diagnosis and the understanding of their etiology, Eur J Hum Genet, 2024
  14. G. Valentini, D. Malchiodi, J. Gliozzo, M. Mesiti, M. Soto-Gomez, A. Cabri, J. Reese, E. Casiraghi, P. Robinson, The promises of large language models for protein design and modeling, Frontiers in Bioinformatics, vol. 3, 2023
  15. B. Antony, H. Blau, E. Casiraghi, J. Loomba, T. Callahan, B. Laraway, K. Wilkins, C. Antonescu, G. Valentini, A. Williams, P. Robinson, J. Reese, T. Murali, Predictive models of long COVID, eBioMedicine - The Lancet Discovery Science, vol. 96, 104777, 2023
  16. L. Cappelletti, T. Fontana, E. Casiraghi, V. Ravanmehr, T. J.Callahan, C. Cano, M. P. Joachimiak, C. J. Mungall, P. N. Robinson, J. Reese, G. Valentini, GRAPE for Fast and Scalable Graph Processing and Random Walk-based Embedding, Nature Computational Science 3, 552–568, 2023
  17. E. Casiraghi and G. Valentini, A software resource for large graph processing and analysis, Research briefing - Nature Computational Science 3, 2023
  18. G. Karlebach, L. Carmody, J. C. Sundaramurthi, E. Casiraghi, P. Hansen, J. Reese, C. J. Mungall, G. Valentini, P. N. Robinson, An expectation-maximization framework for comprehensive prediction of isoform-specific functions, Bioinformatics, Oxford University Press, 39(4), btad1322023, April 2023
  19. E. Casiraghi, R. Wong, M. Hall, B. Coleman, M. Notaro, M. D. Evans, J. S. Tronieri, H. Blau, B. Laraway, T. J. Callahan, L. E. Chan, C. T. Bramante, J. B. Buse, R. A. Moffitt, T. Stürmer, S. G. Johnson, Y. R. Shao, J. Reese, P. N. Robinson, A. Paccanaro, G. Valentini, J. D. Huling, K. J. Wilkins, A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative, Journal of Biomedical Informatics, 139:104295, 2023
  20. L. Ferrè, F. Clarelli, B. Pignolet, E. Mascia, M. Frasca, S. Santoro, M. Sorosina, F. Bucciarelli, L. Moiola, V. Martinelli, G. Comi, R. Liblau, M. Filippi, G. Valentini, F. Esposito, Combining Clinical and Genetic Data to Predict Response to Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients: A Precision Medicine Approach, Journal of Personalized Medicine, 13(1):122, 2023
  21. J. T. Reese, H. Blau, T, Bergquist, J. J. Loomba, T. Callahan, B. Laraway, C. Antonescu, E. Casiraghi, B, Coleman, M. Gargano, K. J. Wilkins, L. Cappelletti, T. Fontana, N. Ammar, B. Antony, T. M. Murali, G. Karlebach, J. A McMurry, A. Williams, R. Moffitt, J, Banerjee, A. E. Solomonides, H. Davis, K. Kostka, G. Valentini, D, Sahner, C, G. Chute, C, Madlock-Brown, M. A Haendel, P. N. Robinson on behalf of the N3C Consortium, Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs, eBioMedicine - The Lancet Discovery Science, Vol. 87, 2023
  22. L. Cappelletti, A. Petrini, J. Gliozzo, E. Casiraghi, M. Schubach, M. Kircher and G. Valentini, Boosting tissue-specific prediction of active cis-regulatory regions through deep learning and Bayesian optimization techniques, BMC Bioinformatics, 23:154, 2022
  23. L.E. Chan, E. Casiraghi, B. Laraway, B. Coleman, H. Blau, A. Zaman, N. L. Harris, K. Wilkins, B. Antony, M. Gargano, G. Valentini, D. Sahner, M. Haendel, P. N. Robinson, C. Bramante, J. Reese, Metformin is Associated with Reduced COVID-19 Severity in Patients with Prediabetes, Diabetes Research and Clinical Practice, Volume 194, 1101572022, 2022
  24. J. Gliozzo, M. Mesiti, M. Notaro, A. Petrini, A. Patak, A. Puertas-Gallardo, A. Paccanaro, G. Valentini and E. Casiraghi, Heterogeneous data integration methods for patient similarity networks, Briefings in Bioinformatics, Oxford University Press, Jul 18;23(4):bbac207, 2022
  25. J.T. Reese, B. Coleman, L. Chan, H. Blau, T.J. Callahan, L. Cappelletti, T. Fontana, K.R. Bradwell, N.L. Harris, E. Casiraghi, G. Valentini, G. Karlebach, R. Deer, J.A. McMurry, M.A. Haendel, C.G. Chute, E. Pfaff, R. Moffitt, H. Spratt, J.A. Singh, C.J. Mungall, A.E. Williams, P.N. Robinson, NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study, Virology Journal, 19:1, 2022
  26. G. Paolillo, A. Petrini, E. Casiraghi, M. De Iorio, S. Biffani, G. Pagnacco, G. Minozzi and G. Valentini, Automated image analysis to assess hygienic behaviour of honeybees, PLOS ONE, 17(1): e0263183, 2022
  27. V. Ravanmehr, H. Blau, L. Cappelletti, T. Fontana, L. Carmody, B. Coleman, J. George, J. Reese, M. Joachimiak, G. Bocci, P. Hansen, C. Bult, J. Rueter, E. Casiraghi, G. Valentini, C. Mungall, T, Oprea and P. Robinson, Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer, NAR Genomics and Bioinformatics, Oxford Academic, 3(4), 2021
  28. M. Notaro, M. Frasca, A. Petrini, J. Gliozzo, E. Casiraghi, P.N. Robinson and G. Valentini, HEMDAG: a family of modular and scalable hierarchical ensemble methods to improve Gene Ontology term prediction, Bioinformatics, Oxford Academic, 37(23), 2021
  29. D. Danis, J.O.B. Jacobsen, L. Carmody, M.A. Gargano, J.A. McMurry, A. Hegde, M.A. Haendel, G. Valentini, D. Smedley, P.N. Robinson, Interpretable prioritization of splice variants in diagnostic next-generation sequencing, The American Journal of Human Genetics, Cell Press, 108(9), pages 1564-1577, 2021
  30. A Scarabelli, M Zilocchi, E Casiraghi, P Fasani, G Plensich, A Esposito, E Stellato, A Petrini, J Reese, P Robinson, G Valentini, G Carrafiello, Abdominal Computed Tomography Imaging Findings in Hospitalized COVID-19 Patients: A Year-Long Experience and Associations Revealed by Explainable Artificial Intelligence, Journal of Imaging 7(12):258, 2021
  31. A Esposito, E Casiraghi, F Chiaraviglio, A Scarabelli, E Stellato, G Plensich, G Lastella, L Di Meglio, S Fusco, E Avola, A Jachetti, C Giannitto, D Malchiodi, M Frasca, A Beheshti, PN Robinson, G Valentini, L Forzenigo, G Carrafiello, Artificial Intelligence in Predicting Clinical Outcome in COVID-19 Patients from Clinical, Biochemical and a Qualitative Chest X-Ray Scoring System, Reports in Medical Imaging 14:27-39, 2021
  32. A. Cassaro, G. Grillo, M. Notaro, J. Gliozzo, I. Esposito, G. Reda, A. Trojani, G. Valentini, B. Di Camillo, R. Cairoli, A. Beghini, FZD6 triggers Wnt-signalling driven by WNT10BIVS1 expression and highlights new targets in T cell acute lymphoblastic leukemia, Hematol Oncol, Jan 26, 2021
