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  1. Article ; Online: Data-driven recombination detection in viral genomes.

    Alfonsi, Tommaso / Bernasconi, Anna / Chiara, Matteo / Ceri, Stefano

    Nature communications

    2024  Volume 15, Issue 1, Page(s) 3313

    Abstract: Recombination is a key molecular mechanism for the evolution and adaptation of viruses. The first recombinant SARS-CoV-2 genomes were recognized in 2021; as of today, more than ninety SARS-CoV-2 lineages are designated as recombinant. In the wake of the ... ...

    Abstract Recombination is a key molecular mechanism for the evolution and adaptation of viruses. The first recombinant SARS-CoV-2 genomes were recognized in 2021; as of today, more than ninety SARS-CoV-2 lineages are designated as recombinant. In the wake of the COVID-19 pandemic, several methods for detecting recombination in SARS-CoV-2 have been proposed; however, none could faithfully confirm manual analyses by experts in the field. We hereby present RecombinHunt, an original data-driven method for the identification of recombinant genomes, capable of recognizing recombinant SARS-CoV-2 genomes (or lineages) with one or two breakpoints with high accuracy and within reduced turn-around times. ReconbinHunt shows high specificity and sensitivity, compares favorably with other state-of-the-art methods, and faithfully confirms manual analyses by experts. RecombinHunt identifies recombinant viral genomes from the recent monkeypox epidemic in high concordance with manually curated analyses by experts, suggesting that our approach is robust and can be applied to any epidemic/pandemic virus.
    MeSH term(s) Humans ; Pandemics ; COVID-19 ; SARS-CoV-2 ; Genome, Viral ; Recombination, Genetic ; Phylogeny
    Language English
    Publishing date 2024-04-17
    Publishing country England
    Document type Journal Article
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-024-47464-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Predicting Drug Synergism by Means of Non-Negative Matrix Tri-Factorization.

    Pinoli, Pietro / Ceddia, Gaia / Ceri, Stefano / Masseroli, Marco

    IEEE/ACM transactions on computational biology and bioinformatics

    2022  Volume 19, Issue 4, Page(s) 1956–1967

    Abstract: Traditional drug experiments to find synergistic drug pairs are time-consuming and expensive due to the numerous possible combinations of drugs that have to be examined. Thus, computational methods that can give suggestions for synergistic drug ... ...

    Abstract Traditional drug experiments to find synergistic drug pairs are time-consuming and expensive due to the numerous possible combinations of drugs that have to be examined. Thus, computational methods that can give suggestions for synergistic drug investigations are of great interest. Here, we propose a Non-negative Matrix Tri-Factorization (NMTF) based approach that leverages the integration of different data types for predicting synergistic drug pairs in multiple specific cell lines. Our computational framework relies on a network-based representation of available data about drug synergism, which also allows integrating genomic information about cell lines. We computationally evaluate the performances of our method in finding missing relationships between synergistic drug pairs and cell lines, and in computing synergy scores between drug pairs in a specific cell line, as well as we estimate the benefit of adding cell line genomic data to the network. Our approach obtains very good performance (Average Precision Score equal to 0.937, Pearson's correlation coefficient equal to 0.760) when cell line genomic data and rich data about synergistic drugs in a cell line are considered. Finally, we systematically searched our top-scored predictions in the available literature and in the NCI ALMANAC, a well-known database of drug combination experiments, proving the goodness of our findings.
    MeSH term(s) Algorithms ; Computational Biology/methods ; Databases, Factual ; Drug Synergism ; Genomics
    Language English
    Publishing date 2022-08-08
    Publishing country United States
    Document type Journal Article ; Systematic Review ; Research Support, Non-U.S. Gov't
    ISSN 1557-9964
    ISSN (online) 1557-9964
    DOI 10.1109/TCBB.2021.3091814
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: META-BASE: A Novel Architecture for Large-Scale Genomic Metadata Integration.

    Bernasconi, Anna / Canakoglu, Arif / Masseroli, Marco / Ceri, Stefano

    IEEE/ACM transactions on computational biology and bioinformatics

    2022  Volume 19, Issue 1, Page(s) 543–557

    Abstract: The integration of genomic metadata is, at the same time, an important, difficult, and well-recognized challenge. It is important because a wealth of public data repositories is available to drive biological and clinical research; combining information ... ...

