Publications

2024

G. Valentini Exploring the similarity between genetic diseases improves their differential diagnosis and the understanding of their etiology, European Journal of Human Genetics, January 2024

2023

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

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

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

E. Casiraghi and G. Valentini A software resource for large graph processing and analysis - Research briefing - Nature Computational Science 3, 2023

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

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

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, The Lancet eBioMedicine, Vol. 87, 2023

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

2022

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 Academic, Oxford University Press, Jul 18;23(4):bbac207, 2022

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

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

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

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

A Petrini, M. Frasca, E. Casiraghi, S. Bonfitto, T. Castrignanò, P. Robinson, G. Valentini ParBigMen: ParSMURF application to Big genomic and epigenomic data for the detection of pathogenic variants in Mendelian diseases, PRACE Tech.Report, 2022

2021

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

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 37(23), 2021

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 108(9), pages 1564-1577, 2021

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

J.Reese, D. Unni, T. Callahan, L. Cappelletti, V. Ravanmehr, S. Carbon, K. Shefchek, B. Good, J. Balhoff, T. Fontana, H. Blau, N. Matentzoglu, N. Harris, M. Munoz-Torres, M. Haendel, P. Robinson, M. Joachimiak, C. Mungall. KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response, Patterns, vol.2, issue 1, Cell Press, 2021

G. Trucco and V. Cerioli. Non-coding DNA: a methodology for detection and analysis of pseudogenes, BIOINFORMATICS 2021: 12th International Conference on Bioinformatics Models, Methods and Algorithms, 2021

G. Valentini, E. Casiraghi, L. Cappelletti, V. Ravanmehr, T. Fontana, J. Reese, and P. Robinson. Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding, arXiv preprint arXiv:2101.01425, 2021

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

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

S. Bonfitto, L. Cappelletti, F. Trovato, G. Valentini, M. Mesiti. Semi-automatic Column Type Inference for CSV Table Understanding Lecture Notes in Artificial Intelligence - In: SOFSEM 2021: Theory and Practice of Computer Science, Springer, pp. 535-549 , 2021.

G. Marinò, G. Ghidoli, M. Frasca, D. Malchiodi. Compression strategies and space-conscious representations for deep neural networks, 25th International Conference on Pattern Recognition (ICPR), 2021 (in press)

G. Marinò, G. Ghidoli, M. Frasca, D. Malchiodi. Reproducing the sparse Huffman Address Map compression for deep neural networks,Third Workshop on Reproducible Research in Pattern Recognition (RRPR), 2021 (in press)

2020

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. Download citation

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

A. Petrini, M. Mesiti, M. Schubach, M. Frasca, D. Danis, M. Re, G. Grossi, L. Cappelletti, T. Castrignanò, P. Robinson and Giorgio Valentini. parSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants. GigaScience, Oxford University Press 9(5), 2020. Download citation

C. Giannitto, F. M. Sposta, A. Repici, G. Vatteroni, E. Casiraghi, E. Casari, G.M. Ferraroli, A. Fugazza, M.T. Sandri, A. Chiti, L. Balzarini. Chest CT in patients with a moderate or high pretest probability of COVID-19 and negative swab, La Radiologia medica, 125 (12), pp. 1260–1270, 2020

A. Esposito, V. Buscarino, D. Raciti, E. Casiraghi, M. Manini, P. Biondetti, L. Forzenigo. Characterization of liver nodules in patients with chronic liver disease by MRI: performance of the Liver Imaging Reporting and Data System (LI-RADS v. 2018) scale and its comparison with the Likert scale, La radiologia medica, 125 (1), pp. 15-23, 2020

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

B.R. Barricelli, E. Casiraghi, J. Gliozzo, A. Petrini, S. Valtolina. Human digital twin for fitness management, IEEE Access_, 8, pp.26637-26664.

L. Cappelletti, A. Petrini, J. Gliozzo, E. Casiraghi, M. Schubach, M. Kircher and G. Valentini. Bayesian optimization improves tissue-specific prediction of active regulatory regions with deep neural networks. International Work-Conference on Bioinformatics and Biomedical Engineering, 2020. Lecture Notes in Computer Science vol 12108, pp.600-612 Download citation

L. Cappelletti, T. Fontana, G. Donato, L. Tucci, E. Casiraghi and G. Valentini. Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling. Computers, Multidisciplinary Digital Publishing Institute 9(2), 2020. Download citation

M. Frasca, G. Grossi, G. Valentini. Multitask Hopfield Networks. In: Brefeld U., Fromont E., Hotho A., Knobbe A., Maathuis M., Robardet C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science, 11907, pp 349-365, Springer, 2020. Download citation

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, Springer, 2020. Download citation

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, Springer, 2020.