  33. P. Perlasca, M. Frasca, C.T. Ba, J. Gliozzo, M. Notaro, M. Pennacchioni, G. Valentini, M. Mesiti, Multi-resolution visualization and analysis of biomolecular networks through hierarchical community detection and web-based graphical tools, PLoS ONE 15(12): e0244241, 2020
  34. E. Casiraghi, D. Malchiodi, G. Trucco, M. Frasca, L. Cappelletti, T. Fontana, A. Esposito, E. Avola, A. Jachetti, J. Reese, A. Rizzi, P. Robinson, G.Valentini, Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments, IEEE Access vol. 8, pp. 196299-196325, 2020
  35. A. Petrini, M, Mesiti, M. Schubach, M. Frasca, D. Danis, M. Re, G. Grossi, L. Cappelletti, T. Castrignano’, P. Robinson, G. Valentini, parSMURF, a High Performance Computing tool for the genome-wide detection of pathogenic variants, GigaScience, Oxford Academic 9(5), 2020
  36. L. Cappelletti, T. Fontana, G. Di Donato, L Di Tucci, E. Casiraghi and G. Valentini, Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks: A Case Study on Genome Gap-Filling, Computers 9(2):37, 2020
  37. S. Vascon, M. Frasca, R. Tripodi, G. Valentini, M. Pelillo, Protein Function Prediction as a Graph-Transduction Game, Pattern Recognition Letters vo. 134 pp. 96-105, 2020
  38. J. Gliozzo, P. Perlasca, M. Mesiti, E. Casiraghi, V. Vallacchi, E. Vergani, M. Frasca, G. Grossi, A. Petrini, M. Re, A. Paccanaro and G. Valentini, Network modeling of patients’ biomolecular profiles for clinical phenotype/outcome prediction, Scientific Reports, Nature Publishing 10:3612, 2020
  39. N. Zhou, Y. Jiang, T. Bergquist, …, G. Valentini, … P. Radivojac, I. Friedberg, The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens, Genome Biology 20, article number: 244, 2019
  40. P. Perlasca, M. Frasca, Cheick Tidiane Ba, M. Notaro, A. Petrini, E. Casiraghi, G. Grossi, J. Gliozzo, G. Valentini and M. Mesiti, UNIPred-Web: a Web Tool for the Integration and Visualization of Biomolecular Networks for Protein Function Prediction, BMC Bioinformatics 20:422, 2019
  41. L.Cappelletti, J. Gliozzo, A. Petrini, and G. Valentini, Training Neural Networks with Balanced Mini-batch to Improve the Prediction of Pathogenic Genomic Variants in Mendelian Diseases, Special issue “Artificial Intelligence & Neural Networks”, Sensors & Transducers 234:6, pp. 16-21, 2019
  42. M. Frasca, G. Grossi, J. Gliozzo, M. Mesiti, M. Notaro, P. Perlasca, A. Petrini and G. Valentini, A GPU-based algorithm for fast node label learning in large and unbalanced biomolecular networks, BMC Bioinformatics 19:Suppl 10 Oct. 15, 2018
  43. M. Notaro, M. Schubach, P.N. Robinson, G. Valentini, Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods, BMC Bioinformatics, vol. 18 (1), 2017
  44. M. Schubach, M. Re, P.N. Robinson and G. Valentini, Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants, Scientific Reports, Nature Publishing, 7:2959, 2017
  45. D. Smedley, M, Schubach, J. Jacobsen, S. Kohler, T. Zemojtel, M. Spielmann, M. Jager, H. Hochheiser, N. Washington, J. McMurry, M. Haendel, C. Mungall, S. Lewis, T. Groza, G. Valentini and P.N. Robinson, A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease, The American Journal of Human Genetics, 99:3, pp.595-606, September 2016
  46. Y. Jiang, P. Oron, … G. Valentini, … I. Friedberg and P. Radivojac, An expanded evaluation of protein function prediction methods shows an improvement in accuracy, Genome Biology, 17:184 September 2016
  47. G. Valentini, G. Armano, M. Frasca, J. Lin, M. Mesiti and M. Re, RANKS: a flexible tool for node label ranking and classification in biological networks, Bioinformatics, 32(18), September 2016
  48. M. Frasca, S.Bassis, G. Valentini, Learning node labels with multi-category Hopfield networks, Neural Computing and Applications, 27(6), pp 1677-1692, 2016
  49. M. Frasca, G. Valentini, COSNet: an R package for label prediction in unbalanced biological networks, Neurocompting, 2016
  50. M. Frasca, A. Bertoni, G. Valentini, UNIPred: Unbalance-aware Network Integration and Prediction of protein functions, Journal of Computational Biology, 22(12): 1057-1074, 2015
  51. M. Mesiti, M. Re, G. Valentini, Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction, GigaScience, 3:5, 2014
  52. G. Valentini, A. Paccanaro, H. Caniza, A. Romero, M. Re, An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods, Artificial Intelligence in Medicine, Volume 61, Issue 2, pages 63-78, June 2014
  53. H. Caniza, A. Romero, S. Heron, H. Yang, A. Devoto, M. Frasca, M. Mesiti, G. Valentini, A. Paccanaro, GOssTo: a user-friendly stand-alone and web tool for calculating semantic similarities on the Gene Ontology, Bioinformatics, Vol. 30 no. 15, pages 2235-2236, 2014
  54. G. Valentini, Hierarchical Ensemble Methods for Protein Function Prediction, ISRN Bioinformatics, vol. 2014, Article ID 901419, 34 pages, 2014
  55. M. Re, and G. Valentini, Network-based Drug Ranking and Repositioning with respect to DrugBank Therapeutic Categories, IEEE ACM Transactions on Computational Biology and Bioinformatics 10(6), pp. 1359-1371, Nov-Dec 2013
  56. I. Cattinelli, G. Valentini, E. Paulesu, A. Borghese, A Novel Approach to the Problem of Non-uniqueness of the Solution in Hierarchical Clustering, IEEE Transactions on Neural Networks and Learning Systems 24(7) pp.1166-1173, July 2013
  57. M. Frasca, A. Bertoni, M. Re, and G. Valentini, A neural network algorithm for semi-supervised node label learning from unbalanced data, Neural Networks 43, pp.84-98, July 2013
  58. M. Re, M. Mesiti and G. Valentini, A Fast Ranking Algorithm for Predicting Gene Functions in Biomolecular Networks, IEEE ACM Transactions on Computational Biology and Bioinformatics 9(6) pp. 1812-1818, 2012
  59. A. Beghini, F. Corlazzoli, L. Del Giacco, M. Re, F. Lazzaroni, M. Brioschi, G. Valentini, F. Ferrazzi, A. Ghilardi, M. Righi, M. Turrini, M. Mignardi, C. Cesana, V. Bronte, M. Nilsson, E. Morra and R. Cairoli, Regeneration-associated Wnt signaling is activated in long-term reconstituting AC133bright acute myeloid leukemia cells, Neoplasia 14:12, pp. 1236-1248, 2012