    Abstract The integration of genomic metadata is, at the same time, an important, difficult, and well-recognized challenge. It is important because a wealth of public data repositories is available to drive biological and clinical research; combining information from various heterogeneous and widely dispersed sources is paramount to a number of biological discoveries. It is difficult because the domain is complex and there is no agreement among the various metadata definitions, which refer to different vocabularies and ontologies. It is well-recognized in the bioinformatics community because, in the common practice, repositories are accessed one-by-one, learning their specific metadata definitions as result of long and tedious efforts, and such practice is error-prone. In this paper, we describe META-BASE, an architecture for integrating metadata extracted from a variety of genomic data sources, based upon a structured transformation process. We present a variety of innovative techniques for data extraction, cleaning, normalization and enrichment. We propose a general, open and extensible pipeline that can easily incorporate any number of new data sources, and propose the resulting repository-already integrating several important sources-which is exposed by means of practical user interfaces to respond biological researchers' needs.
    MeSH term(s) Computational Biology ; Genomics ; Information Storage and Retrieval ; Metadata
    Language English
    Publishing date 2022-02-03
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1557-9964
    ISSN (online) 1557-9964
    DOI 10.1109/TCBB.2020.2998954
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: ITA-ELECTION-2022

    Pierri, Francesco / Liu, Geng / Ceri, Stefano

    A multi-platform dataset of social media conversations around the 2022 Italian general election

    2023  

    Abstract: Online social media play a major role in shaping public discourse and opinion, especially during political events. We present the first public multi-platform dataset of Italian-language political conversations, focused on the 2022 Italian general ... ...

    Abstract Online social media play a major role in shaping public discourse and opinion, especially during political events. We present the first public multi-platform dataset of Italian-language political conversations, focused on the 2022 Italian general election taking place on September 25th. Leveraging public APIs and a keyword-based search, we collected millions of posts published by users, pages and groups on Facebook, Instagram and Twitter, along with metadata of TikTok and YouTube videos shared on these platforms, over a period of four months. We augmented the dataset with a collection of political ads sponsored on Meta platforms, and a list of social media handles associated with political representatives. Our data resource will allow researchers and academics to further our understanding of the role of social media in the democratic process.

    Comment: 4 pages, 3 figures, 2 tables
    Keywords Computer Science - Social and Information Networks
    Subject code 020
    Publishing date 2023-01-12
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Data-driven recombination detection in viral genomes

    Alfonsi, Tommaso / Bernasconi, Anna / Chiara, Matteo / Ceri, Stefano

    bioRxiv

    Abstract: Recombination is a key molecular mechanism for the evolution and adaptation of viruses. The first recombinant SARS-CoV-2 genomes were recognized in 2021; as of today, more than seventy SARS-CoV-2 lineages are designated as recombinant. In the wake of the ...

    Abstract Recombination is a key molecular mechanism for the evolution and adaptation of viruses. The first recombinant SARS-CoV-2 genomes were recognized in 2021; as of today, more than seventy SARS-CoV-2 lineages are designated as recombinant. In the wake of the COVID-19 pandemic, several methods for detecting recombination in SARS-CoV-2 have been proposed; however, none could faithfully reproduce manual analyses by experts in the field. We hereby present RecombinHunt, a novel, automated method for the identification of recombinant genomes purely based on a data-driven approach. RecombinHunt compares favorably with other state-of-the-art methods and recognizes recombinant SARS-CoV-2 genomes (or lineages) with one or two breakpoints with high accuracy, within reduced turn-around times and small discrepancies with respect to the expert manually-curated standard nomenclature. Strikingly, applied to the complete collection of viral sequences from the recent monkeypox epidemic, RecombinHunt identifies recombinant viral genomes in high concordance with manually curated analyses by experts, suggesting that our approach is robust and can be applied to any epidemic/pandemic virus. Although RecombinHunt does not substitute manual expert curation based on phylogenetic analysis, we believe that our method represents a breakthrough for the detection of recombinant viral lineages in pandemic/epidemic scenarios.
    Keywords covid19
    Language English
    Publishing date 2023-06-07
    Publisher Cold Spring Harbor Laboratory
    Document type Article ; Online
    DOI 10.1101/2023.06.05.543733
    Database COVID19

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  6. Book ; Online: Exploring the evolution of research topics during the COVID-19 pandemic

    Invernici, Francesco / Bernasconi, Anna / Ceri, Stefano

    2023  

    Abstract: The COVID-19 pandemic has changed the research agendas of most scientific communities, resulting in an overwhelming production of research articles in a variety of domains, including medicine, virology, epidemiology, economy, psychology, and so on. ... ...

    Abstract The COVID-19 pandemic has changed the research agendas of most scientific communities, resulting in an overwhelming production of research articles in a variety of domains, including medicine, virology, epidemiology, economy, psychology, and so on. Several open-access corpora and literature hubs were established; among them, the COVID-19 Open Research Dataset (CORD-19) has systematically gathered scientific contributions for 2.5 years, by collecting and indexing over one million articles. Here, we present the CORD-19 Topic Visualizer (CORToViz), a method and associated visualization tool for inspecting the CORD-19 textual corpus of scientific abstracts. Our method is based upon a careful selection of up-to-date technologies (including large language models), resulting in an architecture for clustering articles along orthogonal dimensions and extraction techniques for temporal topic mining. Topic inspection is supported by an interactive dashboard, providing fast, one-click visualization of topic contents as word clouds and topic trends as time series, equipped with easy-to-drive statistical testing for analyzing the significance of topic emergence along arbitrarily selected time windows. The processes of data preparation and results visualization are completely general and virtually applicable to any corpus of textual documents - thus suited for effective adaptation to other contexts.