S. Vascon, M. Frasca, R. Tripodi, G. Valentini and M. Pelillo. Protein function prediction as a graph-transduction game. Pattern Recognition Letters, Elsevier 134, 2020. Download citation

2019

N. Zhou, Y. Jiang, [...], M. Frasca, M. Notaro, G. Grossi, A. Petrini, M. Re, G. Valentini, M. Mesiti and others. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. Genome Biology 20(1), 2019. Download citation

M.Sepehri, M. Frasca, Analysis of Novel Annotations in the Gene Ontology for Boosting the Selection of Negative Examples. Proceedings of the 2019 9th International Conference on Biomedical Engineering and Technology (ICBET'19), pp. 294–301, 2019. Download citation

B.R. Barricelli, E. Casiraghi, J. Gliozzo, V. Huber, B.E. Leone, A. Rizzi, B. Vergani. ki67 Nuclei detection and ki67-index estimation: a novel automatic approach based on human vision modeling. BMC Bioinformatics, 20 (1), pp. 1-14, 2019

B.R. Barricelli, E. Casiraghi, D. Fogli. **A survey on digital twin: Definitions, characteristics, applications, and design implications, IEEE Access, 7, pp.167653-167671.

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. Sensors & Transducers, IFSA Publishing, SL 234(6), 2019. Download citation

S. Valtolina, F. Hachem, B. Barricelli, E. Belay, S. Bonfitto and M. Mesiti. Facilitating the Development of IoT Applications in Smart City Platforms. International Symposium on End User Development, 2019. Download citation

A. Cuzzocrea, L. Cappelletti and G. Valentini. A neural model for the prediction of pathogenic genomic variants in Mendelian diseases. Proceedings of the 1st International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI'19), Barcelona, Spain, 2019. [Download citation](/bib/cuzzocrea2019neural.bib)

M. Frasca, J. F. Fontaine, G. Valentini, M. Mesiti, M. Notaro, D. Malchiodi and M. Andrade-Navarro. Disease--Genes Must Guide Data Source Integration in the Gene Prioritization Process. Computational Intelligence Methods for Bioinformatics and Biostatistics: 14th International Meeting, CIBB 2017, Cagliari, Italy, September 7-9, 2017, Revised Selected Papers 10834, 2019. Download citation

P. Perlasca, M. Frasca, C. 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, BioMed Central 20(1), 2019. Download citation

S. Bonfitto, F. Hachem, E. Belay, S. Valtolina and M. Mesiti. On the Bulk Ingestion of IoT Devices from Heterogeneous IoT Brokers. 2019 IEEE International Congress on Internet of Things (ICIOT), 2019. Download citation

B. Barricelli, E. Casiraghi, J. Gliozzo, V. Huber, B. Leone, A. Rizzi and B. Vergani. ki67 nuclei detection and ki67-index estimation: a novel automatic approach based on human vision modeling. BMC Bioinformatics, Springer 20(1), 2019. Download citation

B. Barricelli, E. Casiraghi and D. Fogli. A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications. IEEE Access, IEEE 7, 2019. Download citation

2018

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, BioMed Central 19(10), 2018. Download citation

E. Casiraghi, V. Huber, M. Frasca, M. Cossa, M. Tozzi, L. Rivoltini, B. Leone, A. Villa and B. Vergani. A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections. BMC Bioinformatics, BioMed Central 19(10), 2018. Download citation

P. Boldi, M. Frasca and D. Malchiodi. Evaluating the impact of topological protein features on the negative examples selection. BMC Bioinformatics, BioMed Central 19(14), 2018. Download citation

C. Ba, E. Casiraghi, M. Frasca, J. Gliozzo, G. Grossi, M. Mesiti, M. Notaro, P. Perlasca, A. Petrini, M. Re and Others. A Graphical Tool for the Exploration and Visual Analysis of Biomolecular Networks. International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, 2018. Download citation