  60. M. Re and G. Valentini, Cancer module genes ranking using kernelized score functions, BMC Bioinformatics 13 (Suppl 14): S3, 2012
  61. N. Cesa-Bianchi, M. Re, G. Valentini, Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference, Machine Learning, vol.88(1), pp. 209-241, 2012
  62. M. Re, M. Mesiti, G. Valentini, Drug repositioning through pharmacological spaces integration based on networks projection, EMBnet.journal, vol 18, Supplement A, pp.30-31, BITS 2012, Bioinformatics Italian Society Meeting, Catania, Italy, 2012
  63. M. Frasca, A. Bertoni, G. Valentini, Regularized Network-Based Algorithm for Predicting Gene Functions with High-Imbalanced Data, EMBnet.journal, vol 18, Supplement A, pp.41,42, BITS 2012, Bioinformatics Italian Society Meeting, Catania, Italy, 2012
  64. G. Valentini, True Path Rule hierarchical ensembles for genome-wide gene function prediction, IEEE ACM Transactions on Computational Biology and Bioinformatics, vol.8 n.3 pp. 832-847, 2011
  65. M. Muselli, A. Bertoni, M. Frasca, A. Beghini, F. Ruffino, and G. Valentini, A mathematical model for the validation of gene selection methods, IEEE ACM Transactions on Computational Biology and Bioinformatics, vol.8 n.5 pp. 1385-1392, 2011
  66. M. Re, G. Valentini, Noise tolerance of Multiple Classifier Systems in data integration-based gene function prediction, Journal of Integrative Bioinformatics, 7(3):139, 2010
  67. M. Re, G. Valentini, Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction, Journal of Machine Learning Research, W&C Proceedings, vol.8: Machine Learning in Systems Biology, pp. 98-111, 2010
  68. N. Cesa-Bianchi, G. Valentini, Hierarchical cost-sensitive algorithms for genome-wide gene function prediction, Journal of Machine Learning Research, W&C Proceedings, vol.8: Machine Learning in Systems Biology, pp.14-29, 2010
  69. M. Re, G. Valentini, Integration of heterogeneous data sources for gene function prediction using Decision Templates and ensembles of learning machines, Neurocomputing, 73:7-9 pp. 1533-37, 2010
  70. M. Mesiti, E. Jimenez-Ruiz, I. Sanz, R. Berlanga-Llavori, P. Perlasca, G. Valentini and D. Manset, XML-Based Approaches for the Integration of Heterogeneous Bio-Molecular Data, BMC Bioinformatics 10:(S12)S7, 2009
  71. R. Avogadri, M. Brioschi, F. Ferrazzi, M. Re, A. Beghini, and G. Valentini, A stability-based algorithm to validate hierarchical clusters of genes, International Journal of Knowledge Engineering and Soft Data Paradigms, 1(4), pp. 318-330, 2009
  72. G.Valentini, R.Tagliaferri, F.Masulli, Computational Intelligence and Machine Learning in Bioinformatics, Artificial Intelligence in Medicine 45(2), pp. 91-96, 2009
  73. R. Avogadri, G.Valentini, Fuzzy ensemble clustering based on random projections for DNA microarray data analysis, Artificial Intelligence in Medicine 45(2), pp. 173-183, 2009
  74. G.Pavesi, G.Valentini, Classification of co-expressed genes from DNA regulatory regions, Information Fusion 10(3), pp. 233-241, 2009
  75. A. Bertoni, G.Valentini, Discovering multi-level structures in bio-molecular data through the Bernstein inequality, BMC Bioinformatics 9(Suppl 2):S4, 2008
  76. G.Valentini, N. Cesa-Bianchi, HCGene: a software tool to support the hierarchical classification of genes, Bioinformatics, 24(5), pp. 729-731, 2008
  77. F. Ruffino, M. Muselli, G.Valentini, Gene expression modelling through positive Boolean functions, International Journal of Approximate Reasoning, 47(1), pp. 97-108, 2008
  78. A.Bertoni, G.Valentini, Model order selection for biomolecular data clustering, BMC Bioinformatics, vol.8, Suppl.3, 2007
  79. G.Valentini, Mosclust: a software library for discovering significant structures in bio-molecular data, Bioinformatics 23(3):387-389, 2007
  80. G. Valentini, F.Ruffino, Characterization of Lung tumor subtypes through gene expression cluster validity assessment, RAIRO - Theoretical Informatics and Applications, 40:163-176, 2006
  81. A.Bertoni, G. Valentini, Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses, Artificial Intelligence in Medicine 37(2):85-109, 2006
  82. G.Valentini, Clusterv: a tool for assessing the reliability of clusters discovered in DNA microarray data, Bioinformatics 22(3):369-370, 2006
  83. G.Valentini, An experimental bias-variance analysis of SVM ensembles based on resampling techniques, IEEE Transactions on Systems, Man and Cybernetics, Part B 35(6) pp. 1252-1271, 2005
  84. P. Campadelli, E. Casiraghi, G.Valentini, Support Vector Machines for candidate nodules classification, Neurocomputing 68 pp. 281-289, 2005
  85. A. Bertoni, R. Folgieri, G. Valentini, Bio-molecular cancer prediction with random subspace ensembles of Support Vector Machines, Neurocomputing 63C pp. 535-539, 2005
  86. G. Valentini, T. G. Dietterich, Bias-variance analysis of Support Vector Machines for the development of SVM-based ensemble methods, Journal of Machine Learning Research, 5(Jul) pp. 725-775, 2004
  87. F. Masulli, G. Valentini, An experimental analysis of the dependence among codeword bit errors in ECOC learning machines, Neurocomputing 57 pp. 189-214, 2004
  88. G. Valentini, M. Muselli and F. Ruffino, Cancer recognition with bagged ensembles of Support Vector Machines, Neurocomputing 56 pp. 461-466, 2004
  89. F. Masulli, G. Valentini, Effectiveness of output coding decomposition schemes in ensemble and monolithic learning machines, Pattern Analysis and Applications 6 pp. 285-300, 2003
  90. G.Valentini, Gene expression data analysis of human lymphoma using Support Vector Machines and Output Coding ensembles, Artificial Intelligence in Medicine 26(3) pp 283-306, 2002
  91. G. Valentini, F. Masulli, NEURObjects: an object-oriented library for neural network development, Neurocomputing 48(1-4) pp. 623-646, 2002
  92. M. Pardo, G. Sberveglieri, A.Taroni, F. Masulli, G. Valentini, Decompositive classification models for electronic noses, Anal. Chim. Acta (446) pp. 223-232, 2001

