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  1. Book: Computational Intelligence Methods for Bioinformatics and Biostatistics

    Chicco, Davide / Facchiano, Angelo / Tavazzi, Erica / Cazzaniga, Paolo / Vettoretti, Martina / Bernasconi, Anna / Avesani, Simone / Longato, Enrico

    17th International Meeting, CIBB 2021, Virtual Event, November 15¿17, 2021, Revised Selected Papers

    (Lecture Notes in Bioinformatics)

    2022  

    Series title Lecture Notes in Bioinformatics
    Keywords artificial intelligence ; computational and systems biology ; computer networks ; Computer Systems ; Computer vision ; Correlation Analysis ; Data Mining ; education ; Image Analysis ; image processing ; Image segmentation ; Learning ; Machine Learning ; Neural Networks ; pattern recognition ; Signal Processing ; biostatistics ; computer systems ; computer vision ; correlation analysis ; data mining ; image analysis ; image segmentation ; learning ; machine learning ; neural networks ; signal processing
    Language English
    Size 272 p.
    Edition 1
    Publisher Springer International Publishing
    Document type Book
    Note PDA Manuell_17
    Format 155 x 235 x 15
    ISBN 9783031208362 ; 3031208366
    Database PDA

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  2. Article ; Online: Data quality-aware genomic data integration

    Anna Bernasconi

    Computer Methods and Programs in Biomedicine Update, Vol 1, Iss , Pp 100009- (2021)

    2021  

    Abstract: Genomic data are growing at unprecedented pace, along with new protocols, update polices, formats and guidelines, terminologies and ontologies, which are made available every day by data providers. In this continuously evolving universe, enforcing ... ...

    Abstract Genomic data are growing at unprecedented pace, along with new protocols, update polices, formats and guidelines, terminologies and ontologies, which are made available every day by data providers. In this continuously evolving universe, enforcing quality on data and metadata is increasingly critical. While many aspects of data quality are addressed at each individual source, we focus on the need for a systematic approach when data from several sources are integrated, as such integration is an essential aspect for modern genomic data analysis. Data quality must be assessed from many perspectives, including accessibility, currency, representational consistency, specificity, and reliability.In this article we review relevant literature and, based on the analysis of many datasets and platforms, we report on methods used for guaranteeing data quality while integrating heterogeneous data sources. We explore several real-world cases that are exemplary of more general underlying data quality problems and we illustrate how they can be resolved with a structured method, sensibly applicable also to other biomedical domains. The overviewed methods are implemented in a large framework for the integration of processed genomic data, which is made available to the research community for supporting tertiary data analysis over Next Generation Sequencing datasets, continuously loaded from many open data sources, bringing considerable added value to biological knowledge discovery.
    Keywords Data quality ; Data integration ; Data curation ; Genomic datasets ; Metadata ; Interoperability ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 004
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. 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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  4. Article ; Online: Editorial: Identification of phenotypically important genomic variants.

    Heron, Elizabeth A / Valle, Giorgio / Bernasconi, Anna

    Frontiers in bioinformatics

    2023  Volume 3, Page(s) 1328945

    Language English
    Publishing date 2023-11-10
    Publishing country Switzerland
    Document type Editorial
    ISSN 2673-7647
    ISSN (online) 2673-7647
    DOI 10.3389/fbinf.2023.1328945
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  5. Article ; Online: Correction: Processing genome-wide association studies within a repository of heterogeneous genomic datasets.

    Bernasconi, Anna / Canakoglu, Arif / Comolli, Federico

    BMC genomic data

    2023  Volume 24, Issue Suppl 1, Page(s) 32

    Language English
    Publishing date 2023-06-05
    Publishing country England
    Document type Published Erratum
    ISSN 2730-6844
    ISSN (online) 2730-6844
    DOI 10.1186/s12863-023-01135-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Processing genome-wide association studies within a repository of heterogeneous genomic datasets.

    Bernasconi, Anna / Canakoglu, Arif / Comolli, Federico

    BMC genomic data

    2023  Volume 24, Issue 1, Page(s) 13

    Abstract: Background: Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants - typically single-nucleotide polymorphisms (SNPs) - in different individuals that are associated with phenotypic traits. Research ... ...

