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39 results on '"Quadrini Michela"'

1. Structural relation matching: an algorithm to identify structural patterns into RNAs and their interactions

Author

Quadrini Michela

Subjects
core, loops, relation matrix, relations, shape, structural pattern, Biotechnology, TP248.13-248.65
Abstract

RNA molecules play crucial roles in various biological processes. Their three-dimensional configurations determine the functions and, in turn, influences the interaction with other molecules. RNAs and their interaction structures, the so-called RNA–RNA interactions, can be abstracted in terms of secondary structures, i.e., a list of the nucleotide bases paired by hydrogen bonding within its nucleotide sequence. Each secondary structure, in turn, can be abstracted into cores and shadows. Both are determined by collapsing nucleotides and arcs properly. We formalize all of these abstractions as arc diagrams, whose arcs determine loops. A secondary structure, represented by an arc diagram, is pseudoknot-free if its arc diagram does not present any crossing among arcs otherwise, it is said pseudoknotted. In this study, we face the problem of identifying a given structural pattern into secondary structures or the associated cores or shadow of both RNAs and RNA–RNA interactions, characterized by arbitrary pseudoknots. These abstractions are mapped into a matrix, whose elements represent the relations among loops. Therefore, we face the problem of taking advantage of matrices and submatrices. The algorithms, implemented in Python, work in polynomial time. We test our approach on a set of 16S ribosomal RNAs with inhibitors of Thermus thermophilus, and we quantify the structural effect of the inhibitors.

Published
2021
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2. The $\mu\mathcal{G}$ Language for Programming Graph Neural Networks

Author

Belenchia, Matteo, Corradini, Flavio, Quadrini, Michela, and Loreti, Michele

Subjects
Computer Science - Formal Languages and Automata Theory, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, D.2.4
Abstract

Graph neural networks form a class of deep learning architectures specifically designed to work with graph-structured data. As such, they share the inherent limitations and problems of deep learning, especially regarding the issues of explainability and trustworthiness. We propose $\mu\mathcal{G}$, an original domain-specific language for the specification of graph neural networks that aims to overcome these issues. The language's syntax is introduced, and its meaning is rigorously defined by a denotational semantics. An equivalent characterization in the form of an operational semantics is also provided and, together with a type system, is used to prove the type soundness of $\mu\mathcal{G}$. We show how $\mu\mathcal{G}$ programs can be represented in a more user-friendly graphical visualization, and provide examples of its generality by showing how it can be used to define some of the most popular graph neural network models, or to develop any custom graph processing application.

Published
2024

3. Monitoring Local and Global Properties of Collective Adaptive Systems

Author

Del Giudice, Nicola, Loreti, Michele, Quadrini, Michela, Rehman, Aniqa, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Margaria, Tiziana, editor

Published
2025
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5. A Spatial Logic for Simplicial Models

Author

Loreti, Michele and Quadrini, Michela

Subjects
Computer Science - Logic in Computer Science
Abstract

Collective Adaptive Systems often consist of many heterogeneous components typically organised in groups. These entities interact with each other by adapting their behaviour to pursue individual or collective goals. In these systems, the distribution of these entities determines a space that can be either physical or logical. The former is defined in terms of a physical relation among components. The latter depends on logical relations, such as being part of the same group. In this context, specification and verification of spatial properties play a fundamental role in supporting the design of systems and predicting their behaviour. For this reason, different tools and techniques have been proposed to specify and verify the properties of space, mainly described as graphs. Therefore, the approaches generally use model spatial relations to describe a form of proximity among pairs of entities. Unfortunately, these graph-based models do not permit considering relations among more than two entities that may arise when one is interested in describing aspects of space by involving interactions among groups of entities. In this work, we propose a spatial logic interpreted on simplicial complexes. These are topological objects, able to represent surfaces and volumes efficiently that generalise graphs with higher-order edges. We discuss how the satisfaction of logical formulas can be verified by a correct and complete model checking algorithm, which is linear to the dimension of the simplicial complex and logical formula. The expressiveness of the proposed logic is studied in terms of the spatial variants of classical bisimulation and branching bisimulation relations defined over simplicial complexes.