International Conferences

  1. M Soto-Gomez, C. Cano, J Reese, PN Robinson, G Valentini and E Casiraghi, Biasing second-order random walk sampling for heterogeneous graph embedding, International Joint Conference on Neural Networks - IJCNN 2025 (accepted)
  2. F. Stacchietti, M. Nicolini, L. Chimirri, P. Robinson, E. Casiraghi and G. Valentini, Modular Deep Neural Networks with residual connections for predicting the pathogenicity of genetic variants in non coding genomic regions, Proc. of IWANN - 18th International Work-Conference on Artificial Neural Networks, Lecture Notes in Computer Science (in press)
  3. M. Nicolini, F. Stacchietti, C. Cano, E. Casiraghi and G. Valentini, A transformer-based model to predict micro RNA interactions, Proc. of IWANN - 18th International Work-Conference on Artificial Neural Networks, Lecture Notes in Computer Science (in press)
  4. M. Nicolini, F. Stacchietti, E. Casiraghi and G. Valentini, Computational Understanding of Pairwise Interactions in ncRNA Data, Proc. of CIBB - 20th conference on Computational Intelligence methods for Bioinformatics and Biostatistics, 2025
  5. E. Fassi, M. Nicolini, E. Saitto, G. Valentini, E. Casiraghi and G. Grazioso, Protein Language Model-Generated Enzyme Sequences Exhibit high stability in Molecular Dynamics Simulation, Proc. of CIBB - 20th conference on Computational Intelligence methods for Bioinformatics and Biostatistics, 2025
  6. R. Jiménez, A. Galeano, M. Báez, S. Ferreyra, G. Melo, G. Valentini, E. Casiraghi, L. Cernuzzi and A. Paccanaro, An Empirical Study of Remote Homology Detection using Protein Language Models, Proc. of the 51th Latin American Conference on Informatics (CLEI), IEEE 2025
  7. M Nicolini, E Saitto, R Jimenez, E Cavalleri, A Galeano, D Malchiodi, A Paccanaro, PN Robinson, E Casiraghi, G Valentini, In silico enzyme prediction and generation by fine tuning protein language models, ELLIS ML for Molecules and Materials in the Era of LLMs Workshop Berlin - December 2024)
  8. M Nicolini, E Saitto, R Jimenez, E Cavalleri, M Mesiti, A Galeano, D Malchiodi, A Paccanaro, PN Robinson, E Casiraghi, G Valentini, Enhancing Enzyme Generation with Fine-Tuned Conditional Transformers, BSB2024, Brazilian Symposyum on Bioinformatics 2024
  9. E Cavalleri, M Soto-Gomez A Pashaeibarough, D Malchiodi, H Caufield, J. Reese, CJ Mungall, PN Robinson, E Casiraghi, G. Valentini, M. Mesiti, SPIREX: Improving LLM-based relation extraction from RNA-focused scientific literature using graph machine learning, International Workshop on LLM+KG: Data Management Opportunities in Unifying Large Language Models+Knowledge Graphs, in conjunction with 50th International Conference on Very Large Databases Guangzhou, China - August 2024
  10. M. Nicolini, D. Malchiodi, A. Cabri, E. Cavalleri, M. Mesiti, A. Paccanaro, PN, Robinson, J. Reese, E. Casiraghi and G. Valentini, Fine-Tuning of Conditional Transformers Improves the Generation of Functionally Characterized Proteins, Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSTEC 2024, Roma, BIOINFORMATICS vol 1, pp.561-568, 2024
  11. J. Gliozzo, A. Patak, A. Puertas-Gallardo, E. Casiraghi and G. Valentini, Patient Similarity Networks Integration for Partial Multimodal Datasets, Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSTEC 2023, Lisboa (Portugal), BIOINFORMATICS vol 3, pp.228-234, 2023
  12. V. Guarino, J. Gliozzo, F. Clarelli, B. Pignolet, K. Misra, E. Mascia, G, Antonino, S. Santoro, L. Ferré, M. Cannizzaro, M. Sorosina, R. Liblau, M. Filippi, E. Mosca, F. Esposito, G. Valentini and E. Casiraghi, Intrinsic-Dimension Analysis for Guiding Dimensionality Reduction in Multi-Omics Data, Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSTEC 2023, Lisboa (Portugal), BIOINFORMATICS vol 3, pp.243-251, 2023
  13. P. Perlasca, M. Frasca, CT Ba, J. Gliozzo, M. Notaro, M. Pennacchioni, G. Valentini, M. Mesiti, Integration and Visual Analysis of Biomolecular Networks Through UNIPred-Web, Current Trends in Web Engineering, Communications in Computer and Information Science vol 1668, pp.192-197, 2023
  14. L. Cappelletti, A. Petrini, J. Gliozzo, E. Casiraghi, M. Schubach, M. Kircher, G. Valentini, Bayesian Optimization Improves Tissue-Specific Prediction of Active Regulatory Regions with Deep Neural Networks, Bioinformatics and Biomedical Engineering. IWBBIO 2020, Granada (Spain), Lecture Notes in Computer Science vol 12108, pp.600-612, 2020
  15. M. Frasca, M. Sepehri, A. Petrini, G. Grossi, G. Valentini, Committee-Based Active Learning to Select Negative Examples for Predicting Protein Functions, 15th Int. Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Lisboa, Lecture Notes in Artificial Intelligence pp.80-87, 2020
  16. C.T. Ba, E. Casiraghi, M. Frasca, J. Gliozzo, G. Grossi, M. Mesiti, M. Notaro, P. Perlasca, A. Petrini, M. Re, G. Valentini, A Graphical Tool for the Exploration and Visual Analysis of Biomolecular Networks, 15th Int. Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Lisboa, Lecture Notes in Artificial Intelligence pp.88-98, 2020
  17. M. Frasca, G. Grossi, G. Valentini, Multitask Hopfield Networks, Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019, Wurzburg (Germany), Lecture Notes in Computer Science vol 11907, pp.349-365, 2020
  18. A. Cuzzocrea, L. Cappelletti, G. Valentini, A neural model for the prediction of pathogenic genomic variants in Mendelian diseases, 1st International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI), Barcelona, 2019
  19. M. Notaro, M. Schubach, M.Frasca, M. Mesiti, P.N. Robinson, G. Valentini, Ensembling Descendant Term Classifiers to Improve Gene - Abnormal Phenotype Predictions, Lecture Notes in Bioinformatics, vol. 10834, pp. 60-69, 2019
  20. M. Frasca, J.F. Fontaine, G. Valentini, M. Mesiti, M. Notaro, D. Malchiodi and M.A. Andrade-Navarro, Disease Genes must Guide Data Source Integration in the Gene Prioritization Process, Lecture Notes in Bioinformatics, vol. 10834, pp. 70-80, 2019
