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  1. Book ; Online: Efficacy, Safety, and Immunogenicity of Vaccines against Viruses: From Network Medicine to Clinical Experimentation

    Guzzi, Pietro Hiram / Milano, Marianna / Das, Jayanta Kumar

    2023  

    Keywords Research & information: general ; Biology, life sciences ; Biochemistry ; monkeypox ; mpox ; MPXV ; universal vaccine ; multi-epitope mRNA vaccine ; immunoinformatics ; influenza ; H3N2 ; antigenic distance ; hemagglutinin ; attribute network embedding ; herpes simplex virus ; HSV-2 ; vaccine ; costimulation ; genital ; antibodies ; T cells ; MDV ; chickens ; Th17 cells ; IL-17A ; interferon-gamma and adaptive immunity ; adenoviral vector ; cell fusion ; human endogenous retrovirus type W (HERV-W) ; R-peptide ; Syncytin-1 ; HIV ; PLWH ; ART ; vaccination ; immune responses ; CD4 ; COVID-19 ; HPV ; respiratory syncytial virus ; RSV ; mucosal vaccine ; inactivated vaccine ; low-energy electron irradiation ; LEEI ; PC formulation ; PCLS ; binding antibody assay ; immune correlates of protection ; modified treatment policy ; neutralizing antibody assay ; principal stratification ; principal surrogate ; SARS-CoV-2 ; stochastic intervention ; stochastic interventional vaccine efficacy ; peste des petits ruminants ; ewes ; lambs ; passive immunity ; quadrivalent adjuvanted influenza vaccines ; toll-like receptors ; CVID ; azoximer bromide
    Language English
    Size 1 electronic resource (204 pages)
    Publisher MDPI - Multidisciplinary Digital Publishing Institute
    Publishing place Basel
    Document type Book ; Online
    Note English
    HBZ-ID HT030645798
    ISBN 9783036592329 ; 3036592326
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Book ; Online ; E-Book: Biological network analysis

    Guzzi, Pietro Hiram / Roy, Swarup

    trends, approaches, graph theory, and algorithms

    2020  

    Author's details Pietro Hiram Guzzi, Swarup Roy
    Keywords Electronic books
    Language English
    Size 1 Online-Ressource (xix, 189 Seiten), Illustrationen, Diagramme
    Publisher Elsevier
    Publishing place Amsterdam
    Publishing country Netherlands
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    HBZ-ID HT020537530
    ISBN 978-0-12-819351-8 ; 9780128193501 ; 0-12-819351-4 ; 0128193506
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  3. Book: Microarray data analysis

    Guzzi, Pietro Hiram

    methods and applications

    (Methods in molecular biology ; 1375 ; Springer protocols)

    2016  

    Author's details edited by Pietro Hiram Guzzi
    Series title Methods in molecular biology ; 1375
    Springer protocols
    Collection
    Keywords Non-coding RNA.
    Subject code 572.88
    Language English
    Size xi, 226 Seiten, Illustrationen, Diagramme, 26 cm
    Edition Second edition
    Publisher Humana Press
    Publishing place New York
    Publishing country United States
    Document type Book
    HBZ-ID HT018909548
    ISBN 978-1-4939-3172-9 ; 978-1-4939-3173-6 ; 1-4939-3172-5 ; 1-4939-3173-3
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    Zs A 7042 -1375- verfügbar in Königswinter Königswinter

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  4. Article ; Online: Editorial: Graph representation learning in biological network.

    Roy, Swarup / Guzzi, Pietro Hiram / Kalita, Jugal

    Frontiers in bioinformatics

    2023  Volume 3, Page(s) 1222711

    Language English
    Publishing date 2023-06-09
    Publishing country Switzerland
    Document type Editorial
    ISSN 2673-7647
    ISSN (online) 2673-7647
    DOI 10.3389/fbinf.2023.1222711
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Editorial

    Swarup Roy / Pietro Hiram Guzzi / Jugal Kalita

    Frontiers in Bioinformatics, Vol

    Graph representation learning in biological network

    2023  Volume 3

    Keywords graph ; representation learning ; embedding ; complex network ; regulatory network ; protein network ; Computer applications to medicine. Medical informatics ; R858-859.7
    Language English
    Publishing date 2023-06-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Disease spreading modeling and analysis: a survey.