    Comment: 16 pages, 6 figures, 1 table
    Keywords Computer Science - Computation and Language ; Computer Science - Social and Information Networks
    Subject code 306
    Publishing date 2023-10-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Searching COVID-19 clinical research using graphical abstracts

    Invernici, Francesco / Bernasconi, Anna / Ceri, Stefano

    2023  

    Abstract: Objective. Graphical abstracts are small graphs of concepts that visually summarize the main findings of scientific articles. While graphical abstracts are customarily used in scientific publications to anticipate and summarize their main results, we ... ...

    Abstract Objective. Graphical abstracts are small graphs of concepts that visually summarize the main findings of scientific articles. While graphical abstracts are customarily used in scientific publications to anticipate and summarize their main results, we propose them as a means for expressing graph searches over existing literature. Materials and methods. We consider the COVID-19 Open Research Dataset (CORD-19), a corpus of more than one million abstracts; each of them is described as a graph of co-occurring ontological terms, selected from the Unified Medical Language System (UMLS) and the Ontology of Coronavirus Infectious Disease (CIDO). Graphical abstracts are also expressed as graphs of ontological terms, possibly augmented by utility terms describing their interactions (e.g., "associated with", "increases", "induces"). We build a co-occurrence network of concepts mentioned in the corpus; we then identify the best matches of graphical abstracts on the network. We exploit graph database technology and shortest-path queries. Results. We build a large co-occurrence network, consisting of 128,249 entities and 47,198,965 relationships. A well-designed interface allows users to explore the network by formulating or adapting queries in the form of an abstract; it produces a bibliography of publications, globally ranked; each publication is further associated with the specific parts of the abstract that it explains, thereby allowing the user to understand each aspect of the matching. Discussion and Conclusion. Our approach supports the process of scientific hypothesis formulation and evidence search; it can be reapplied to any scientific domain, although our mastering of UMLS makes it most suited to clinical domains.

    Comment: 12 pages, 6 figures
    Keywords Computer Science - Information Retrieval ; Computer Science - Digital Libraries
    Publishing date 2023-10-06
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: PG-Triggers

    Gagliardi, Alessia / Bernasconi, Anna / Martinenghi, Davide / Ceri, Stefano

    Triggers for Property Graphs

    2023  

    Abstract: Graph databases are emerging as the leading data management technology for storing large knowledge graphs; significant efforts are ongoing to produce new standards (such as the Graph Query Language, GQL), as well as enrich them with properties, types, ... ...

    Abstract Graph databases are emerging as the leading data management technology for storing large knowledge graphs; significant efforts are ongoing to produce new standards (such as the Graph Query Language, GQL), as well as enrich them with properties, types, schemas, and keys. In this article, we propose PG-Triggers, a complete proposal for adding triggers to Property Graphs, along the direction marked by the SQL3 Standard. We define the syntax and semantics of PG-Triggers and then illustrate how they can be implemented on top of Neo4j, one of the most popular graph databases. In particular, we introduce a syntax-directed translation from PG-Triggers into Neo4j, which makes use of the so-called APOC triggers; APOC is a community-contributed library for augmenting the Cypher query language supported by Neo4j. We also illustrate the use of PG-Triggers through a life science application inspired by the COVID-19 pandemic. The main result of this article is proposing reactive aspects within graph databases as first-class citizens, so as to turn them into an ideal infrastructure for supporting reactive knowledge management.

    Comment: 12 pages, 4 figures, 3 tables
    Keywords Computer Science - Databases ; cs.DB
    Subject code 004
    Publishing date 2023-07-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book: Web information retrieval

    Ceri, Stefano

    (Data-centric systems and applications)

    2013  

    Author's details Stefano Ceri
    Series title Data-centric systems and applications
    Keywords Multimedia ; Suchverfahren ; Information Retrieval ; Internet ; Abfrageverarbeitung ; Suchmaschine
    Language English
    Size XIV, 284 S., Ill., graph. Darst., 235 mm x 155 mm
    Publisher Springer
    Publishing place Berlin u.a.
    Document type Book
    Note Literaturverz. S. 259 - 276
    ISBN 3642393136 ; 9783642393136 ; 9783642393143 ; 3642393144
    Database Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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  10. Book: Creating innovation leaders

    Banerjee, Banny / Ceri, Stefano

    a global perspective

    (Understanding innovation)

    2016  

    Author's details Banny Banerjee ; Stefano Ceri editors
    Series title Understanding innovation
    Keywords Führungskräfteentwicklung ; Innovation ; Sozialtechnologie ; Betriebswirtschaftsstudium ; Welt
    Language English
    Size XIII, 277 Seiten, Illustrationen, Diagramme, 235 mm x 155 mm, 0 g
    Publisher Springer
    Publishing place Cham ; Heidelberg ; New York ; Dordrecht ; London
    Document type Book
    Note Enth. 17 Beitr.
    ISBN 9783319205199 ; 9783319205205 ; 3319205196 ; 331920520X
    Database ECONomics Information System

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