Published
2021
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8. Stress Detection from Wearable Sensor Data Using Gramian Angular Fields and CNN

Author

Quadrini, Michela, Daberdaku, Sebastian, Blanda, Alessandro, Capuccio, Antonino, Bellanova, Luca, Gerard, Gianluca, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pascal, Poncelet, editor, and Ienco, Dino, editor

Published
2022
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9. Sibilla: A Tool for Reasoning about Collective Systems

Author

Del Giudice, Nicola, Matteucci, Lorenzo, Quadrini, Michela, Rehman, Aniqa, Loreti, Michele, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, ter Beek, Maurice H., editor, and Sirjani, Marjan, editor

Published
2022
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10. ProSPs: Protein Sites Prediction Based on Sequence Fragments

Author

Quadrini, Michela, Cavallin, Massimo, Daberdaku, Sebastian, Ferrari, Carlo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nicosia, Giuseppe, editor, Ojha, Varun, editor, La Malfa, Emanuele, editor, La Malfa, Gabriele, editor, Jansen, Giorgio, editor, Pardalos, Panos M., editor, Giuffrida, Giovanni, editor, and Umeton, Renato, editor

Published
2022
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12. Searching RNA Substructures with Arbitrary Pseudoknots

Author

Quadrini, Michela, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Panuccio, Gabriella, editor, Rocha, Miguel, editor, Fdez-Riverola, Florentino, editor, Mohamad, Mohd Saberi, editor, and Casado-Vara, Roberto, editor

Published
2021
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13. Hierarchical Representation and Graph Convolutional Networks for the Prediction of Protein–Protein Interaction Sites

Author

Quadrini, Michela, Daberdaku, Sebastian, Ferrari, Carlo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nicosia, Giuseppe, editor, Ojha, Varun, editor, La Malfa, Emanuele, editor, Jansen, Giorgio, editor, Sciacca, Vincenzo, editor, Pardalos, Panos, editor, Giuffrida, Giovanni, editor, and Umeton, Renato, editor

Published
2020
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14. Label Core for Understanding RNA Structure

Author

Quadrini, Michela, Merelli, Emanuela, Piergallini, Riccardo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cazzaniga, Paolo, editor, Besozzi, Daniela, editor, Merelli, Ivan, editor, and Manzoni, Luca, editor

Published
2020
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21. Topological Classification of RNA Structures via Intersection Graph

Author

Quadrini, Michela, Culmone, Rosario, Merelli, Emanuela, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Martín-Vide, Carlos, editor, Neruda, Roman, editor, and Vega-Rodríguez, Miguel A., editor

Published
2017
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39. Exploiting the Role of Features for Antigens-Antibodies Interaction Site Prediction.

Author

Quadrini M and Ferrari C

Subjects
Antigens immunology, Binding Sites, Antibody, Antibodies immunology, Antibodies chemistry, Humans, Antigen-Antibody Complex chemistry, Antigen-Antibody Complex immunology, Protein Binding, Machine Learning, Databases, Protein, Algorithms, Computational Biology methods
Abstract

Antibodies are a class of proteins that recognize and neutralize pathogens by binding to their antigens. They are the most significant category of biopharmaceuticals for both diagnostic and therapeutic applications. Understanding how antibodies interact with their antigens plays a fundamental role in drug and vaccine design and helps to comprise the complex antigen binding mechanisms. Computational methods for predicting interaction sites of antibody-antigen are of great value due to the overall cost of experimental methods. Machine learning methods and deep learning techniques obtained promising results.In this work, we predict antibody interaction interface sites by applying HSS-PPI, a hybrid method defined to predict the interface sites of general proteins. The approach abstracts the proteins in terms of hierarchical representation and uses a graph convolutional network to classify the amino acids between interface and non-interface. Moreover, we also equipped the amino acids with different sets of physicochemical features together with structural ones to describe the residues. Analyzing the results, we observe that the structural features play a fundamental role in the amino acid descriptions. We compare the obtained performances, evaluated using standard metrics, with the ones obtained with SVM with 3D Zernike descriptors, Parapred, Paratome, and Antibody i-Patch., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Published
2024
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