    Hiram Guzzi, Pietro / Petrizzelli, Francesco / Mazza, Tommaso

    Briefings in bioinformatics

    2022  Volume 23, Issue 4

    Abstract: Motivation: The control of the diffusion of diseases is a critical subject of a broad research area, which involves both clinical and political aspects. It makes wide use of computational tools, such as ordinary differential equations, stochastic ... ...

    Abstract Motivation: The control of the diffusion of diseases is a critical subject of a broad research area, which involves both clinical and political aspects. It makes wide use of computational tools, such as ordinary differential equations, stochastic simulation frameworks and graph theory, and interaction data, from molecular to social granularity levels, to model the ways diseases arise and spread. The coronavirus disease 2019 (COVID-19) is a perfect testbench example to show how these models may help avoid severe lockdown by suggesting, for instance, the best strategies of vaccine prioritization.
    Results: Here, we focus on and discuss some graph-based epidemiological models and show how their use may significantly improve the disease spreading control. We offer some examples related to the recent COVID-19 pandemic and discuss how to generalize them to other diseases.
    MeSH term(s) COVID-19/epidemiology ; Communicable Disease Control ; Computer Simulation ; Humans ; Pandemics ; Surveys and Questionnaires
    Language English
    Publishing date 2022-06-13
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2068142-2
    ISSN 1477-4054 ; 1467-5463
    ISSN (online) 1477-4054
    ISSN 1467-5463
    DOI 10.1093/bib/bbac230
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6262: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  7. Article: Aligning Cross-Species Interactomes for Studying Complex and Chronic Diseases.

    Milano, Marianna / Cinaglia, Pietro / Guzzi, Pietro Hiram / Cannataro, Mario

    Life (Basel, Switzerland)

    2023  Volume 13, Issue 7

    Abstract: Neurodegenerative diseases (NDs) are a group of complex disorders characterized by the progressive degeneration and dysfunction of neurons in the central nervous system. NDs encompass many conditions, including Alzheimer's disease and Parkinson's disease. ...

    Abstract Neurodegenerative diseases (NDs) are a group of complex disorders characterized by the progressive degeneration and dysfunction of neurons in the central nervous system. NDs encompass many conditions, including Alzheimer's disease and Parkinson's disease. Alzheimer's disease (AD) is a complex disease affecting almost forty million people worldwide. AD is characterized by a progressive decline of cognitive functions related to the loss of connections between nerve cells caused by the prevalence of extracellular Aβ plaques and intracellular neurofibrillary tangles plaques. Parkinson's disease (PD) is a neurodegenerative disorder that primarily affects the movement of an individual. The exact cause of Parkinson's disease is not fully understood, but it is believed to involve a combination of genetic and environmental factors. Some cases of PD are linked to mutations in the LRRK2, PARKIN and other genes, which are associated with familial forms of the disease. Different research studies have applied the Protein Protein Interaction (PPI) networks to understand different aspects of disease progression. For instance, Caenorhabditis elegans is widely used as a model organism for the study of AD due to roughly 38% of its genes having a
    Language English
    Publishing date 2023-07-06
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662250-6
    ISSN 2075-1729
    ISSN 2075-1729
    DOI 10.3390/life13071520
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Machine learning pipeline to analyze clinical and proteomics data: experiences on a prostate cancer case.

    Vizza, Patrizia / Aracri, Federica / Guzzi, Pietro Hiram / Gaspari, Marco / Veltri, Pierangelo / Tradigo, Giuseppe

    BMC medical informatics and decision making

    2024  Volume 24, Issue 1, Page(s) 93

    Abstract: Proteomic-based analysis is used to identify biomarkers in blood samples and tissues. Data produced by devices such as mass spectrometry requires platforms to identify and quantify proteins (or peptides). Clinical information can be related to mass ... ...

