P.J. Kersey et al. Ensembl Genomes 2018: an integrated omics infrastructure for non-vertebrate species, Nucleic Acids Research, gkx1011, 2017.

M. Ruffier et al. Ensembl Core Software Resources: storage and programmatic access for DNA sequence and genome annotation, DATABASE - The Journal of Biological Databases and Curation, 2017(1):bax020, 2017.

B.L. Aken et al. Ensembl 2017, Nucleic Acids Research, 45(Database issue):D635-D642, 2017.

A. Yates et al. Ensembl 2016, Nucleic Acids Research, 44(D1):D710-D716, 2016.

F. Cunningham et al. Ensembl 2015, Nucleic Acids Research, 43(Database Issue):D662-D669, 2015.

A. Yates et al. The Ensembl REST API: Ensembl Data for Any Language, Bioinformatics, 31(1):143-145, 2015.

P. Flicek et al. Ensembl 2014, Nucleic Acids Research, 42(Database Issue):D749-D755, 2014.

I. Walsh, A.J.M. Martin, T. Di Domenico, A. Vullo, G. Pollastri, S. Tosatto. CSpritz: Accurate Prediction of Protein Disorder Segments with Annotation for Homology, Secondary Structure and Linear Motifs, Nucleic Acids Research, 39(Suppl.2), W190-6, 2011.

I. Walsh, A. Vullo, G. Pollastri. Recursive Neural Networks for Undirected Graphs for Learning Molecular Endpoints, Proceedings of the PRIB 2009 conference, Sheffield UK, LNCS 5780/2009:391-403, 2009.

I. Walsh, A.J.M. Martin, C. Mooney, E. Rubagotti, A. Vullo, G. Pollastri. Ab Initio and Homology Based Prediction of Protein Domains by Recursive Neural Networks, BMC Bioinformatics, 10:195, 2009.

I. Walsh, A. Vullo, G. Pollastri. An adaptive model for learning molecular endpoints, Similarity-based learning on structures, M.Biehl, B.Hammer, S.Hochreiter, S.C Kremer and T.Villmann Eds., Dagstuhl Seminar Proceedings, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany, 2009.

I. Walsh, D.Baù, A.J.M. Martin, C. Mooney, A. Vullo, G. Pollastri. Ab Initio and Template-Based Prediction of Multi-Class Distance Maps by Two-Dimensional Recursive Neural Networks, BMC Structural Biology, 545.0, 2009.

A.J.M. Martin, A. Vullo, G. Pollastri. Neural Network Pairwise Interaction Fields for Protein Model Quality Assessment, Proceedings of the LION3 conference, Trento (Italy) LNCS 5851, 2009.

D. Baù, I. Walsh, G. Pollastri, A. Vullo. Fast Modeling of Protein Structures Through Multi-level Contact Maps, Computational Biology: New Research, Alona S. Russe editor, Nova Publishers, 2008.

A.J.M. Martin, D. Baù, I. Walsh, A. Vullo, G. Pollastri. Long-range Information and Physicality Constraints Improve Predicted Protein Contact Maps, Journal of Bioinformatics and Computational Biology, 6(5):1001-20, 2008.

A. Vullo, A. Passerini, P. Frasconi, F. Costa, G. Pollastri. On the Convergence of Protein Structure and Dynamics. Statistical Learning Studies of Pseudo Folding Pathways, Proceedings of the EVOBIO 2008, Naples (Italy) LNCS 4973/2008:200-11, 2008.

D. Baù, G. Pollastri, A. Vullo. Distill: a Machine Learning Approach to Ab Initio Protein Structure Prediction, Analysis of Biological Data: A Soft Computing Approach, S. Bandyopadhyay, U. Maulik and J. T. L. Wang eds., World Scientific, 2007.

A. Passerini, A. Vullo. Machine Learning in Structural Genomics, Bioinformatica: sfide e prospettive, Franco Angeli Press, 2007.

G.Pollastri, A.J.M. Martin, C. Mooney, A. Vullo. Accurate Prediction of Protein Secondary Structure and Solvent Accessibility by Consensus Combiners of Sequence and Structure Information, BMC Bioinformatics, 8:201, 2007.

