- 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 (accepted)
- N. Zhou, Y. Jiang, T. Bergquist, ..., M. Frasca, M. Notaro, G. Grossi, A. Petrini, M. Re, G. Valentini, M. Mesiti, ... 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 20:244, 2019
- 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
- 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
- M. Frasca, G. Grossi, G. Valentini Multitask Hopfield Networks , European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, , Wurzburg (Germany), Lecture Notes in Computer Science (in press), 2019
- 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
- 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
- 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
- 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-S(10): 269-280, 2018
- S. Vascon, M. Frasca, R. Tripodi, G. Valentini, M. Pelillo Protein Function Prediction as a Graph-Transduction Game, Pattern Recognition Letters (in press), 2018 doi.org/10.1016/j.patrec.2018.04.002
- 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 doi.org/10.1186/s12859-017-1854-y awarded by the International Medical Informatics Association (IMIA) as one of the five best "Knowledge Representation and Management" papers of 2017 in the field of Medical Informatics.
- 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.
- 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
- 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
- M. Frasca. Gene2DisCo:Gene to disease using disease commonalities. Artificial Intelligence in Medicine, 2017. In press. DOI:10.1016/j.artmed.2017.08.001.
- M. Frasca and N. Cesa Bianchi. Multitask protein function prediction through task dissimilarity. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017 doi:10.1109/TCBB.2017.2684127
- M. Frasca and D. Malchiodi. Exploiting negative sample selection for prioritizing candidate disease genes. Genomics and Computational Biology, Vol. 3, No. 3 (2017): e47.
- 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.
- 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.
- 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
- P. Perlasca, G. Valentini, M. Frasca, M. Mesiti Multi-species Protein Function Prediction: Towards Web-based Visual Analytics, The 18th International Conference on Information Integration and Web-based Applications & Services (iiWAS2016), Singapore, ACM 2016
- 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
- Y. Jiang, P. Oron, ... M. Re, M. Mesiti, 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
- 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
- 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:1 0.1007/s00521-015-1965-1
- 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
- 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
- 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
- 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
- M. Frasca. Automated Gene Function Prediction through Gene Multifunctionality in Biological Networks. Neurocomputing, vol. 162, pp. 48-56, 2015.
- M. Frasca: Selection of Negatives in Hopfield Networks. International School and Workshop Dynamics of Multi-Level Systems (DYMULT) 2015, Max Planck Institute for the Physics of Complex Systems, Dresden. Poster contribution.
- 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
- 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
- M. Re, M.Mesiti, G. Valentini, On the Automated Function Prediction of Big Multi-Species Networks, Network Biology SIG 2014 - ISMB 2014, Boston, USA
- 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
- 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, vol. 61, Issue 2, pages 63-78, June 2014
- 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
- G. Valentini, Hierarchical Ensemble Methods for Protein Function Prediction, ISRN Bioinformatics, vol. 2014, Article ID 901419, 34 pages, 2014
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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: L. Iliadis et al. (Eds) AIAI 2012 - Artificial Intelligence Applications and Innovations, pp. 66-75, IFIP AICT Series, Springer, 2012
- 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
- M. Re and G. Valentini, Cancer module genes ranking using kernelized score functions BMC Bioinformatics 13 (Suppl 14): S3, 2012.
- 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
- M. Re, M. Mesiti, G.Valentini, Drug reposition 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- G. ValentiniTrue 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
- M. Muselli, A. Bertoni, M. Frasca, A. Beghini, F. Ruffino, and G. ValentiniA 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
- M. Re, G. ValentiniGenes prioritization with respect to Cancer Gene Modules using functional interaction network data , NETTAB 2011 Workshop on Clinical Bioinformatics, Pavia 12-14 October, 2011.
- 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.
- 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, Haifa, Israel, 2010
- M. Re, G. ValentiniNoise tolerance of Multiple Classifier Systems in data integration-based gene function prediction, Journal of Integrative Bioinformatics, 7(3):139, 2010
- 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
- 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
- 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 ECML-SUEMA, pp. 75-84 Barcelona, Spain, 2010
- A. Bertoni, M. Frasca, G. Grossi, G. Valentini Learning functional linkage networks with a cost-sensitive approach , Proc. of WIRN 2010, IOS Press
- M. Re, G. ValentiniIntegration of heterogeneous data sources for gene function prediction using Decision Templates and ensembles of learning machines, Neurocomputing, 73:7-9 pp. 1533-37, 2010, Neurocomputing site
- M. Re, G. ValentiniAn experimental comparison of Hierarchical Bayes and True Path Rule ensembles for protein function prediction, In: Nineth International Workshop on Multiple Classifier Systems MCS 2010, Lecture Notes in Computer Science, vol. 5997, pp. 294-303, Springer, 2010
- 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.
- M. Re, G. Pesole and D.S. Horner,Accurate discrimination of conserved coding and non-coding regions through multiple indicators of evolutionary dynamics, BMC Bioinformatics 10:282, 2009
- 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
- M. Re, G. Pavesi , Detecting conserved coding genomic regions through signal processing of nucleotide substitution patterns, Artif Intell Med. 45(2-3):117-23, 2009
- 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
- G.Valentini, R.Tagliaferri, F.Masulli, Computational Intelligence and Machine Learning in Bioinformatics, Artificial Intelligence in Medicine 45(2), pp. 91-96, 2009
- 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
- G.Pavesi, G.Valentini, Classification of co-expressed genes from DNA regulatory regions, Information Fusion 10(3), pp. 233-241, 2009
- G. Valentini, M. Re, ,Weighted True Path Rule: a multilabel hierarchical algorithm for gene function prediction, ECML-MLD 2009, 1st International Workshop on learning from Multi-Label Data, Bled, Slovenia, pp. 133-146, 2009.
- M. Re, G. Valentini,Predicting gene expression from heterogeneous data, CIBB 2009, The Sixth International Conference on Bioinformatics and Biostatistics, Genova, Italy, 2009.
- 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.
- M. Re, G. iValentini,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.
- 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, 2009.
- 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.
- A. Bertoni, G.Valentini, Discovering multi-level structures in bio-molecular data through the Bernstein inequality BMC Bioinformatics 9 Suppl 2 S4, 2008
- 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
- F. Ruffino, M. Muselli, G.Valentini, Gene expression modelling through positive Boolean functions,International Journal of Approximate Reasoning 47(1), pp. 97-108, 2008.
- M. Mesiti, E. J. Ruiz, I. Sanz, R. Berlanga, G. Valentini, P Perlasca, D. MansetXML-based approaches for the integration of heterogeneous bio-molecular data, NETTAB 2008, workshop on Bioinformatics Methods for Biomedical Complex System Applications, 2008.
- 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.
- A.Bertoni, G.Valentini Model order selection for biomolecular data clustering, BMC Bioinformatics vol.8, Suppl.3, 2007. Mosclust web-site
- G.Valentini Mosclust: a software library for discovering significant structures in bio-molecular data Bioinformatics 23(3):387-389, 2007.
- 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.
- 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
- 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.
- 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.
- G. Valentini, F.Ruffino, Characterization of Lung tumor subtypes through gene expression cluster validity assessment, RAIRO - Theoretical Informatics and Applications, 40:163-176, 2006
- 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
- 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
- A.Bertoni, G. Valentini,Model order selection for clustered bio-molecular data, In: J. Rousu, S. Kaski and E. Ukkonen (Eds.), Probabilistic Modeling and Machine Learning in Structural and Systems Biology, Tuusula, Finland, pp. 85-90, Helsinki University Printing House, 2006
- 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.