V. Huber, V. Vallacchi, V. Fleming, X. Hu, A. Cova, M. Dugo, E. Shahaj, R. Sulsenti, E. Vergani, P. Filipazzi and Others. Tumor-derived microRNAs induce myeloid suppressor cells and predict immunotherapy resistance in melanoma. The Journal of clinical investigation, Am Soc Clin Investig 128(12), 2018. Download citation

2017

M. Schubach, M. Re, P. Robinson and G. Valentini. Imbalance-aware machine learning for predicting rare and common disease-associated non-coding variants. Scientific reports, Nature Publishing Group 7(1), 2017. Download citation

P. Bonizzoni, A.P. Carrieri, G. Della Vedova, R. Rizzi, and G. Trucco. A colored graph approach to perfect phylogeny with persistent characters, THEORETICAL COMPUTER SCIENCE, 2017

P. Bonizzoni, A. P. Carrieri, G. Della Vedova, R. Rizzi, and G. Trucco. Species-driven persistent phylogeny, FUNDAMENTA INFORMATICAE, 2017

M. Frasca. Gene2DisCo: gene to disease using disease commonalities. Artificial intelligence in medicine, Elsevier 82, 2017. Download citation

M. Schubach, M. Re, P. Robinson and G. Valentini. Variant relevance prediction in extremely imbalanced training sets. F1000Research 6, 2017. [Download citation](/bib/schubach2017variant.bib)

M. Frasca and D. Malchiodi. Exploiting negative sample selection for prioritizing candidate disease genes. Genomics and Computational Biology 3(3), 2017. Download citation

M. Frasca and N. Cesa-Bianchi. Multitask protein function prediction through task dissimilarity. IEEE/ACM transactions on computational biology and bioinformatics, IEEE, 2017. Download citation

M. Frasca and G. Valentini. COSNet: An R package for label prediction in unbalanced biological networks. Neurocomputing, Elsevier 237, 2017. Download citation

E. Casiraghi, M. Cossa, V. Huber, M. Tozzi, L. Rivoltini, A. Villa and B. Vergani. MIAQuant, a novel system for automatic segmentation, measurement, and localization comparison of different biomarkers from serialized histological slices. European journal of histochemistry: EJH, PAGEPress 61(4), 2017. Download citation

A. Petrini, M. Schubach, M. Re, M. Frasca, M. Mesiti, G. Grossi, T. Castrignanò, P. Robinson and G. Valentini. Parameters tuning boosts HyperSMURF predictions of rare deleterious non-coding genetic variants. PeerJ Preprints, PeerJ Inc. San Francisco, USA 5, 2017. Download citation

M. Frasca, F. Lipreri and D. Malchiodi. Analysis of informative features for negative selection in protein function prediction. International Conference on Bioinformatics and Biomedical Engineering, 2017. Download citation

M. Notaro, M. Schubach, P. Robinson and G. Valentini. Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods. BMC Bioinformatics, BioMed Central 18(1), 2017. Download citation

M. Notaro, M. Schubach, M. Frasca, M. Mesiti, P. Robinson and G. Valentini. Ensembling descendant term classifiers to improve gene-abnormal phenotype predictions. International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, 2017. Download citation

2016

D. Smedley, M. Schubach, J. Jacobsen, S. Köhler, T. Zemojtel, M. Spielmann, M. Jäger, H. Hochheiser, N. Washington, J. McMurry and Others. A whole-genome analysis framework for effective identification of pathogenic regulatory variants in Mendelian disease. The American Journal of Human Genetics, Elsevier 99(3), 2016. Download citation

Y. Jiang, T. Oron, W. Clark, A. Bankapur, D. D’Andrea, R. Lepore, C. Funk, I. Kahanda, K. Verspoor, A. Ben-Hur and Others. An expanded evaluation of protein function prediction methods shows an improvement in accuracy. Genome biology, BioMed Central 17(1), 2016. Download citation

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, Oxford University Press 32(18), 2016. Download citation

M. Frasca, S. Bassis and G. Valentini. Learning node labels with multi-category hopfield networks. Neural Computing and Applications, Springer 27(6), 2016. Download citation

