Gianluca Pollastri spends much of his time researching Recurrent neural network, Artificial intelligence, Protein structure prediction, Artificial neural network and Machine learning. His research investigates the connection between Recurrent neural network and topics such as Directed acyclic graph that intersect with problems in Theoretical computer science, Feedforward neural network, Representation, Gradient descent and Graphical model. Artificial intelligence is closely attributed to Protein secondary structure in his study.
His Protein secondary structure study combines topics from a wide range of disciplines, such as Similarity and Protein Data Bank. His Protein structure prediction research incorporates themes from Probabilistic logic, Data mining and Pattern recognition. His study in the field of Protein secondary structure prediction also crosses realms of Molecular descriptor.
His primary areas of investigation include Artificial intelligence, Machine learning, Artificial neural network, Protein structure prediction and Protein secondary structure. His study in the fields of Recurrent neural network and Deep learning under the domain of Artificial intelligence overlaps with other disciplines such as Sequence and Web server. His research investigates the link between Recurrent neural network and topics such as Protein secondary structure prediction that cross with problems in Overfitting.
His research integrates issues of Sequence learning, Contact map and Protein folding in his study of Machine learning. His Artificial neural network research integrates issues from Theoretical computer science, Support vector machine, Feature vector, Algorithm and Computational biology. The Protein secondary structure study combines topics in areas such as Polyproline helix, Data mining, Structural motif, Stereochemistry and Protein Data Bank.
His primary areas of study are Artificial intelligence, Protein secondary structure, Deep learning, Machine learning and Function. As a member of one scientific family, Gianluca Pollastri mostly works in the field of Artificial intelligence, focusing on Pattern recognition and, on occasion, Helix and Protein secondary structure prediction. His work deals with themes such as Protein structure, Polyproline helix and Stereochemistry, which intersect with Protein secondary structure.
His Deep learning research incorporates elements of Recurrent neural network, Protein structure prediction and Convolutional neural network. His Convolutional neural network research includes elements of Artificial neural network, Subcellular localization and Computational biology. His Machine learning study typically links adjacent topics like Structural motif.
Gianluca Pollastri mainly focuses on Recurrent neural network, Deep learning, Artificial intelligence, Protein structure prediction and Machine learning. His Artificial intelligence study combines topics in areas such as Protein structure and Pattern recognition. His research in Pattern recognition intersects with topics in Helix and Protein secondary structure, Protein secondary structure prediction.
His Convolutional neural network study frequently links to related topics such as Structural bioinformatics. His Leverage study frequently draws connections between adjacent fields such as Artificial neural network. Among his Function studies, you can observe a synthesis of other disciplines of science such as Structure, Sequence and Encoding.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles.
Gianluca Pollastri;Darisz Przybylski;Burkhard Rost;Pierre Baldi.
Exploiting the past and the future in protein secondary structure prediction.
Pierre Baldi;Søren Brunak;Paolo Frasconi;Giovanni Soda.
international conference on bioinformatics (1999)
Porter: a new, accurate server for protein secondary structure prediction
Gianluca Pollastri;Aoife Mclysaght.
Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules
Alessandro Lusci;Gianluca Pollastri;Pierre Baldi.
Journal of Chemical Information and Modeling (2013)
Prediction of coordination number and relative solvent accessibility in proteins.
Gianluca Pollastri;Pierre Baldi;Pietro Fariselli;Rita Casadio.
The principled design of large-scale recursive neural network architectures--dag-rnns and the protein structure prediction problem
Pierre Baldi;Gianluca Pollastri.
Journal of Machine Learning Research (2003)
Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners.
Gianluca Pollastri;Pierre Baldi.
intelligent systems in molecular biology (2002)
Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity
Catherine Mooney;Niall J. Haslam;Gianluca Pollastri;Denis C. Shields.
PLOS ONE (2012)
Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information.
Gianluca Pollastri;Alberto J. M. Martin;Catherine Mooney;Alessandro Vullo.
BMC Bioinformatics (2007)
Spritz: a server for the prediction of intrinsically disordered regions in protein sequences using kernel machines.
Alessandro Vullo;Oscar Bortolami;Gianluca Pollastri;Silvio C. E. Tosatto.
Nucleic Acids Research (2006)
Profile was last updated on December 6th, 2021.
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