  21. A. Petrini, M. Schubach, M. Re, M. Frasca, M. Mesiti, G. Grossi, T. Castrignano’, P.N. Robinson, G. Valentini, Parameters tuning boosts hyperSMURF predictions of rare deleterious non-coding genetic variants, PeerJ Preprints 5:e3185v1, 2017 presented at Methods, tools & platforms for Personalized Medicine in the Big Data Era - NETTAB 2017, Palermo, Italy
  22. M. Schubach, M. Re, P.N. Robinson, G. Valentini, Variant relevance prediction in extremely imbalanced training sets, F1000Research 2017, 6(ISCB Comm J):1392 (poster) (doi: 10.7490/f1000research.1114637.1), presented at the 25th International Conference on Intelligent Systems for Molecular Biology (ISMB), Prague 2017
  23. J. Lin, M. Mesiti, M. Re and G. Valentini, Within network learning on big graphs using secondary memory-based random walk kernels, Complex Networks & Their Applications V: Proceedings of the 5th International Workshop on Complex Networks and their Applications (COMPLEX NETWORKS 2016), Studies in Computational Intelligence, Springer, pp. 235-245, 2017
  24. P. Perlasca, G. Valentini, M. Frasca, M. Mesiti, Multi-species Protein Function Prediction: Towards Web-based Visual Analytics, Proceedings of the 18th International Conference on Information Integration and Web-based Applications & Services, Singapore, ACM, New York, USA pp. 1-5, 2016
  25. H. Su, G. Valentini, S. Szedmak and J. Rousu, Transport Protein Classification through Structured Prediction and Multiple Kernel Learning, NIPS Workshop on Machine Learning in Computational Biology (MLCB) & Machine Learning in Systems Biology (MLSB) 2015 - Montreal, Canada, December 2015
  26. P.N. Robinson, M.Frasca, S. Kohler, M. Notaro, M. Re, G. Valentini, A hierarchical ensemble method for DAG-structured taxonomies, Multiple Classifier Systems - MCS 2015 - Gunzburg, Germany Lecture Notes in Computer Science, vol. 9132, pp. 15-36, Springer, 2015
  27. G. Valentini, S. Kohler, M. Re, M. Notaro, P.N. Robinson, Prediction of human gene - phenotype associations by exploiting the hierarchical structure of the Human Phenotype Ontology, 3rd International Work-Conference on Bioinformatics and Biomedical Engineering - IWBBIO 2015, Granada, Spain Lecture Notes in Bioinformatics, vol. 9043, pp. 66-77, Springer, 2015
  28. M. Re, M.Mesiti, G. Valentini, An automated pipeline for multi-species protein function prediction from the UniProt Knowledgebase, Automated Function Prediction SIG 2014 - ISMB 2014, Boston, USA
  29. M. Re, M.Mesiti, G. Valentini, On the Automated Function Prediction of Big Multi-Species Networks, Network Biology SIG 2014 - ISMB 2014, Boston, USA
  30. M.Frasca, A. Bertoni, G. Valentini, An unbalance-aware network integration method for gene function prediction, MLSB 2013 - Machine Learning for Systems Biology, Berlin, 2013
  31. G. Valentini, A. Paccanaro, H. C. Vierci, A. E. Romero, M. Re, Network integration boosts disease gene prioritization, Network Biology SIG 2013 - ISMB 2013, Berlin
  32. M.Mesiti, M. Re, G. Valentini, Scalable Network-based Learning Methods for Automated Function Prediction based on the Neo4j Graph-database, Automated Function Prediction SIG 2013 - ISMB 2013, Berlin
  33. H. C. Vierci, A. E. Romero, S. Heron, H. Yang, M. Frasca, M. Mesiti, G. Valentini and A. Paccanaro, GOssTo & GOssToWeb: user-friendly tools for calculating semantic similarities on the Gene Ontology, Bio-Ontologies SIG 2013 - ISMB 2013, Berlin
  34. M. Re, M.Mesiti, G. Valentini, Comparison of early and late omics data integration for cancer modules gene ranking, NETTAB 2012 Workshop on Integrated Bio-Search, Como 14-16 November, 2012
  35. M. Re and G. Valentini, Random walking on functional interaction networks to rank genes involved in cancer, 2nd Artificial Intelligence Applications in Biomedicine Workshop, in: AIAI 2012 - Artificial Intelligence Applications and Innovations, pp. 66-75, IFIP AICT Series, Springer, 2012
  36. M. Re, G. Valentini, Large Scale Ranking and Repositioning of Drugs with Respect to DrugBank Therapeutic Categories, International Symposium on Bioinformatics Research and Applications (ISBRA 2012), Dallas, USA, Lecture Notes in Bioinformatics vol.7292, pp. 225-236, Springer, 2012
  37. M. Re, G. Valentini, Ensemble methods: a review, Advances in Machine Learning and Data Mining for Astronomy, Chapman & Hall Data Mining and Knowledge Discovery Series, Chap. 26, pp. 563-594, 2012
  38. M. Re, G. Valentini, Genes prioritization with respect to Cancer Gene Modules using functional interaction network data, NETTAB 2011 Workshop on Clinical Bioinformatics, Pavia 12-14 October, 2011
  39. A. Bertoni, M. Frasca, G. Valentini, COSNet: a Cost Sensitive Neural Network for Semi-supervised Learning in Graphs, Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2011, Athens, Greece, Proceedings, Part I, Lecture Notes in Artificial Intelligence, vol. 6911, pp.219-234, Springer, 2011
  40. A. Rozza, G. Lombardi, M. Re, E. Casiraghi, G. Valentini and P. Campadelli, A Novel Ensemble Technique for Protein Subcellular Location Prediction, Ensembles in Machine Learning Applications, Studies in Computational Intelligence vol. 373, pp. 151-167, Springer, 2011
  41. M. Frasca, A. Bertoni, G. Valentini, A cost-sensitive neural algorithm to predict gene functions using large biological networks, Network Biology SIG: On the Analysis and Visualization of Networks in Biology, ISMB 2011, Wien
  42. A. Bertoni, M. Re, F. Sacca, G. Valentini, Identification of promoter regions in genomic sequences by 1-dimensional constraint clustering, Frontiers in Artificial Intelligence and Applications, vol. 234, Neural Nets WIRN11 - Proceedings, pp. 162-169, 2011
  43. A. Rozza, G. Lombardi, M. Re, E. Casiraghi, and G. Valentini, DDAG K-TIPCAC: an ensemble method for protein subcellular localization, Proc. of the Third Edition of SUEMA, pp. 75-84, ECML, Barcelona, Spain, 2010
  44. N. Cesa-Bianchi, M. Re, G. Valentini, Functional Inference in FunCat through the Combination of Hierarchical Ensembles with Data Fusion Methods, ICML Workshop on learning from Multi-Label Data MLD’10, Haifa, Israel, pp.13-20, 2010