A. Ceroni, A. Passerini, A. Vullo, P. Frasconi. DISULFIND: a Disulfide Bonding State and Cysteine Connectivity Prediction Server, Nucleic Acids Research, 34(Web Server Issue):W177--W181, 2006.

D. Baù, A.J.M. Martin, C. Mooney, A. Vullo, I. Walsh, G. Pollastri. Distill: A Suite of Web Servers for the Prediction of One-, Two- and Three-Dimensional Structural Features of Proteins, BMC Bioinformatics, 7:402, 2006.

C. Mooney, A. Vullo, G. Pollastri. Protein Structural Motif Prediction in Multidimensional φ-ψ Space leads to improved Secondary Structure Prediction, Journal of Computational Biology, 13:8, 1486-1502, 2006.

A. Vullo, O. Bortolami, G. Pollastri, S. Tosatto. Spritz: a Server for the Prediction of Intrinsically Disordered Regions in Protein Sequences Using Kernel Machines, Nucleic Acids Research, 34:W164-W168, 2006.

A. Vullo, I. Walsh, G. Pollastri. A Two-Stage Approach for Improved Prediction of Residue Contact Maps, BMC Bioinformatics, 7:180, 2006.

G. Pollastri, A. Vullo, P. Frasconi, P. Baldi. Modular DAG-RNN Architectures for Assembling Coarse Protein Structures, Journal of Computational Biology, 13:3, 631-650, 2006.

P. Baldi, J. Cheng, A. Vullo. Large-Scale Prediction of Disulphide Bond Connectivity, Advances in Neural Information Processing Systems 17 (NIPS 2004), L. Saul, Y. Weiss, and L. Bottou editors, MIT press, pp.97-104, Cambridge MA, 2004.

A. Ceroni, P. Frasconi, A. Vullo. Protein Structure Assembly from Knowledge of β-sheet Motifs and Secondary Structure, Proceedings of the XV Italian Workshop on Neural Networks, WIRN 04, 2004.

A. Vullo. Learning Protein Structural Representations by Specialised Recursive Connectionist Models, PhD Thesis, Dipartimento di Sistemi e Informatica, Università degli Studi di Firenze, 2004.

A. Vullo, P. Frasconi. Disulfide Connectivity Prediction Using Recursive Neural Networks and Evolutionary Information, Bioinformatics, 20:653-659, 2004.

G. Pollastri, P. Baldi, A. Vullo, P. Frasconi. Prediction of Protein Topologies using GIOHMMs and GRNNs, Advances in Neural Information Processing Systems (NIPS 2003), S.Thrun, S.Becker and K.Obermayer, editors, 15, pp.1449-1456, MIT Press, Cambridge MA, 2003.

A. Vullo, P. Frasconi. Prediction of Protein Coarse Contact Maps, Journal of Bioinformatics and Computational Biology, 1(2):411-431, 2003.

A. Ceroni, P. Frasconi, A. Passerini, A. Vullo. Predicting the Disulfide Bonding State of Cysteines with Combinations of Kernel Machines, Journal of VLSI Signal Processing, 35:287-295, 2003.

A. Ceroni, P. Frasconi, A. Passerini, A. Vullo. A Combination of Support Vector Machines and Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction, AI*IA 2003: Advances in Artificial Intelligence, A. Cappelli and F. Turini, editors, LNCS, volume 2829, pp. 142-153, 2003.

A. Ceroni, P. Frasconi, A. Passerini, A. Vullo. Cysteine Bonding State: Local Prediction and Global Refinement Using a Combination of Kernel Machines and Bidirectional Recurrent Neural Networks, AI*IA 2003: Advances in Artificial Intelligence, A. Cappelli and F. Turini, editors, 2003.

P. Baldi, G. Pollastri, P. Frasconi, A. Vullo. New Machine Learning Methods for the Prediction of Protein Topologies, Artificial Intelligence and Heuristic Methods for Bioinformatics, P. Frasconi and R.Shamir, editors, Vol. 183 of NATO Science Series III: Computer and Systems Sciences, IOS Press, 2003.

