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

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

    2023  

    Schlagwörter 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
    Sprache Englisch
    Umfang 1 electronic resource (204 pages)
    Verlag MDPI - Multidisciplinary Digital Publishing Institute
    Erscheinungsort Basel
    Dokumenttyp Buch ; Online
    Anmerkung English
    HBZ-ID HT030645798
    ISBN 9783036592329 ; 3036592326
    Datenquelle ZB MED Katalog Medizin, Gesundheit, Ernährung, Umwelt, Agrar

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

    Guzzi, Pietro Hiram / Roy, Swarup

    trends, approaches, graph theory, and algorithms

    2020  

    Verfasserangabe Pietro Hiram Guzzi, Swarup Roy
    Schlagwörter Electronic books
    Sprache Englisch
    Umfang 1 Online-Ressource (xix, 189 Seiten), Illustrationen, Diagramme
    Verlag Elsevier
    Erscheinungsort Amsterdam
    Erscheinungsland Niederlande
    Dokumenttyp Buch ; Online ; E-Book
    Bemerkung Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    HBZ-ID HT020537530
    ISBN 978-0-12-819351-8 ; 9780128193501 ; 0-12-819351-4 ; 0128193506
    Datenquelle ZB MED Katalog Medizin, Gesundheit, Ernährung, Umwelt, Agrar

    Volltext online

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  3. Buch: Microarray data analysis

    Guzzi, Pietro Hiram

    methods and applications

    (Methods in molecular biology ; 1375 ; Springer protocols)

    2016  

    Verfasserangabe edited by Pietro Hiram Guzzi
    Serientitel Methods in molecular biology ; 1375
    Springer protocols
    Überordnung
    Schlagwörter Non-coding RNA.
    Thema/Rubrik (Code) 572.88
    Sprache Englisch
    Umfang xi, 226 Seiten, Illustrationen, Diagramme, 26 cm
    Ausgabenhinweis Second edition
    Verlag Humana Press
    Erscheinungsort New York
    Erscheinungsland Vereinigte Staaten
    Dokumenttyp Buch
    HBZ-ID HT018909548
    ISBN 978-1-4939-3172-9 ; 978-1-4939-3173-6 ; 1-4939-3172-5 ; 1-4939-3173-3
    Datenquelle Katalog ZB MED Medizin, Gesundheit

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    Verfügbar in ZB MED Köln/Königswinter

    SignaturLeihstatusStandort
    Zs A 7042 -1375- verfügbar in Königswinter Königswinter

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

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

    Frontiers in bioinformatics

    2023  Band 3, Seite(n) 1222711

    Sprache Englisch
    Erscheinungsdatum 2023-06-09
    Erscheinungsland Switzerland
    Dokumenttyp Editorial
    ISSN 2673-7647
    ISSN (online) 2673-7647
    DOI 10.3389/fbinf.2023.1222711
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Artikel ; Online: Editorial

    Swarup Roy / Pietro Hiram Guzzi / Jugal Kalita

    Frontiers in Bioinformatics, Vol

    Graph representation learning in biological network

    2023  Band 3

    Schlagwörter graph ; representation learning ; embedding ; complex network ; regulatory network ; protein network ; Computer applications to medicine. Medical informatics ; R858-859.7
    Sprache Englisch
    Erscheinungsdatum 2023-06-01T00:00:00Z
    Verlag Frontiers Media S.A.
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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

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

    Briefings in bioinformatics

    2022  Band 23, Heft 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-Begriff(e) COVID-19/epidemiology ; Communicable Disease Control ; Computer Simulation ; Humans ; Pandemics ; Surveys and Questionnaires
    Sprache Englisch
    Erscheinungsdatum 2022-06-13
    Erscheinungsland England
    Dokumenttyp 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
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Verfügbar in ZB MED Köln/Königswinter

    Zs.A 6262: Hefte anzeigen Standort:
    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. Artikel: Aligning Cross-Species Interactomes for Studying Complex and Chronic Diseases.

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

    Life (Basel, Switzerland)

    2023  Band 13, Heft 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
    Sprache Englisch
    Erscheinungsdatum 2023-07-06
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2662250-6
    ISSN 2075-1729
    ISSN 2075-1729
    DOI 10.3390/life13071520
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  8. Artikel ; 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  Band 24, Heft 1, Seite(n) 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-Begriff(e) Male ; Humans ; Proteomics ; Prostate ; Prostatic Hyperplasia ; Prostatic Neoplasms/diagnosis ; Machine Learning ; Biomarkers ; Peptides
    Chemische Substanzen Biomarkers ; Peptides
    Sprache Englisch
    Erscheinungsdatum 2024-04-08
    Erscheinungsland England
    Dokumenttyp 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
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  9. Artikel ; Online: A systems biology approach to pathogenesis of gastric cancer: gene network modeling and pathway analysis.

    Mottaghi-Dastjerdi, Negar / Ghorbani, Abozar / Montazeri, Hamed / Guzzi, Pietro Hiram

    BMC gastroenterology

    2023  Band 23, Heft 1, Seite(n) 248

    Abstract: Background: Gastric cancer (GC) ranks among the most common malignancies worldwide. This study aimed to find critical genes/pathways in GC pathogenesis.: Methods: Gene interactions were analyzed, and the protein-protein interaction network was drawn. ...

    Abstract Background: Gastric cancer (GC) ranks among the most common malignancies worldwide. This study aimed to find critical genes/pathways in GC pathogenesis.
    Methods: Gene interactions were analyzed, and the protein-protein interaction network was drawn. Then enrichment analysis of the hub genes was performed and network cluster analysis and promoter analysis of the hub genes were done. Age/sex analysis was done on the identified genes.
    Results: Eleven hub genes in GC were identified in the current study (ATP5A1, ATP5B, ATP5D, MT-ATP8, COX7A2, COX6C, ND4, ND6, NDUFS3, RPL8, and RPS16), mostly involved in mitochondrial functions. There was no report on the ATP5D, ND6, NDUFS3, RPL8, and RPS16 in GC. Our results showed that the most affected processes in GC are the metabolic processes, and the oxidative phosphorylation pathway was considerably enriched which showed the significance of mitochondria in GC pathogenesis. Most of the affected pathways in GC were also involved in neurodegenerative diseases. Promoter analysis showed that negative regulation of signal transduction might play an important role in GC pathogenesis. In the analysis of the basal expression pattern of the selected genes whose basal expression presented a change during the age, we found that a change in age may be an indicator of changes in disease insurgence and/or progression at different ages.
    Conclusions: These results might open up new insights into GC pathogenesis. The identified genes might be novel diagnostic/prognostic biomarkers or potential therapeutic targets for GC. This work, being based on bioinformatics analysis act as a hypothesis generator that requires further clinical validation.
    Mesh-Begriff(e) Humans ; Gene Regulatory Networks ; Systems Biology ; Gene Expression Profiling/methods ; Stomach Neoplasms/pathology ; Protein Interaction Maps/genetics ; Gene Expression Regulation, Neoplastic
    Sprache Englisch
    Erscheinungsdatum 2023-07-24
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2041351-8
    ISSN 1471-230X ; 1471-230X
    ISSN (online) 1471-230X
    ISSN 1471-230X
    DOI 10.1186/s12876-023-02891-4
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  10. Buch ; 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.
    Schlagwörter Computer Science - Social and Information Networks
    Erscheinungsdatum 2020-08-11
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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