Home   Home

Giorgio Valentini publications

International Journals

Proceedings of International Conferences

National Journals

Proceedings of National Conference

Edited books

Technical Reports

Most recent works

International Journals

  1. 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.   

  2. 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. doi.org/10.1016/j.ajhg.2016.07.005   

  3. 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. doi.org/10.1186/s13059-016-1037-6    Supplementary Information

  4. 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. doi:10.1093/bioinformatics/btw235    Pre-print version    Supplementary Information

  5. M. Frasca, S.Bassis, G. Valentini Learning node labels with multi-category Hopfield networks,
    Neural Computing and Applications, 27(6), pp 1677-1692, 2016 doi:10.1007/s00521-015-1965-1

  6. M. Frasca, G. Valentini COSNet: an R package for label prediction in unbalanced biological networks,
    Neurocompting, 2016. doi:10.1016/j.neucom.2015.11.096    Bioconductor COSNet web site

  7. 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. doi:10.1089/cmb.2014.0110    Supplementary Information

  8. 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

  9. 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

  10. 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

  11. G. Valentini, Hierarchical Ensemble Methods for Protein Function Prediction,
    ISRN Bioinformatics, vol. 2014, Article ID 901419, 34 pages, 2014

  12. 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 IEEE link Supplemental Material

  13. 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

  14. 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 Science Direct link

  15. 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. IEEE link

  16. 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

  17. M. Re and G. Valentini Cancer module genes ranking using kernelized score functions
    BMC Bioinformatics 13 (Suppl 14): S3, 2012.

  18. 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. Springer link

  19. 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.

  20. 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.

  21. 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.  IEEE CS Digital library

  22. 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.  IEEE CS Digital library

  23. M. Re, G. Valentini, Noise tolerance of Multiple Classifier Systems in data integration-based gene function prediction,   Supplementary Information
    Journal of Integrative Bioinformatics, 7(3):139, 2010

  24. 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.

  25. 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.

  26. 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  doi:10.1016/j.neucom.2009.12.012

  27. 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

  28. 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

  29. G.Valentini, R.Tagliaferri, F.Masulli, Computational Intelligence and Machine Learning in Bioinformatics
    Artificial Intelligence in Medicine 45(2), pp. 91-96, 2009

  30. 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

  31. G.Pavesi, G.Valentini, Classification of co-expressed genes from DNA regulatory regions,
    Information Fusion  10(3), pp. 233-241, 2009

  32. A. Bertoni, G.Valentini, Discovering multi-level structures in bio-molecular data through the Bernstein inequality
    BMC Bioinformatics 9(Suppl 2):S4, 2008

  33. G.Valentini, N. Cesa-Bianchi, HCGene: a software tool to support the hierarchical classification of genes,
    Bioinformatics, 24(5), pp. 729-731, 2008.     HCGene web-site

  34. F. Ruffino,  M. Muselli, G.Valentini, Gene expression modelling through positive Boolean functions,
    International Journal of Approximate Reasoning
    ,   47(1), pp. 97-108, 2008.

  35. A.Bertoni, G.Valentini, Model order selection for biomolecular data clustering,
    BMC Bioinformatics
    , vol.8, Suppl.3, 2007.       Mosclust web-site

  36. G.Valentini,  Mosclust: a software library for discovering significant structures in bio-molecular data
    Bioinformatics 23(3):387-389,  2007.

  37. G. Valentini, F.Ruffino, Characterization of Lung tumor subtypes through gene expression cluster validity assessment,
    RAIRO - Theoretical Informatics and Applications
    ,  40:163-176,  2006.

