Gianluca Pollastri

Orcid: 0000-0002-5825-4949

According to our database1, Gianluca Pollastri authored at least 43 papers between 1999 and 2020.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2020
Brewery: deep learning and deeper profiles for the prediction of 1D protein structure annotations.
Bioinform., 2020

SCLpred-EMS: subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks.
Bioinform., 2020

2019
Algorithms for Structure Comparison and Analysis: Docking.
Proceedings of the Encyclopedia of Bioinformatics and Computational Biology - Volume 1, 2019

Algorithms for Structure Comparison and Analysis: Prediction of Tertiary Structures of Proteins.
Proceedings of the Encyclopedia of Bioinformatics and Computational Biology - Volume 1, 2019

2016
Correct machine learning on protein sequences: a peer-reviewing perspective.
Briefings Bioinform., 2016

G-quadruplex Structure Prediction and integration in the GenData2020 data model.
Proceedings of the 7th ACM International Conference on Bioinformatics, 2016

2014
Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks.
BMC Bioinform., 2014

2013
Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules.
J. Chem. Inf. Model., 2013

Accurate prediction of protein enzymatic class by N-to-1 Neural Networks.
BMC Bioinform., 2013

PeptideLocator: prediction of bioactive peptides in protein sequences.
Bioinform., 2013

Porter, PaleAle 4.0: high-accuracy prediction of protein secondary structure and relative solvent accessibility.
Bioinform., 2013

CPPpred: prediction of cell penetrating peptides.
Bioinform., 2013

2011
CSpritz: accurate prediction of protein disorder segments with annotation for homology, secondary structure and linear motifs.
Nucleic Acids Res., 2011

SCLpred: protein subcellular localization prediction by N-to-1 neural networks.
Bioinform., 2011

Machine Learning Approaches for Contact Maps Prediction in CASP9 Experiment.
Proceedings of the Sistemi Evoluti per Basi di Dati, 2011

2010
De Novo Protein Subcellular Localization Prediction by N-to-1 Neural Networks.
Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2010

2009
Ab initio and homology based prediction of protein domains by recursive neural networks.
BMC Bioinform., 2009

Recursive Neural Networks for Undirected Graphs for Learning Molecular Endpoints.
Proceedings of the Pattern Recognition in Bioinformatics, 2009

Neural Network Pairwise Interaction Fields for Protein Model Quality Assessment.
Proceedings of the Learning and Intelligent Optimization, Third International Conference, 2009

An adaptive model for learning molecular endpoints.
Proceedings of the Similarity-based learning on structures, 15.02. - 20.02.2009, 2009

2008
Long-Range Information and Physicality Constraints Improve predicted protein Contact Maps.
J. Bioinform. Comput. Biol., 2008

A neural network approach to ordinal regression.
Proceedings of the International Joint Conference on Neural Networks, 2008

On the Convergence of Protein Structure and Dynamics. Statistical Learning Studies of Pseudo Folding Pathways.
Proceedings of the Evolutionary Computation, 2008

2007
Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information.
BMC Bioinform., 2007

Distill: A Machine Learning Approach to Ab Initio Protein Structure Prediction.
Proceedings of the Analysis of Biological Data: A Soft Computing Approach, 2007

2006
Spritz: a server for the prediction of intrinsically disordered regions in protein sequences using kernel machines.
Nucleic Acids Res., 2006

Modular DAG-RNN Architectures for Assembling Coarse Protein Structures.
J. Comput. Biol., 2006

Protein Structural Motif Prediction in Multidimensional <i>ø</i>-<i>Psi</i> Space Leads to Improved Secondary Structure Prediction.
J. Comput. Biol., 2006

A two-stage approach for improved prediction of residue contact maps.
BMC Bioinform., 2006

Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins.
BMC Bioinform., 2006

2005
Learning protein secondary structure from sequential and relational data.
Neural Networks, 2005

Porter: a new, accurate server for protein secondary structure prediction.
Bioinform., 2005

2004
Combining protein secondary structure prediction models with ensemble methods of optimal complexity.
Neurocomputing, 2004

ICBS: a database of interactions between protein chains mediated by ?-sheet formation.
Bioinform., 2004

2003
The Principled Design of Large-Scale Recursive Neural Network Architectures--DAG-RNNs and the Protein Structure Prediction Problem.
J. Mach. Learn. Res., 2003

2002
A Machine-Learning Strategy for Protein Analysis.
IEEE Intell. Syst., 2002

Prediction of Protein Topologies Using Generalized IOHMMS and RNNs.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners.
Proceedings of the Tenth International Conference on Intelligent Systems for Molecular Biology, 2002

2001
Bidirectional Dynamics for Protein Secondary Structure Prediction.
Proceedings of the Sequence Learning - Paradigms, Algorithms, and Applications, 2001

Improved prediction of the number of residue contacts in proteins by recurrent neural networks.
Proceedings of the Ninth International Conference on Intelligent Systems for Molecular Biology, 2001

2000
Matching Protein b-Sheet Partners by Feedforward and Recurrent Neural Networks.
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, 2000

Protein β-Sheet Partner Prediction by Neural Networks.
Proceedings of the Artificial Neural Networks in Medicine and Biology, 2000

1999
Exploiting the past and the future in protein secondary structure prediction.
Bioinform., 1999


  Loading...