J. Lin, M. Mesiti, M. Re and G. Valentini. Within network learning on big graphs using secondary memory-based random walk kernels. International Workshop on Complex Networks and their Applications, 2016. Download citation

P. Perlasca, G. Valentini, M. Frasca and M. Mesiti. Multi-species protein function prediction: towards web-based visual analytics.. iiWAS, 2016. Download citation

2015

M. Frasca and D. Malchiodi. Selection of Negatives in Hopfield Networks. International Workshop on Dynamics of Multi-Level Systems (DYMULT), 2015. Download citation

H. Su, G. Valentini, S. Szedmak and J. Rousu. Transport protein classification through structured prediction and multiple lernel learning. NIPS Workshop on Machine Learning in Computational Biology (MLCB) \& Machine Learning in Systems Biology (MLSB), 2015. Download citation

M. Frasca. Automated gene function prediction through gene multifunctionality in biological networks. Neurocomputing, Elsevier 162, 2015. Download citation

G. Valentini, S. Köhler, M. Re, M. Notaro and P. Robinson. Prediction of human gene-phenotype associations by exploiting the hierarchical structure of the human phenotype ontology. International Conference on Bioinformatics and Biomedical Engineering, 2015. [Download citation](/bib/valentini2015prediction.bib)

M. Frasca, A. Bertoni and G. Valentini. UNIPred: Unbalance-aware Network Integration and Prediction of protein functions. Journal of Computational Biology, Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA 22(12), 2015. Download citation

P. Robinson, M. Frasca, S. Köhler, M. Notaro, M. Re and G. Valentini. A hierarchical ensemble method for dag-structured taxonomies. International Workshop on Multiple Classifier Systems, 2015. Download citation

2014

P. Bonizzoni, A.P. Carrieri, G. Della Vedova, and G. Trucco. Explaining Evolution via Constrained Persistent Perfect Phylogeny, BMC GENOMICS, 2014.

M. Mesiti, M. Re and G. Valentini. Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction. GigaScience, BioMed Central 3(1), 2014. Download citation

M. Re, M. Mesiti and G. Valentini. An automated pipeline for multi-species protein function prediction from the UniProt Knowledgebase. Automated Function Prediction SIG-ISMB, 2014. Download citation

H. Caniza, A. Romero, S. Heron, H. Yang, A. Devoto, M. Frasca, M. Mesiti, G. Valentini and A. Paccanaro. GOssTo: a stand-alone application and a web tool for calculating semantic similarities on the Gene Ontology. Bioinformatics, Oxford University Press 30(15), 2014. Download citation

G. Valentini. Hierarchical ensemble methods for protein function prediction. ISRN Bioinformatics, Hindawi Publishing Corporation 2014, 2014. [Download citation](/bib/valentini2014hierarchical.bib)

G. Valentini, A. Paccanaro, H. Caniza, A. Romero and M. Re. An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods. Artificial Intelligence in Medicine, Elsevier 61(2), 2014. Download citation

2013

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, IEEE 10(6), 2013. Download citation

G. Valentini, A. Paccanaro, H. Vierci, A. Romero and M. Re. Network integration boosts disease gene prioritization. Network Biology SIG, 2013. [Download citation](/bib/valentini2013network.bib)

M. Frasca, A. Bertoni, M. Re and G. Valentini. A neural network algorithm for semi-supervised node label learning from unbalanced data. Neural Networks, Elsevier 43, 2013. Download citation

M. Mesiti, M. Re and G. Valentini. Scalable network-based learning methods for automated function prediction based on the neo4j graph-database. Proceedings of the Automated Function Prediction SIG 2013—ISMB Special Interest Group Meeting, 2013. Download citation

M. Frasca, A. Bertoni and G. Valentini. An unbalance-aware network integration method for gene function prediction. MLSB 2013-Machine Learning for Systems Biology, 2013. Download citation

H. Vierci, A. Romero, S. Heron, H. Yang, M. Frasca, M. Mesiti, G. Valentini and A. Paccanaro. GOssTo \& GOssToWeb: user-friendly tools for calculating semantic simi-larities on the Gene Ontology. Bio-Ontologies SIG 2013-ISMB 2013, 2013. [Download citation](/bib/vierci2013gossto.bib)

2012

P. Bonizzoni, C. Braghin, R. Dondi, and G. Trucco. 2012-10-05. The binary perfect phylogeny with persistent characters, In THEORETICAL COMPUTER SCIENCE, 2012