  38. 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, Science Direct access

  39. G.Valentini,  Clusterv: a tool for assessing the reliability of clusters discovered in DNA microarray data,
    Bioinformatics 22(3):369-370, 2006.      Clusterv web-site

  40. G.Valentini, An experimental bias-variance analysis of SVM ensembles based on resampling techniques,
    IEEE Transactions on Systems, Man and Cybernetics
    , Part B vol.35(6) pp. 1252-1271, 2005  IEEE Explore access

  41. P. Campadelli, E. Casiraghi, G.Valentini, Support Vector Machines for candidate nodules classification,
    Neurocomputing
      vol.68 pp. 281-289, 2005  Science Direct access

  42. A. Bertoni, R. Folgieri, G. Valentini, Bio-molecular cancer prediction with random subspace ensembles of Support Vector Machines,
    Neurocomputing
      vol. 63C pp. 535-539, 2005  Science Direct access

  43. 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, MIT Press,  JMLR link

  44. F. Masulli, G. Valentini, An experimental analysis of the dependence among codeword bit errors in ECOC learning machines.
    Neurocomputing 57 pp. 189-214, 2004,  science direct link

  45. G. Valentini, M. Muselli and F. Ruffino, Cancer recognition with bagged ensembles of Support Vector Machines,
    Neurocomputing  56 pp. 461-466, 2004, science direct link

  46. 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.

  47. 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

  48. G. Valentini, F. Masulli, NEURObjects: an object-oriented library for neural network development
    Neurocomputing
    48(1-4) pp. 623-646 , 2002, science direct link

  49. M. Pardo, G. Sberveglieri, A.Taroni, F. Masulli, G. Valentini Decompositive classification models for electronic noses.
    Anal. Chim. Acta
    (446) pp. 223-232, 2001.

National Journals

  1. F.Ruffino, G.Valentini, M. Muselli, Valutazione di metodi di gene selection per l'analisi di dati con DNA microarray,
    Automazione e Strumentazione
    LIII (10) pp. 106-119, 2005

  2. G. Valentini, Gene expression-based prediction of malignancies,
    AIIA Notizie XV(4) pp. 34-38, 2002

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 ComputationalIntelligence, vol. 126 Springer, ISBN: 978-3-540-78980-2, 2008.

Proceedings of International Conferences and book chapters

  1. 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

  2. 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

  3. M. Notaro, M. Schubach, P.N. Robinson, G. Valentini Ensembling Descendant Term Classifiers to Improve Gene - Abnormal Phenotype Predictions,
    CIBB 2017, The 14th International Conference on Bioinformatics and Biostatistics, Cagliari, Italy, 2017.

  4. 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 ,
    CIBB 2017, The 14th International Conference on Bioinformatics and Biostatistics, Cagliari, Italy, 2017.

  5. 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, doi.org/10.1007/978-3-319-50901-3_19   

  6. 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. doi.org/10.1145/3011141.3011222   

  7. 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

  8. 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

  9. 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

  10. 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

  11. M. Re, M.Mesiti, G. Valentini, On the Automated Function Prediction of Big Multi-Species Networks,
    Network Biology SIG 2014 - ISMB 2014, Boston, USA

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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.

  17. 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

  18. M. Re, G. Valentini Large Scale Ranking and Repositioning of Drugs with Respect to DrugBank Therapeutic Categories, slides
    In: L. Bleris et al. (Eds.): International Symposium on Bioinformatics Research and Applications (ISBRA 2012), Dallas, USA, Lecture Notes in Bioinformatics vol.7292,  pp. 225-236, Springer, 2012.

  19. M. Re, G. Valentini, Ensemble methods: a review,
    In:  Advances in Machine Learning and Data Mining for Astronomy, Chapman & Hall Data Mining and Knowledge Discovery Series, Chap. 26, pp. 563-594, 2012.

  20. 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.

  21. A. Bertoni, M. Frasca, G. Valentini COSNet: a Cost Sensitive Neural Network for Semi-supervised Learning in Graphs.,
    In: "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.

  22. A. Rozza, G. Lombardi, M. Re, E. Casiraghi, G. Valentini and P. Campadelli A Novel Ensemble Technique for Protein Subcellular Location Prediction ,
    In: "Ensembles in Machine Learning Applications",
    Studies in Computational Intelligence vol. 373, pp. 151-167, Springer, 2011

  23. 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

  24. 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.

