Gianluca Pollastri

6.2k total citations · 3 hit papers
73 papers, 4.4k citations indexed

About

Gianluca Pollastri is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Gianluca Pollastri has authored 73 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 69 papers in Molecular Biology, 25 papers in Materials Chemistry and 12 papers in Computational Theory and Mathematics. Recurrent topics in Gianluca Pollastri's work include Protein Structure and Dynamics (55 papers), Machine Learning in Bioinformatics (50 papers) and Enzyme Structure and Function (23 papers). Gianluca Pollastri is often cited by papers focused on Protein Structure and Dynamics (55 papers), Machine Learning in Bioinformatics (50 papers) and Enzyme Structure and Function (23 papers). Gianluca Pollastri collaborates with scholars based in Ireland, United States and Italy. Gianluca Pollastri's co-authors include Pierre Baldi, Catherine Mooney, Denis C. Shields, Aoife McLysaght, Niall Haslam, Burkhard Rost, Alessandro Vullo, Mirko Torrisi, Paolo Frasconi and Søren Brunak and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and PLoS ONE.

In The Last Decade

Gianluca Pollastri

69 papers receiving 4.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles

2002 557 citations Gianluca Pollastri, Pierre Baldi et al. Proteins Structure Function and Bioinformatics profile →
Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity

2012 461 citations Catherine Mooney, Niall Haslam et al. PLoS ONE profile →
Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules

2013 390 citations Gianluca Pollastri, Pierre Baldi et al. profile →

Peers

Countries citing papers authored by Gianluca Pollastri

Since Specialization

Citations

This map shows the geographic impact of Gianluca Pollastri's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gianluca Pollastri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gianluca Pollastri more than expected).

Fields of papers citing papers by Gianluca Pollastri

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gianluca Pollastri. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Gianluca Pollastri. The network helps show where Gianluca Pollastri may publish in the future.

Co-authorship network of co-authors of Gianluca Pollastri

This figure shows the co-authorship network connecting the top 25 collaborators of Gianluca Pollastri. A scholar is included among the top collaborators of Gianluca Pollastri based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gianluca Pollastri. Gianluca Pollastri is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	PUNCH2: Explore the strategy for intrinsically disordered protein predictor 2025 ·PLoS ONE ·(unknown), Gianluca Pollastri	5
2	PUNCH: An Interactive Web Server for Predicting Intrinsically Disordered Regions in Protein Sequences 2025 ·Journal of Molecular Biology ·(unknown), Gianluca Pollastri	0
3	DeepPredict: a state-of-the-art web server for protein secondary structure and relative solvent accessibility prediction 2025 ·Frontiers in Bioinformatics ·(unknown), (unknown), Gianluca Pollastri	0
4	A Comparison of Mutual Information, Linear Models and Deep LearningNetworks for Protein Secondary Structure Prediction 2023 ·Current Bioinformatics ·(unknown), (unknown), (unknown), Gianluca Pollastri, Giuseppe Serra, Federico Fogolari	3
5	Deeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction 2019 ·Scientific Reports ·Mirko Torrisi, (unknown), Gianluca Pollastri	65
6	PaleAle 5.0: prediction of protein relative solvent accessibility by deep learning 2019 ·Amino Acids ·(unknown), Mirko Torrisi, Catherine Mooney, Gianluca Pollastri	25
7	CSpritz: accurate prediction of protein disorder segments with annotation for homology, secondary structure and linear motifs 2011 ·Nucleic Acids Research ·Ian Walsh, Alberto J. M. Martín, Tomás Di Domenico, Alessandro Vullo, Gianluca Pollastri, Silvio C. E. Tosatto	70
8	In Silico Protein Motif Discovery and Structural Analysis 2011 ·Methods in molecular biology ·Catherine Mooney, Norman E. Davey, Alberto J. M. Martín, Ian Walsh, Denis C. Shields, Gianluca Pollastri	2
9	Prediction of Short Linear Protein Binding Regions 2011 ·Journal of Molecular Biology ·Catherine Mooney, Gianluca Pollastri, Denis C. Shields, Niall Haslam	63
10	Beyond the Twilight Zone: Automated prediction of structural properties of proteins by recursive neural networks and remote homology information 2009 ·Proteins Structure Function and Bioinformatics ·Catherine Mooney, Gianluca Pollastri	41
11	Ab initio and homology based prediction of protein domains by recursive neural networks 2009 ·BMC Bioinformatics ·Ian Walsh, Alberto J. M. Martín, Catherine Mooney, Enrico Rubagotti, Alessandro Vullo, Gianluca Pollastri	14
12	Structural Alphabets for Protein Structure Classification: A Comparison Study 2008 ·Journal of Molecular Biology ·Quan Le, Gianluca Pollastri, Patrice Koehl	25
13	Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins 2006 ·BMC Bioinformatics ·Davide Baù, Alberto J. M. Martín, Catherine Mooney, Alessandro Vullo, Ian Walsh, Gianluca Pollastri	76
14	Spritz: a server for the prediction of intrinsically disordered regions in protein sequences using kernel machines 2006 ·Nucleic Acids Research ·Alessandro Vullo, Oscar Bortolami, Gianluca Pollastri, Silvio C. E. Tosatto	107
15	Porter: a new, accurate server for protein secondary structure prediction 2004 ·Computer applications in the biosciences ·Gianluca Pollastri, Aoife McLysaght	379
16	ICBS: a database of interactions between protein chains mediated by β-sheet formation 2004 ·Bioinformatics ·Yimeng Dou, (unknown), Gianluca Pollastri, (unknown), James S. Nowick, Pierre Baldi	45
17	Prediction of Protein Topologies Using Generalized IOHMMs and RNNs 2002 ·Neural Information Processing Systems ·Gianluca Pollastri, Pierre Baldi, Alessandro Vullo, Paolo Frasconi	0
18	Improved prediction of the number of residue contacts in proteins by recurrent neural networks 2001 ·Bioinformatics ·Gianluca Pollastri, Pierre Baldi, Piero Fariselli, Rita Casadio	35
19	Bidirectional Dynamics for Protein Secondary Structure Prediction 1999 ·Pierre Baldi, Søren Brunak, Paolo Frasconi, Gianluca Pollastri	4
20	Exploiting the past and the future in protein secondary structure prediction 1999 ·Bioinformatics ·Pierre Baldi, Søren Brunak, Paolo Frasconi, G. Soda, Gianluca Pollastri	346

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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