  45. A. Bertoni, M. Frasca, G. Grossi, G. Valentini, Learning functional linkage networks with a cost-sensitive approach, Neural Networks - WIRN 2010, IOS Press, pp. 52-61, 2010
  46. M. Re, G. Valentini, An experimental comparison of Hierarchical Bayes and True Path Rule ensembles for protein function prediction, Nineth International Workshop on Multiple Classifier Systems MCS 2010, Lecture Notes in Computer Science, vol. 5997, pp. 294-303, Springer, 2010
  47. N. Cesa-Bianchi, G. Valentini, Hierarchical cost-sensitive algorithms for genome-wide gene function prediction, Machine Learning in Systems Biology, Proceedings of the Third international workshop, Ljubljana, Slovenia, pp. 25-34, 2009
  48. M. Re, G. Valentini, Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction, Machine Learning in Systems Biology, Proceedings of the Third international workshop, Ljubljana, Slovenia, pp. 95-104, 2009
  49. G. Valentini, M. Re, Weighted True Path Rule: a multilabel hierarchical algorithm for gene function prediction, MLD-ECML 2009, 1st International Workshop on learning from Multi-Label Data, Bled, Slovenia, pp. 133-146, 2009
  50. M. Re, G. Valentini, Predicting gene expression from heterogeneous data, CIBB 2009, The Sixth International Conference on Bioinformatics and Biostatistics, Genova, Italy, 2009
  51. M. Re, G. Valentini, Comparing early and late data fusion methods for gene function prediction, Neural Nets WIRN09 - Proceedings of the 19th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, 2009, Frontiers in Artificial Intelligence and Applications vol. 204, pp. 197-207, IOS Press, 2009
  52. M. Re, G. Valentini, Ensemble based Data Fusion for Gene Function Prediction, Eighth International Workshop on Multiple Classifier Systems MCS 2009, Lecture Notes in Computer Science, vol.5519 pp.448-457, Springer 2009
  53. G. Valentini, True Path Rule Hierarchical Ensembles, Eighth International Workshop on Multiple Classifier Systems MCS 2009, Lecture Notes in Computer Science, vol.5519 pp.232-241, Springer 2009
  54. O. Okun, G. Valentini, H. Priisalu, Exploring the link between bolstered classification error and dataset complexity for gene expression based cancer classification, New Signal Processing Research, Nova Publishers, pp. 249-278, 2009
  55. A. Bertoni, G. Valentini, Unsupervised stability-based ensembles to discover reliable structures in complex bio-molecular data, Proc. CIBB 2008, The Fifth International Conference on Bioinformatics and Biostatistics, Lecture Notes in Computer Science, vol. 5488 pp. 25-43, Springer, 2009
  56. M. Re, G. Valentini, Prediction of gene function using ensembles of SVMs and heterogeneous data sources, Applications of supervised and unsupervised ensemble methods, Computational Intelligence Series, vol.245, pp. 79-91, Springer, 2010
  57. M. Mesiti, E. J. Ruiz, I. Sanz, R. Berlanga, G. Valentini, P Perlasca, D. Manset, Data Integration and Opportunities in Biological XML Data Management, Open and Novel Issues in XML Database Applications: Future Directions and Advanced Technologies, Information Science, pp. 263-286, 2009
  58. R. Avogadri, M. Brioschi, F. Ruffino, F. Ferrazzi, A. Beghini and G. Valentini, An algorithm to assess the reliability of hierarchical clusters in gene expression data, Knowledge-Based Intelligent Information and Engineering Systems, 12th International Conference, KES 2008, Zagreb, Croatia, September 3-5, 2008, Proceedings, Part III. Lecture Notes in Computer Science, vol.5179 pp. 764-770, Springer 2008
  59. M. Mesiti, E. J. Ruiz, I. Sanz, R. Berlanga, G. Valentini, P Perlasca, D. Manset, XML-based approaches for the integration of heterogeneous bio-molecular data, NETTAB 2008 workshop on: “Bioinformatics Methods for Biomedical Complex System Applications”, 2008
  60. O. Okun, G.Valentini, Dataset Complexity Can Help to Generate Accurate Ensembles of K-Nearest Neighbors, IEEE International Joint Conference on Neural Networks - IJCNN 2008 (IEEE World Congress on Computational Intelligence), pp. 450-457, 2008
  61. R. Avogadri, G.Valentini, Ensemble Clustering with a Fuzzy Approach, Supervised and Unsupervised Ensemble Methods and their Applications, Studies in Computational Intelligence, vol. 126, Springer, 2008
  62. R. Tagliaferri, A. Bertoni, F. Iorio, G. Miele, F. Napolitano, G. Raiconi and G. Valentini, A Review on clustering and visualization methodologies for Genomic data analysis (extended abstract), Workshop on Computational Intelligence approaches for the analysis of Bioinformatics data, IJCNN 2007, Orlando, USA, 2007
  63. A. Bertoni, G.Valentini, Discovering Significant Structures in Clustered Bio-molecular Data Through the Bernstein Inequality, Knowledge-Based Intelligent Information and Engineering Systems, 11th International Conference, KES 2007, Lecture Notes in Computer Science, vol. 4694 pp. 886-891, 2007
  64. R. Avogadri, G.Valentini, Fuzzy ensemble clustering for DNA microarray data analysis, CIBB 2007, The Fourth International Conference on Bioinformatics and Biostatistics, Lecture Notes in Computer Science, vol. 4578, pp.537-543, 2007
  65. R. Avogadri, G.Valentini, An unsupervised fuzzy ensemble algorithmic scheme for gene expression data analysis, NETTAB 2007 workshop on a Semantic Web for Bioinformatics, Pisa, Italy, 2007
  66. A.Bertoni, G.Valentini, Randomized Embedding Cluster Ensembles for gene expression data analysis, SETIT 2007 - IEEE International Conf. on Sciences of Electronic, Technologies of Information and Telecommunications, Hammamet, Tunisia, 2007
  67. F. Ruffino, M. Muselli, G. Valentini, Modeling gene expression data via positive Boolean functions, NETTAB 2006 workshop on Distributed Applications, Web Services, Tools and GRID Infrastructures for Bioinformatics, S.Margherita di Pula 10-13 July, Italy, 2006
  68. A.Bertoni, G. Valentini, Model order selection for clustered bio-molecular data, Probabilistic Modeling and Machine Learning in Structural and Systems Biology, J. Rousu, S. Kaski and E. Ukkonen (Eds.), Tuusula, Finland, 17-18 June, pp. 85-90, Helsinki University Printing House, 2006