    Abstract Proteomic-based analysis is used to identify biomarkers in blood samples and tissues. Data produced by devices such as mass spectrometry requires platforms to identify and quantify proteins (or peptides). Clinical information can be related to mass spectrometry data to identify diseases at an early stage. Machine learning techniques can be used to support physicians and biologists in studying and classifying pathologies. We present the application of machine learning techniques to define a pipeline aimed at studying and classifying proteomics data enriched using clinical information. The pipeline allows users to relate established blood biomarkers with clinical parameters and proteomics data. The proposed pipeline entails three main phases: (i) feature selection, (ii) models training, and (iii) models ensembling. We report the experience of applying such a pipeline to prostate-related diseases. Models have been trained on several biological datasets. We report experimental results about two datasets that result from the integration of clinical and mass spectrometry-based data in the contexts of serum and urine analysis. The pipeline receives input data from blood analytes, tissue samples, proteomic analysis, and urine biomarkers. It then trains different models for feature selection, classification and voting. The presented pipeline has been applied on two datasets obtained in a 2 years research project which aimed to extract hidden information from mass spectrometry, serum, and urine samples from hundreds of patients. We report results on analyzing prostate datasets serum with 143 samples, including 79 PCa and 84 BPH patients, and an urine dataset with 121 samples, including 67 PCa and 54 BPH patients. As results pipeline allowed to identify interesting peptides in the two datasets, 6 for the first one and 2 for the second one. The best model for both serum (AUC=0.87, Accuracy=0.83, F1=0.81, Sensitivity=0.84, Specificity=0.81) and urine (AUC=0.88, Accuracy=0.83, F1=0.83, Sensitivity=0.85, Specificity=0.80) datasets showed good predictive performances. We made the pipeline code available on GitHub and we are confident that it will be successfully adopted in similar clinical setups.
    MeSH term(s) Male ; Humans ; Proteomics ; Prostate ; Prostatic Hyperplasia ; Prostatic Neoplasms/diagnosis ; Machine Learning ; Biomarkers ; Peptides
    Chemical Substances Biomarkers ; Peptides
    Language English
    Publishing date 2024-04-08
    Publishing country England
    Document type Journal Article
    ZDB-ID 2046490-3
    ISSN 1472-6947 ; 1472-6947
    ISSN (online) 1472-6947
    ISSN 1472-6947
    DOI 10.1186/s12911-024-02491-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Book ; Online: Using Network Embeddings for Improving Network Alignment

    Guzzi, Pietro Hiram

    2020  

    Abstract: Network (or Graph) Alignment Algorithms aims to reveal structural similarities among graphs. In particular Local Network Alignment Algorithms (LNAs) finds local regions of similarity among two or more networks. Such algorithms are in general based on a ... ...

    Abstract Network (or Graph) Alignment Algorithms aims to reveal structural similarities among graphs. In particular Local Network Alignment Algorithms (LNAs) finds local regions of similarity among two or more networks. Such algorithms are in general based on a set of seed nodes that are used to grow an alignment. Almost all LNAs algorithms use as seed nodes a set of vertices based on context information (e.g. a set of biologically related in biological network alignment) and this may cause a bias or a data-circularity problem. More recently, we demonstrated that the use of topological information in the choice of seed nodes may improve the quality of the alignments. We used some common approaches based on global alignment algorithms for capturing topological similarity among nodes. In parallel, it has been demonstrated that the use of network embedding methods (or representation learning), may capture the structural similarity among nodes better than other methods. Therefore we propose to use network embeddings to learn structural similarity among nodes and to use such similarity to improve LNA extendings our previous algorithms. We define a framework for LNA.
    Keywords Computer Science - Social and Information Networks
    Publishing date 2020-08-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online ; Thesis: Pioneering network shape intelligence for protein-protein interaction prediction via Cannistraci-Hebb network automata theory

    Abdelhamid, Ilyes Verfasser] / [Schroeder, Michael [Gutachter] / Schroeder, Michael [Akademischer Betreuer] / Guzzi, Pietro Hiram [Gutachter]

    2024  

    Author's details Ilyes Abdelhamid ; Gutachter: Michael Schroeder, Pietro Hiram Guzzi ; Betreuer: Michael Schroeder
    Keywords Biowissenschaften, Biologie ; Life Science, Biology
    Subject code sg570
    Language English
    Publisher Technische Universität Dresden
    Publishing place Dresden
    Document type Book ; Online ; Thesis
    Database Digital theses on the web

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