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 (TCBB), IEEE Computer Society Press 9(6), 2012. Download citation

M. Re and G. Valentini. Large scale ranking and repositioning of drugs with respect to DrugBank therapeutic categories. International Symposium on Bioinformatics Research and Applications, 2012. Download citation

N. Cesa-Bianchi, M. Re and G. Valentini. Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference. Machine Learning, Springer 88(1-2), 2012. Download citation

M. Re, M. Mesiti and G. Valentini. Drug repositioning through pharmacological spaces integration based on networks projections. EMBnet. journal 18(A), 2012. Download citation

M. Re, M. Mesiti and G. Valentini. Comparison of early and late omics data integration for cancer modules gene ranking. NETTAB: Integrated Bio-Search, 2012. [Download citation](/bib/re2012comparison.bib)

A. Beghini, F. Corlazzoli, L. Del Giacco, M. Re, F. Lazzaroni, M. Brioschi, G. Valentini, F. Ferrazzi, A. Ghilardi, M. Righi and Others. Regeneration-associated WNT signaling is activated in long-term reconstituting AC133bright acute myeloid leukemia cells. Neoplasia (New York, NY), Neoplasia Press 14(12), 2012. [Download citation](/bib/beghini2012regeneration.bib)

M. Frasca, A. Bertoni and G. Valentini. Regularized network-based algorithm for predicting gene functions with high-imbalanced data. EMBnet. journal 18(A), 2012. Download citation

M. Re and G. Valentini. Cancer module genes ranking using kernelized score functions. BMC Bioinformatics, BioMed Central 13(14), 2012. [Download citation](/bib/re2012cancer.bib)

M. Re and G. Valentini. Random walking on functional interaction networks to rank genes involved in cancer. IFIP International Conference on Artificial Intelligence Applications and Innovations, 2012. Download citation

2011

M. Re and G. Valentini. Genes prioritization with respect to Cancer Gene Modules using functional interaction network data.. NETTAB 2011 Workshop on Clinical Bioinformatics, 2011. Download citation

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, Springer, 2011. Download citation

F. Bredolo, A. Esposito, E. Casiraghi, G. Cornalba and P. Biondetti. Intestinal interposition: the prevalence and clinical relevance of non-hepatodiaphragmatic conditions (non-Chilaiditi forms) documented by CT and review of the literature. La radiologia medica, Springer 116(4), 2011. Download citation

A. Bertoni, M. Re, F. Saccà and G. Valentini. Identification of promoter regions in genomic sequences by 1-dimensional constraint clustering.. WIRN, 2011. [Download citation](/bib/bertoni2011identification.bib)

M. Frasca, A. Bertoni and 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, 2011. Download citation

B. Apolloni and Others. Learning functional linkage networks with a cost-sensitive approach. Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets 226, 2011. [Download citation](/bib/apolloni2011learning.bib)

A. Bertoni, M. Frasca and G. Valentini. COSNet: a cost sensitive neural network for semi-supervised learning in graphs. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2011. Download citation

2010

G. Valentini. True path rule hierarchical ensembles for genome-wide gene function prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, IEEE 8(3), 2010. [Download citation](/bib/valentini2010true.bib)

M. Re and G. Valentini. An experimental comparison of Hierarchical Bayes and True Path Rule ensembles for protein function prediction. International Workshop on Multiple Classifier Systems, 2010. Download citation

N. Cesa-Bianchi, M. Re and 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, 2010. Download citation

M. Rè and G. Valentini. Noise tolerance of Multiple Classifier Systems in data integration-based gene function prediction. Journal of integrative bioinformatics, De Gruyter 7(3), 2010. Download citation

P. Campadelli, E. Casiraghi and S. Pratissoli. A segmentation framework for abdominal organs from CT scans. Artificial Intelligence in Medicine, Elsevier 50(1), 2010. Download citation

A. Rozza, G. Lombardi, M. Re, E. Casiraghi and G. Valentini. DDAG K-TIPCAC: an ensemble method for protein subcellular localization. ECML SUEMA 2010 workshop: supervised and unsupervised ensemble methods and their applications, 2010. Download citation

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, IEEE 8(5), 2010. Download citation