  69. A.Bertoni, G. Valentini, Ensembles Based on Random Projections to Improve the Accuracy of Clustering Algorithms, Neural Nets, WIRN 2005, Lecture Notes in Computer Science, vol. 3931, pp. 31-37, 2006
  70. B. Apolloni, G. Valentini, A.Brega, BICA and Random Subspace ensembles for DNA microarray-based diagnosis, CIBB 2006 - International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics In Proc. of 7th International FLINS Conference on Applied Artificial Intelligence pp. 623-631, World Scientific, 2006
  71. F.Ruffino, M. Muselli, G.Valentini, Biological specifications for a synthetic gene expression data generation model, WILF 2005, Lecture Notes in Artificial Intelligence vol. 3849, pp. 277-283, 2006
  72. P. Campadelli, E. Casiraghi, G.Valentini, Lung nodules detection and classification, ICIP 05, The IEEE International Conference on Image Processing, Genova, Italy, 2005
  73. A. Bertoni, G. Valentini, Random projections for assessing gene expression cluster stability, IJCNN ’05. Proceedings IEEE International Joint Conference on Neural Networks, vol. 1 pp. 149-154, 2005
  74. A. Bertoni, R. Folgieri, G. Valentini, Feature selection combined with random subspace ensemble for gene expression based diagnosis of malignancies, Biological and Artificial Intelligence Environments, pp. 29-36, Springer, 2005
  75. A. Bertoni, R. Folgieri, G. Valentini, Random subspace ensembles for the bio-molecular diagnosis of tumors, Models and Metaphors from Biology to Bioinformatics Tools, NETTAB 2004
  76. G. Valentini, Random aggregated and bagged ensembles of SVMs: an empirical bias-variance analysis, Fifth International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 3077, pp. 263-272, 2004
  77. G. Valentini, T.G. Dietterich, Low Bias Bagged Support Vector Machines, The Twentieth International Conference on Machine Learning, ICML 2003, Washington D.C. USA, pp. 752-759, AAAI Press, 2003
  78. G. Valentini, An application of Low Bias Bagged SVMs to the classification of heterogeneous malignant tissues, Pre-WIRN workshop on Bioinformatics and Biostatistic, Lecture Notes in Computer Science, vol. 2859, pp.316-321, 2003
  79. G. Valentini, M. Muselli and F. Ruffino, Bagged Ensembles of SVMs for Gene Expression Data Analysis, IJCNN2003, Proc. of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Portland, USA, pp. 1844-1849, IEEE, 2003
  80. G. Valentini, F. Masulli, Ensembles of learning machines, Neural Nets WIRN Vietri-2002, Lecture Notes in Computer Sciences, vol. 2486, pp. 3-19, 2002
  81. G. Valentini, T.G. Dietterich, Bias-Variance Analysis and Ensembles of SVM, Third International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science vol. 2364, pp. 222-231, 2002
  82. F. Masulli, M. Pardo, G. Sberveglieri, G. Valentini, Boosting and Classification of Electronic Nose Data, Third International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science vol. 2364, pp. 262-271, 2002
  83. G. Valentini, Supervised gene expression data analysis using Support Vector Machines and Multi-Layer Perceptrons, Knowledge-Based Intelligent Information Engineering Systems and Allied technologies - Sixth International Conference on Knowledge-Based Intelligent Information & Engineering Systems KES’2002, special session Machine Learning in Bioinformatics, pp. 482-487, 2002
  84. F. Ruffino, M. Muselli and G. Valentini, [Feature Selection and Bagging Improve Malignancy Prediction based on Gene Expression Data], Understanding the Genome: Scientific Progress and Microarray Technology, Genova, Italy, 2002
  85. G. Valentini, [Identifying different types of human lymphomas by SVM and ensembles of learning machines using DNA microarray data], ISMB 2001 9th International Conference on Intelligent Systems and Molecular Biology (Poster section), Copenaghen, Denmark, 2001
  86. G. Valentini, Classification of human malignancies by machine learning methods using DNA microarray gene expression data, Proceedings of the Fourth International Conference “Neural Networks and Expert Systems in Medicine and HealthCare”, Milos island, Greece, pp. 399-408, 2001
  87. M. Pardo, G. Sberveglieri, G. Valentini, D. Della Casa, F.Masulli, [Boosting applied to electronic nose data], LFTNC-SC 2001 - 2001 NATO ARW on Limits and Future Trends of Neural Computing, 2001
  88. F. Masulli, G. Valentini, M. Pardo, G. Sberveglieri, [Classification of sensor array data by Output Coding decomposition methods], Proc of the International Workshop MATCHEMS 2001, pp. 169-172, Brescia, Italy, 2001
  89. F. Masulli, G. Valentini, Quantitative evaluation of dependence among outputs in ECOC classifiers using mutual information based measures, Proceedings of the International Joint Conference on Neural Networks IJCNN’01, K. Marko and P. Webos (eds.), vol.2, IEEE, Piscataway, NJ, USA, pp. 784-789, 2001
  90. F. Masulli and G. Valentini, Dependence among Codeword Bit Errors in ECOC Learning Machines: an Experimental Analysis, Proceedings of the Second International Workshop Multiple Classifier Systems MCS 2001, Cambridge, UK, Lecture Notes in Computer Science vol. 2096, pp. 158-167, 2001
  91. M. Pardo, G. Sberveglieri, D. Della Casa, F.Masulli, G. Valentini, [Multiple classifiers for electronic nose data], 8th International Symposium on Olfaction and Electronic Noses, Washington, 2001
  92. F. Masulli, G. Valentini, Comparing Decomposition Methods for Classification, KES’2000, Fourth International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies, Brighton, UK, IEEE, Piscataway, NJ, USA, pp. 788-791, 2000
  93. F. Masulli, G. Valentini, Parallel Non Linear Dichotomizers, IJCNN2000, The IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy, vol.2, pp. 29-33, 2000
  94. M. Pardo, G. Sberveglieri, G. Valentini, F. Masulli, [Decompositive classification models for electronic noses], 7th International Symposium on Chemometrics in Analytical Chemistry (CAC), Antwerp, 2000
