LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 10 of total 85

Search options

  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

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  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

    Full text online

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  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

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

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

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  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

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. 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)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  6. 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

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  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

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  8. Article ; 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  Volume 23, Issue 1, Page(s) 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 term(s) Humans ; Gene Regulatory Networks ; Systems Biology ; Gene Expression Profiling/methods ; Stomach Neoplasms/pathology ; Protein Interaction Maps/genetics ; Gene Expression Regulation, Neoplastic
    Language English
    Publishing date 2023-07-24
    Publishing country England
    Document type 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
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  9. Article ; Online: Design and Implementation of a New Local Alignment Algorithm for Multilayer Networks.

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

    Entropy (Basel, Switzerland)

    2022  Volume 24, Issue 9

    Abstract: Network alignment (NA) is a popular research field that aims to develop algorithms for comparing networks. Applications of network alignment span many fields, from biology to social network analysis. NA comes in two forms: global network alignment (GNA), ...

    Abstract Network alignment (NA) is a popular research field that aims to develop algorithms for comparing networks. Applications of network alignment span many fields, from biology to social network analysis. NA comes in two forms: global network alignment (GNA), which aims to find a global similarity, and LNA, which aims to find local regions of similarity. Recently, there has been an increasing interest in introducing complex network models such as multilayer networks. Multilayer networks are common in many application scenarios, such as modelling of relations among people in a social network or representing the interplay of different molecules in a cell or different cells in the brain. Consequently, the need to introduce algorithms for the comparison of such multilayer networks, i.e., local network alignment, arises. Existing algorithms for LNA do not perform well on multilayer networks since they cannot consider inter-layer edges. Thus, we propose local alignment of multilayer networks (MultiLoAl), a novel algorithm for the local alignment of multilayer networks. We define the local alignment of multilayer networks and propose a heuristic for solving it. We present an extensive assessment indicating the strength of the algorithm. Furthermore, we implemented a synthetic multilayer network generator to build the data for the algorithm's evaluation.
    Language English
    Publishing date 2022-09-09
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e24091272
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  10. Article ; Online: Using dual-network-analyser for communities detecting in dual networks.

    Guzzi, Pietro Hiram / Tradigo, Giuseppe / Veltri, Pierangelo

    BMC bioinformatics

    2022  Volume 22, Issue Suppl 15, Page(s) 614

    Abstract: Background: Representations of the relationships among data using networks are widely used in several research fields such as computational biology, medical informatics and social network mining. Recently, complex networks have been introduced to better ...

    Abstract Background: Representations of the relationships among data using networks are widely used in several research fields such as computational biology, medical informatics and social network mining. Recently, complex networks have been introduced to better capture the insights of the modelled scenarios. Among others, dual networks (DNs) consist of mapping information as pairs of networks containing the same set of nodes but with different edges: one, called physical network, has unweighted edges, while the other, called conceptual network, has weighted edges.
    Results: We focus on DNs and we propose a tool to find common subgraphs (aka communities) in DNs with particular properties. The tool, called Dual-Network-Analyser, is based on the identification of communities that induce optimal modular subgraphs in the conceptual network and connected subgraphs in the physical one. It includes the Louvain algorithm applied to the considered case. The Dual-Network-Analyser can be used to study DNs, to find common modular communities. We report results on using the tool to identify communities on synthetic DNs as well as real cases in social networks and biological data.
    Conclusion: The proposed method has been tested by using synthetic and biological networks. Results demonstrate that it is well able to detect meaningful information from DNs.
    MeSH term(s) Algorithms ; Computational Biology
    Language English
    Publishing date 2022-01-10
    Publishing country England
    Document type Journal Article
    ZDB-ID 2041484-5
    ISSN 1471-2105 ; 1471-2105
    ISSN (online) 1471-2105
    ISSN 1471-2105
    DOI 10.1186/s12859-022-04564-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

To top