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 peer-reviewed Conferences and book chapters

1. 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.
2. 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 on Artificial Intelligence, vol. 6911, pp.219-234, Springer, 2011.
3. 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
4. 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
   
5. 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.
   
6. M. Re, G. Valentini, Ensemble methods: a review,
   In: Data Mining and Machine Learning for Astronomical Applications , Chapman & Hall (in press)
   
7. 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.
   
8. 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
   
9. 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, 2011
   
10. 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.
    
11. 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.
    
12. 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.
    
13. 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.
    
14. M. Re, G. Valentini, Predicting gene expression from heterogeneous data,
    CIBB 2009, The Sixth International Conference on Bioinformatics and Biostatistics, Genova, Italy, 2009.
    
15. 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.
    
16. 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.
17. 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.
18. 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, 2009.
    
19. 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.
    
20. 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.
    
21. 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.
    
22. 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.
    
23. 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.
24. 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.

25. 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.
26. 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.
    
27. 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. 
28. 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
    
29. 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. 
30. 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.  
31. 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.
32. 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
33. 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.
34. 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.
35. 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.
36. P. Campadelli, E. Casiraghi, G.Valentini, Lung  nodules detection and classification, 
    ICIP 05, The IEEE International Conference on Image Processing, Genova, Italy, 2005.
    
37. 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.
38. 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.
    
39. 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.
40. 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
41. 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.
42. 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.
43. 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.
44. 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.
    
45. 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.
    
46.  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.
47. G. Valentini, Supervised gene expression data analysis using Support Vector Machines and Multi-Layer Perceptrons,
    Sixth International Conference on Knowledge-Based Intelligent Information  & Engineering Systems KES'2002 , special session Machine Learning in Bioinformatics, 2002
48. 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.
49. 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.
    
50. 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.
51. 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.
52. 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
53. 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.
54. 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
55. 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
56. 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.  
57. 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. 
58. 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.
59. 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. 
60. 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