A. Vullo, P. Frasconi. A Recursive Connectionist Approach for Predicting Disulfide Connectivity in Proteins, Proceedings of the 2003 ACM symposium on Applied computing (SAC 03), pp. 67-71, Melbourne, Florida USA, 2003.

P. Frasconi, G. Soda, A. Vullo. Hidden Markov Models for Text Categorization in Multi-Page Documents, Journal of Intelligent Information Systems, special issue on automated text categorization, 18(2/3):195-217, 2002.

A. Vullo, P. Frasconi. A Bi-Recursive Neural Network Architecture for the Prediction of Protein Coarse Contact Maps, Proceedings of the 1st IEEE Computer Society Bioinformatics Conference, 18(2/3):195-217, 2002.

P. Frasconi, A. Passerini, A. Vullo. A Two-Stage SVM Architecture for Predicting the Disulfide Bonding State of Cysteines, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing (IEEE NNSP02), special session on signal processing and neural networks for bionformatics, 2002.

A. Vullo. On the Role of Machine Learning in Protein Structure Determination, AI*IA Notizie (journal of the Italian Association for Artificial Intelligence), XV(3):22-30, 2002.

P. Frasconi, A. Vullo. Prediction of Protein Coarse Contact Maps Using Recursive Neural Networks, Proceedings of the IEEE-EMBS Special Topic Conference on Molecular, Cellular and Tissue Engineering, , 2002.

P. Frasconi, G. Soda, A. Vullo. Text Categorization for Multi-Page Documents. A Hybrid Naive Bayes-HMM Approach, Proceedings of the ACM-IEEE Joint Conference on Digital Libraries (JCDL 2001), Roanoke, Virginia USA, 2001.

A.J.M. Martin, D. Baú, C. Mooney, C. Roche, E. Rubagotti, A. Vullo, I. Walsh, G. Pollastri. Protein Structural Features Prediction and Modelling of Cα Traces Through Predicted Structural Constraints, CASP8, Cagliari, Italy.

C. Mooney, A.J.M. Martin, A. Vullo, G. Pollastri. Exploiting Similarity to Proteins of Known Structure Leads to Improved Protein Structural Motif Prediction, ISMB 2007, Vienna, Austria.

D.Baú, A. Vullo, G. Pollastri. A New Neural Network Ranker to Evaluate Protein Structure Prediction, ISMB 2007, Vienna, Austria.

D.Baú, A.J.M. Martin, C. Mooney, A. Vullo, I. Walsh, G. Pollastri. Modelling of Protein Cα Traces Through Residue Contact Maps Predicted by Machine Learning, CASP7, Asilomar, California USA.

C. Mooney, A. Vullo, G. Pollastri. Porter+: A Server for Protein Structural Motif Prediction, ISMB 2006, Fortaleza, Brazil.

I. Walsh, A. Vullo, G. Pollastri. XXStout: Improving the Prediction of Long Range Residue Contacts, ISMB 2006, Fortaleza, Brazil.

D.Baú, A. Vullo, G. Pollastri. Ab Initio Modelling of Protein Cα Traces Through Residue Contact Maps Predicted by Machine Learning, ISMB 2006, Fortaleza, Brazil.

A.J.M. Martin, A. Vullo, G. Pollastri. A Filtering Approach for Improved Modelling of Predicted Contact Maps, ISMB 2006, Fortaleza, Brazil.

D. Baú, A. Vullo, G. Pollastri. Ab Initio Modelling of Protein Cα Traces Through Residue Contact Maps Predicted by Machine Learning, MGMS International Meeting 2005, Dublin, Ireland.

A. Vullo, C.P. Roche, G. Pollastri. Template-based Recognition of Natively Disordered Regions in Proteins, Technical Report UCD-CSI-2012-01.

C. Mooney, A. Vullo, G. Pollastri. Protein Backbone Angle Prediction in Multidimensional φ-ψ Space, Technical Report UCD-CSI-2006-1.