  25. 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.

  26. 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

  27. 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

  28. M. Re,  G. Valentini, An experimental comparison of Hierarchical Bayes and True Path Rule ensembles for protein function prediction,
    In: (N. El Gayar, J. Kittler and F.  Roli, Eds)
    Nineth International Workshop on Multiple Classifier Systems MCS 2010, Lecture Notes in Computer Science, vol. 5997, pp. 294-303, Springer, 2010.

  29. 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.

  30. 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.

  31. 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.

  32. M. Re, G. Valentini, Predicting gene expression from heterogeneous data,
    CIBB 2009, The Sixth International Conference on Bioinformatics and Biostatistics, Genova, Italy, 2009.

  33. 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.

  34. M. Re, G. Valentini, Ensemble based Data Fusion for Gene Function Prediction,
    In: (J. Kittler, J. Benediktsson, F. Roli,
    Eds.) Eighth International Workshop on Multiple Classifier Systems MCS 2009, Lecture Notes in Computer Science, vol.5519 pp.448-457, Springer 2009.

  35. G. Valentini, True Path Rule Hierarchical Ensembles,
    In: (J. Kittler, J. Benediktsson, F. Roli,
    Eds.) Eighth International Workshop on Multiple Classifier Systems MCS 2009, Lecture Notes in Computer Science, vol.5519 pp.232-241, Springer 2009.

  36. O. Okun, G. Valentini, H. Priisalu, Exploring the link between bolstered classification error and dataset complexity for gene expression based cancer classification
    In T. Maeda, ed.,
    New Signal Processing Research, Nova Publishers, pp. 249-278, 2009.

  37. A. Bertoni, G. Valentini, Unsupervised stability-based ensembles to discover reliable structures in complex bio-molecular data,
    in: Proc. CIBB 2008, The Fifth International Conference on Bioinformatics and Biostatistics,
    Lecture Notes in Computer Science, vol. 5488 pp. 25-43, Springer, 2009.

  38. M. Re, G. Valentini, Prediction of gene function using ensembles of SVMs and heterogeneous data sources,
    in: Applications of supervised and unsupervised ensemble methods, Computational Intelligence Series, vol.245, pp. 79-91, Springer, 2010.

  39. M. Mesiti, E. J. Ruiz, I. Sanz, R. Berlanga, G. Valentini, P Perlasca, D. Manset, Data Integration and Opportunities in Biological XML Data Management,
    in: E. Pardede (editor): Open and Novel Issues in XML Database Applications: Future Directions and Advanced Technologies, Information Science, pp. 263-286, 2009.

  40. 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,
    in: I. Lovrek, R. J. Howlett, L. C. Jain (Eds.): 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.

  41. 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.

  42. 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.

  43. R. Avogadri, G.Valentini, Ensemble Clustering with a Fuzzy Approach,
    in: 
    "Supervised and Unsupervised Ensemble Methods and their Applications", Studies in Computational Intelligence, vol. 126, Springer, 2008.

  44. 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.

  45. 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. 

  46. 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

  47. 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. 

  48. 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.  

  49. 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.

  50. A.Bertoni, G. Valentini, Model order selection for clustered bio-molecular data
    In:
    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, slides

  51. 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.

  52. 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.

  53. F.Ruffino, M. Muselli, G.Valentini Biological specifications for a synthetic gene expression data generation model,
    In: I.Bloch, A. Petrosino, A.Tettamanzi (Eds.) WILF 2005,
    Lecture Notes in Artificial Intelligence vol. 3849, pp. 277-283, 2006.

  54. P. Campadelli, E. Casiraghi, G.Valentini, Lung  nodules detection and classification
    ICIP 05, The IEEE International Conference on Image Processing, Genova, Italy, 2005.

  55. 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.

