Many of his studies involve connections with topics such as Set (abstract data type) and Template and Programming language. His Set (abstract data type) study often links to related topics such as Programming language. His research is interdisciplinary, bridging the disciplines of Pattern recognition (psychology) and Artificial intelligence. His Artificial intelligence research extends to the thematically linked field of Pattern recognition (psychology). His work on Leverage (statistics) expands to the thematically related Machine learning. In his papers, he integrates diverse fields, such as Artificial neural network and Test set. While working in this field, he studies both Test set and Data mining. Gianluca Pollastri performs integrative Data mining and Overfitting research in his work. With his scientific publications, his incorporates both Overfitting and Artificial neural network.
His Artificial intelligence study frequently draws parallels with other fields, such as Pattern recognition (psychology). His Pattern recognition (psychology) study frequently draws connections to other fields, such as Artificial intelligence. He brings together Biochemistry and Organic chemistry to produce work in his papers. In his research, Gianluca Pollastri undertakes multidisciplinary study on Organic chemistry and Biochemistry. Machine learning and Data mining are two areas of study in which he engages in interdisciplinary research. While working in this field, he studies both Data mining and Machine learning. He integrates several fields in his works, including Protein structure and Protein Data Bank (RCSB PDB). His study deals with a combination of Protein Data Bank (RCSB PDB) and Protein structure. He merges Computational biology with Bioinformatics in his study.
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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.
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)
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)
A neural network approach to ordinal regression
Jianlin Cheng;Zheng Wang;G. Pollastri.
international joint conference on neural network (2008)
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)
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