M. Re and G. Valentini. Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction.. MLSB, 2010. [Download citation](/bib/re2010simple.bib)

M. Re and G. Valentini. Integration of heterogeneous data sources for gene function prediction using decision templates and ensembles of learning machines. Neurocomputing, Elsevier 73(7-9), 2010. Download citation

2009

M. Re and G. Valentini. Prediction of gene function using ensembles of SVMs and heterogeneous data sources. Applications of supervised and unsupervised ensemble methods, Springer, 2009. Download citation

M. Re and G. Valentini. Predicting gene expression from heterogeneous data. International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB), 2009. Download citation

M. Mesiti, E. Jimènez-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, BioMed Central 10(12), 2009. Download citation

P. Campadelli, E. Casiraghi, S. Pratissoli and G. Lombardi. Automatic abdominal organ segmentation from CT images. ELCVIA: electronic letters on computer vision and image analysis 8(1), 2009. Download citation

G. Valentini and M. Re. Weighted True Path Rule: a multilabel hierarchical algorithm for gene function prediction. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases: 1st International Workshop on learning from Multi-Label Data, 2009. Download citation

G. Pavesi and G. Valentini. Classification of co-expressed genes from DNA regulatory regions. Information Fusion, Elsevier 10(3), 2009. Download citation

R. Avogadri and G. Valentini. Fuzzy ensemble clustering based on random projections for DNA microarray data analysis. Artificial Intelligence in Medicine, Elsevier 45(2-3), 2009. Download citation

O. Okun, G. Valentini and 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. Download citation

N. Cesa-Bianchi and G. Valentini. Hierarchical cost-sensitive algorithms for genome-wide gene function prediction. Machine Learning in Systems Biology, 2009. [Download citation](/bib/cesa2009hierarchical.bib)

G. Valentini. True path rule hierarchical ensembles. International Workshop on Multiple Classifier Systems, 2009. Download citation

M. Rè and G. Pavesi. Detecting conserved coding genomic regions through signal processing of nucleotide substitution patterns. Artificial intelligence in medicine, Elsevier 45(2-3), 2009. [Download citation](/bib/re2009detecting.bib)

G. Valentini, R. Tagliaferri and F. Masulli. Computational intelligence and machine learning in bioinformatics. Elsevier, 2009. Download citation

N. Apolloni and Others. 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 May 28-30 2009 204, 2009. Download citation

M. Re and G. Valentini. Ensemble based data fusion for gene function prediction. International Workshop on Multiple Classifier Systems, 2009. [Download citation](/bib/re2009ensemble.bib)

M. Rè, G. Pesole and D. Horner. Accurate discrimination of conserved coding and non-coding regions through multiple indicators of evolutionary dynamics. BMC Bioinformatics, BioMed Central 10(1), 2009. [Download citation](/bib/re2009accurate.bib)

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, Inderscience Publishers 1(4), 2009. Download citation

2008

F. Ruffino, M. Muselli and G. Valentini. Gene expression modeling through positive Boolean functions. International journal of approximate reasoning, Elsevier 47(1), 2008. [Download citation](/bib/ruffino2008gene.bib)

R. Avogadri and G. Valentini. Ensemble clustering with a fuzzy approach. Supervised and unsupervised ensemble methods and their applications, Springer, 2008. [Download citation](/bib/avogadri2008ensemble.bib)

A. Bertoni and G. Valentini. Discovering multi--level structures in bio-molecular data through the Bernstein inequality. BMC Bioinformatics, BioMed Central 9(2), 2008. Download citation

P. Campadelli, E. Casiraghi and E. Esposito. Liver segmentation from ct scans: a survey and a new algorithm. Journal of Artificial Intelligence in Medicine, 2008. [Download citation](/bib/campadelli2008liver.bib)

A. Bertoni and G. Valentini. Unsupervised stability-based ensembles to discover reliable structures in complex bio-molecular data. International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, 2008. Download citation

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. International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2008. Download citation

G. Valentini and N. Cesa-Bianchi. Hcgene: a software tool to support the hierarchical classification of genes. Bioinformatics, Oxford University Press 24(5), 2008. [Download citation](/bib/valentini2008hcgene.bib)

2007

G. Trucco and S.L. Fornili. Molecular dynamics simulation of the enterostatin APGPR and VPDPR peptides in water, In CHEMICAL PHYSICS LETTERS, 2007