  56. A. Bertoni, R. Folgieri, G. Valentini, Feature selection combined with random subspace ensemble for gene expression based diagnosis of malignancies,
    In: (B.Apolloni, M.Marinaro and R. Tagliaferri, eds)
    Biological and Artificial Intelligence Environments, pp. 29-36, Springer, 2005.

  57. 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.

  58. G. Valentini, Random aggregated and bagged ensembles of SVMs: an empirical bias-variance analysis,
    In: (F. Roli, J. Kittler , T. Windeatt Eds.) Fifth International Workshop on Multiple Classifier Systems,
    Lecture Notes in Computer Science, vol. 3077, pp. 263-272, 2004, Powerpoint slides

  59. 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.

  60. 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.

  61. 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.

  62. G. Valentini, F. Masulli, Ensembles of learning machines.
    In R. Tagliaferri and M. Marinaro, editors, Neural Nets WIRN Vietri-2002, 
    Lecture Notes in Computer Sciences,  vol. 2486, pp. 3-19, 2002.

  63. G. Valentini, T.G. Dietterich,  Bias-Variance Analysis and Ensembles of SVM.
    In J. Kittler and F. Roli (Eds) Third International Workshop on Multiple Classifier Systems,
    Lecture Notes in Computer Science vol. 2364,  pp. 222-231, 2002.

  64.  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.

  65. G. Valentini, Supervised gene expression data analysis using Support Vector Machines and Multi-Layer Perceptrons,
    In: 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.

  66. 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.

  67. G. Valentini, Identifying different types of human lymphomas by SVM and ensembles of learning machines using DNA microarray data,
    ISMB 2001
    HTML  extended abstract] [HTML slides] 9th International Conference on Intelligent Systems and Molecular Biology (Poster section), Copenaghen, Denmark, 2001.

  68. 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.

  69. 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.

  70. 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

  71. 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.

  72. F. Masulli and G. Valentini, Dependence among Codeword Bit Errors in ECOC Learning Machines: an Experimental Analysis,
    In: J.Kittler and F.Roli (eds.) Proceedings of the Second International Workshop Multiple Classifier Systems MCS 2001, Cambridge, UK,
    Lecture Notes in Computer Science vol. 2096, pp. 158-167, 2001

  73. 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

  74. 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.  

  75. 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. 

  76. 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.

  77. F. Masulli, G. Valentini, Effectiveness of error correcting output codes in multiclass learning problems,
    In: J.Kittler and F.Roli (eds.)
    Proceedings of the First International Workshop Multiple Classifier Systems MCS 2000, Cagliari, Italy, Lecture Notes in Computer Science vol.1857, pp.107-116, 2000. 

  78. 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.



Proceedings of 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 AI*IA 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 Meeeting
    , 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. 

Technical reports

  1. G. Valentini, Ensemble methods based on bias-variance analysis, Ph.D. thesis, DISI - Dipartimento di Informatica e Scienze dell' Informazione - Universita` di Genova - Tech. Rep. TR-03-04, 2003. [pdf

  2. G. Valentini, Classification of human lymphoma using gene expression data, DISI - Dipartimento di Informatica e Scienze dell' Informazione - Universita` di Genova - Tech. Rep. TR-01-07, 2001. [gzipped  postscript]

  3. F. Masulli, G. Valentini, Evaluating dependence among output errors in ECOC learning machines , DISI - Dipartimento di Informatica e Scienze dell' Informazione - Universita` di Genova - Tech. Rep. TR-01-05, 2001. [gzipped  postscript]

  4. F. Masulli, G. Valentini, Mutual information methods for evaluating dependence among outputs in learning machines, DISI - Dipartimento di Informatica e Scienze dell' Informazione - Universita` di Genova - Tech. Rep. TR-01-02, 2001. [gzipped  postscript]

  5. G. Valentini, Upper bounds on the training error of ECOC SVM ensembles, DISI - Dipartimento di Informatica e Scienze dell' Informazione - Universita` di Genova - Tech. Rep. TR-00-17, 2000. [gzipped  postscript]



Last Updated: August 2017