  95. F. Masulli, G. Valentini, Effectiveness of error correcting output codes in multiclass learning problems, Proceedings of the First International Workshop Multiple Classifier Systems MCS 2000, Cagliari, Italy, Lecture Notes in Computer Science vol.1857, pp.107-116, 2000
  96. G. Valentini, F. Masulli, [NEURObjects, a set of library classes for neural networks development], Proceedings of the third International ICSC Symposia on Intelligent Industrial Automation (IIA’99) and Soft Computing (SOCO’99), ICSC Academic Press, Millet, Canada, 1999, pp. 184-190

Edited books

  1. O. Okun, G. Valentini, M. Re (eds.), Ensembles in Machine Learning Applications, Studies in Computational Intelligence, vol. 373 Springer, ISBN: 978-3-642-22909-1, 2011
  2. O. Okun, M. Re, G. Valentini (eds.), Proceedings of the the Third Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications (SUEMA), European Conference on Machine Learning, Barcelona, Spain, 2010
  3. O. Okun, G. Valentini (eds.), Applications of Supervised and Unsupervised Ensemble Methods, Studies in Computational Intelligence, vol. 245 Springer, ISBN: 978-3-642-03998-0, 2010
  4. O. Okun, G. Valentini (eds.), Proceedings of the the Second Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications (SUEMA), European Conference on Artificial Intelligence, University of Patras, Greece, ISBN: 978-960-89282-2-0, 2008
  5. O. Okun, G. Valentini (eds.), Supervised and Unsupervised Ensemble Methods and their Applications, Studies in Computational Intelligence, vol. 126 Springer, ISBN: 978-3-540-78980-2, 2008

National Conferences

  1. J. Gliozzo, M. Notaro, A. Petrini, P. Perlasca, M. Mesiti, E. Casiraghi, M.Frasca, G. Grossi, M. Re, A. Paccanaro, G. Valentini, Modeling biomolecular profiles in a graph-structured sample space for clinical outcome prediction with melanoma and ovarian cancer patients, BITS 2017, Bioinformatics Italian Society Meeting, Cagliari, Italy, 2017
  2. A. Petrini, M. Notaro, J. Gliozzo, G. Valentini, G. Grossi, M. Frasca, Speeding up node label learning in unbalanced biomolecular networks through a parallel and sparse GPU-based Hopfield model, BITS 2017, Bioinformatics Italian Society Meeting, Cagliari, Italy, 2017
  3. P. Perlasca, M. Mesiti, M. Notaro, A. Petrini, J. Gliozzo, G. Valentini, M. Frasca, A Web Graphical Tool for the Integration of Unbalanced Biomolecular Networks, BITS 2017, Bioinformatics Italian Society Meeting, Cagliari, Italy, 2017
  4. M. Re, M. Mesiti, M. Frasca, J. Lin, G. Valentini, Analysis of bio-molecular networks through semi-supervised graph-based learning methods, Third Italian Workshop on Machine Learning and Data Mining - XIII AIIA Symposium on Artificial Intelligence* (invited talk), Pisa December 2014
  5. M. Dugo, M. Callari, P. Miodini, V. Cappelletti, M.L. Carcangiu, R. Orlandi, G. Valentini, MG Daidone, Performance of single sample predictors in defining breast cancer molecular subtypes, 53rd Annual Meeting of the Italian Cancer Society, Torino, October 2011
  6. A. Bertoni, M. Frasca, G.Valentini, An efficient supervised method to integrate multiple biological networks, BITS 2011, Bioinformatics Italian Society Meeting, Pisa, Italy, 2011
  7. A. Rozza, G. Lombardi, M. Re, E. Casiraghi, G. Valentini, P. Campadelli, A Novel Ensemble Approach for the Subcellular Localization of Proteins, BITS 2011, Bioinformatics Italian Society Meeting, Pisa, Italy, 2011
  8. D. Malchiodi, M. Re and G. Valentini, Uso di Mathematica per la classificazione di dati di qualità variabile, Mathematica Italia User Group Meeting - Atti del Convegno 2010, Adalta (ISBN 978-88-96810-00-2), 2010
  9. M. Re, G.Valentini, Data fusion based gene function prediction using ensemble methods, BITS 2009, Bioinformatics Italian Society Meeting, Genova, Italy, 2009
  10. N. Cesa-Bianchi, G. Valentini, Genome-wide hierarchical classification of gene function, BITS 2009, Bioinformatics Italian Society Meeting, Genova, Italy, 2009
  11. R. Avogadri, A. Bertoni, G. Valentini, An integrated algorithmic procedure for the assessment and discovery of clusters in DNA microarray data, BITS 2009, Bioinformatics Italian Society Meeting, Genova, Italy, 2009
  12. G.Valentini, Statistical methods for the assessment of clusters discovered in bio-molecular data, Proc. of the 6th SIB National Congress, Statistics in Life and Environment Sciences, Pisa, Italy, 2007
  13. A.Bertoni, G.Valentini, A statistical test based on the Bernstein inequality to discover multi-level structures in bio-molecular data, BITS 2007, Bioinformatics Italian Society Meeting, Napoli, Italy, 2007
  14. G.Pavesi, G.Valentini, Classification of co-expressed genes from DNA regulatory regions, BITS 2007, Bioinformatics Italian Society Meeting, Napoli, Italy, 2007
  15. G. Pavesi, G. Valentini, G. Mauri, G. Pesole, Motif Based Classification of Coregulated Genes, BITS 2006, Bioinformatics Italian Society Meeting, Bologna Italy, 2006
  16. A. Bertoni, R. Folgieri, F. Ruffino, G. Valentini, Assessment of clusters reliability for high dimensional genomic data, BITS 2005, Bioinformatics Italian Society Meeting, Milano Italy, 2005
  17. F. Ruffino, G. Valentini, M.Muselli, Evaluation of gene selection methods through artificial and real-world data concerning DNA microarray experiments, BITS 2005, Bioinformatics Italian Society Meeting, Milano Italy, 2005
  18. M. Muselli, F. Ruffino, and G. Valentini, An Artificial Model for Validating Gene Selection Methods, BITS 2004, Bioinformatics Italian Society Meeting, Padova, Italy, 2004
  19. F. Ruffino, G. Valentini, and M. Muselli, Metodi di Bagging e di selezione delle variabili per l’ analisi dei dati di DNA microarray, SIS 2003
  20. G. Valentini, Metodi di apprendimento automatico supervisionato per il riconoscimento di linfomi tramite DNA microarray, Atti III Convegno Federazione Italiana Scienze della Vita - FISV 2001, Riva del Garda (TN), 2001
  21. M. Pardo, G. Benussi, G. Sberveglieri, G. Valentini, F. Masulli and M. Riani, Application of parallel non-linear dichotomizers to electronic noses, INFMeeting 2000, Genova, 2000