    Abstract Background: Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants - typically single-nucleotide polymorphisms (SNPs) - in different individuals that are associated with phenotypic traits. Research efforts have so far been directed to improving GWAS techniques rather than on making the results of GWAS interoperable with other genomic signals; this is currently hindered by the use of heterogeneous formats and uncoordinated experiment descriptions.
    Results: To practically facilitate integrative use, we propose to include GWAS datasets within the META-BASE repository, exploiting an integration pipeline previously studied for other genomic datasets that includes several heterogeneous data types in the same format, queryable from the same systems. We represent GWAS SNPs and metadata by means of the Genomic Data Model and include metadata within a relational representation by extending the Genomic Conceptual Model with a dedicated view. To further reduce the gap with the descriptions of other signals in the repository of genomic datasets, we perform a semantic annotation of phenotypic traits. Our pipeline is demonstrated using two important data sources, initially organized according to different data models: the NHGRI-EBI GWAS Catalog and FinnGen (University of Helsinki). The integration effort finally allows us to use these datasets within multi-sample processing queries that respond to important biological questions. These are then made usable for multi-omic studies together with, e.g., somatic and reference mutation data, genomic annotations, epigenetic signals.
    Conclusions: As a result of the our work on GWAS datasets, we enable 1) their interoperable use with several other homogenized and processed genomic datasets in the context of the META-BASE repository; 2) their big data processing by means of the GenoMetric Query Language and associated system. Future large-scale tertiary data analysis may extensively benefit from the addition of GWAS results to inform several different downstream analysis workflows.
    MeSH term(s) Humans ; Genome-Wide Association Study ; Genomics ; Epigenomics ; Big Data ; Data Analysis
    Language English
    Publishing date 2023-03-03
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2730-6844
    ISSN (online) 2730-6844
    DOI 10.1186/s12863-023-01111-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Processing genome-wide association studies within a repository of heterogeneous genomic datasets

    Bernasconi, Anna / Canakoglu, Arif / Comolli, Federico

    BMC Genom Data. 2023 Dec., v. 24, no. 1 p.13-13

    2023  

    Abstract: BACKGROUND: Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants – typically single-nucleotide polymorphisms (SNPs) – in different individuals that are associated with phenotypic traits. Research ... ...

    Abstract BACKGROUND: Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants – typically single-nucleotide polymorphisms (SNPs) – in different individuals that are associated with phenotypic traits. Research efforts have so far been directed to improving GWAS techniques rather than on making the results of GWAS interoperable with other genomic signals; this is currently hindered by the use of heterogeneous formats and uncoordinated experiment descriptions. RESULTS: To practically facilitate integrative use, we propose to include GWAS datasets within the META-BASE repository, exploiting an integration pipeline previously studied for other genomic datasets that includes several heterogeneous data types in the same format, queryable from the same systems. We represent GWAS SNPs and metadata by means of the Genomic Data Model and include metadata within a relational representation by extending the Genomic Conceptual Model with a dedicated view. To further reduce the gap with the descriptions of other signals in the repository of genomic datasets, we perform a semantic annotation of phenotypic traits. Our pipeline is demonstrated using two important data sources, initially organized according to different data models: the NHGRI-EBI GWAS Catalog and FinnGen (University of Helsinki). The integration effort finally allows us to use these datasets within multi-sample processing queries that respond to important biological questions. These are then made usable for multi-omic studies together with, e.g., somatic and reference mutation data, genomic annotations, epigenetic signals. CONCLUSIONS: As a result of the our work on GWAS datasets, we enable 1) their interoperable use with several other homogenized and processed genomic datasets in the context of the META-BASE repository; 2) their big data processing by means of the GenoMetric Query Language and associated system. Future large-scale tertiary data analysis may extensively benefit from the addition of GWAS results to inform several different downstream analysis workflows.
    Keywords data collection ; epigenetics ; genomics ; metadata ; models ; mutation ; phenotype
    Language English
    Dates of publication 2023-12
    Size p. 13.
    Publishing place BioMed Central
    Document type Article ; Online
    ISSN 2730-6844
    DOI 10.1186/s12863-023-01111-y
    Database NAL-Catalogue (AGRICOLA)