A. Bertoni and G. Valentini. Model order selection for bio-molecular data clustering. BMC Bioinformatics, BioMed Central 8(2), 2007. Download citation

R. Avogadri and G. Valentini. Fuzzy ensemble clustering for DNA microarray data analysis. International Workshop on Fuzzy Logic and Applications, 2007. Download citation

A. Bertoni and 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, 2007. Download citation

A. Bertoni and G. Valentini. Discovering significant structures in clustered bio-molecular data through the bernstein inequality. International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2007. Download citation

R. Avogadri and G. Valentini. An unsupervised fuzzy ensemble algorithmic scheme for gene expression data analysis. NETTAB 2007 workshop on a Semantic Web for Bioinformatics, 2007. [Download citation](/bib/avogadri2007unsupervised.bib)

2006

A. Bertoni and G. Valentini. Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses. Artificial Intelligence in Medicine, Elsevier 37(2), 2006. Download citation

G. Valentini. Mosclust: a software library for discovering significant structures in bio-molecular data. Bioinformatics, Oxford University Press 23(3), 2006. [Download citation](/bib/valentini2006mosclust.bib)

G. Valentini and F. Ruffino. Characterization of lung tumor subtypes through gene expression cluster validity assessment. RAIRO-Theoretical Informatics and Applications, EDP Sciences 40(2), 2006. Download citation

P. Campadelli, E. Casiraghi and D. Artioli. A fully automated method for lung nodule detection from postero-anterior chest radiographs. IEEE transactions on medical imaging, IEEE 25(12), 2006. Download citation

A. Bertoni and G. Valentini. Model order selection for clustered bio-molecular data. Probabilistic Modeling and Machine Learning in Structural and Systems Biology Workshop, 2006. Download citation

2005

A. Bertoni, R. Folgieri and G. Valentini. Feature selection combined with random subspace ensemble for gene expression based diagnosis of malignancies. Biological and Artificial Intelligence Environments, Springer, 2005. Download citation

A. Bertoni and G. Valentini. Ensembles based on random projections to improve the accuracy of clustering algorithms. Neural nets, Springer, 2005. Download citation

A. Bertoni, R. Folgieri and G. Valentini. Bio-molecular cancer prediction with random subspace ensembles of support vector machines. Neurocomputing, Elsevier 63, 2005. [Download citation](/bib/bertoni2005bio.bib)

P. Campadelli, E. Casiraghi and G. Valentini. Support vector machines for candidate nodules classification. Neurocomputing, Elsevier 68, 2005. [Download citation](/bib/campadelli2005support.bib)

A. Bertoni and G. Valentini. Random projections for assessing gene expression cluster stability. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. 1, 2005. [Download citation](/bib/bertoni2005random.bib)

G. Valentini. Clusterv: a tool for assessing the reliability of clusters discovered in DNA microarray data. Bioinformatics, Oxford University Press 22(3), 2005. [Download citation](/bib/valentini2005clusterv.bib)

2004

G. Valentini, M. Muselli and F. Ruffino. Cancer recognition with bagged ensembles of support vector machines. Neurocomputing, Elsevier 56, 2004. Download citation

2003

G. Valentini. An application of low bias bagged SVMs to the classification of heterogeneous malignant tissues. Italian Workshop on Neural Nets, 2003. Download citation

G. Valentini, M. Muselli and F. Ruffino. Bagged ensembles of support vector machines for gene expression data analysis. Proceedings of the International Joint Conference on Neural Networks, 2003. 3, 2003. Download citation

2002

G. Valentini. Gene expression data analysis of human lymphoma using support vector machines and output coding ensembles. Artificial Intelligence in Medicine, Elsevier 26(3), 2002. Download citation

G. Valentini. Supervised gene expression data analysis using Support Vector Machines and Multi-Layer perceptrons. Proc. of KES’2002, the Sixth International Conference on Knowledge-Based Intelligent Information \& Engineering Systems, special session Machine Learning in Bioinformatics, 2002. Download citation

2001

G. Valentini. Classification of human malignancies by machine learning methods using DNA microarray gene expression data. Fourth International Conference Neural Networks and Expert Systems in Medicine and HealthCare, 2001. Download citation