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  8. Article ; Online: Editorial

    Elizabeth A. Heron / Giorgio Valle / Anna Bernasconi

    Frontiers in Bioinformatics, Vol

    Identification of phenotypically important genomic variants

    2023  Volume 3

    Keywords next-generation sequencing (NGS) ; genomic variant ; prioritisation ; interpretation ; annotation ; Computer applications to medicine. Medical informatics ; R858-859.7
    Language English
    Publishing date 2023-11-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Scenarios for the Integration of Microarray Gene Expression Profiles in COVID-19-Related Studies.

    Bernasconi, Anna / Cascianelli, Silvia

    Methods in molecular biology (Clifton, N.J.)

    2021  Volume 2401, Page(s) 195–215

    Abstract: The COVID-19 pandemic has hit heavily many aspects of our lives. At this time, genomic research is concerned with exploiting available datasets and knowledge to fuel discovery on this novel disease. Studies that can precisely characterize the gene ... ...

    Abstract The COVID-19 pandemic has hit heavily many aspects of our lives. At this time, genomic research is concerned with exploiting available datasets and knowledge to fuel discovery on this novel disease. Studies that can precisely characterize the gene expression profiles of human hosts infected by SARS-CoV-2 are of significant relevance. However, not many such experiments have yet been produced to date, nor made publicly available online. Thus, it is of paramount importance that data analysts explore all possibilities to integrate information coming from similar viruses and related diseases; interestingly, microarray gene profile experiments become extremely valuable for this purpose. This chapter reviews the aspects that should be considered when integrating transcriptomics data, considering mainly samples infected by different viruses and combining together various data types and also the extracted knowledge. It describes a series of scenarios from studies performed in literature and it suggests possible other directions of noteworthy integration.
    MeSH term(s) COVID-19/genetics ; Gene Expression Profiling ; Genomics ; Humans ; Pandemics ; Transcriptome
    Language English
    Publishing date 2021-12-13
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-0716-1839-4_13
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: VirusLab: A Tool for Customized SARS-CoV-2 Data Analysis.

    Pinoli, Pietro / Bernasconi, Anna / Sandionigi, Anna / Ceri, Stefano

    Biotech (Basel (Switzerland))

    2021  Volume 10, Issue 4

    Abstract: Since the beginning of 2020, the COVID-19 pandemic has posed unprecedented challenges to viral data analysis and connected host disease diagnostic methods. We propose VirusLab, a flexible system for analysing SARS-CoV-2 viral sequences and relating them ... ...

    Abstract Since the beginning of 2020, the COVID-19 pandemic has posed unprecedented challenges to viral data analysis and connected host disease diagnostic methods. We propose VirusLab, a flexible system for analysing SARS-CoV-2 viral sequences and relating them to metadata or clinical information about the host. VirusLab capitalizes on two existing resources: ViruSurf, a database of public SARS-CoV-2 sequences supporting metadata-driven search, and VirusViz, a tool for visual analysis of search results. VirusLab is designed for taking advantage of these resources within a server-side architecture that: (i) covers pipelines based on approaches already in use (ARTIC, Galaxy) but entirely cutomizable upon user request; (ii) predigests analysis of raw sequencing data from different platforms (Oxford Nanopore and Illumina); (iii) gives access to public archives datasets; (iv) supplies user-friendly reporting - making it a tool that can also be integrated into a business environment. VirusLab can be installed and hosted within the premises of any organization where information about SARS-CoV-2 sequences can be safely integrated with information about hosts (e.g., clinical metadata). A system such as VirusLab is not currently available in the landscape of similar providers: our results show that VirusLab is a powerful tool to generate tabular/graphical and machine readable reports that can be integrated in more complex pipelines. We foresee that the proposed system can support many research-oriented and therapeutic scenarios within hospitals or the tracing of viral sequences and their mutational processes within organizations for viral surveillance.
    Language English
    Publishing date 2021-11-06
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2673-6284
    ISSN (online) 2673-6284
    DOI 10.3390/biotech10040027
    Database MEDical Literature Analysis and Retrieval System OnLINE

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