LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 10 of total 54

Search options

  1. Book: Microarray data analysis

    Agapito, Giuseppe

    (Methods in molecular biology ; 2401 ; Springer protocols)

    2022  

    Author's details edited by Giuseppe Agapito
    Series title Methods in molecular biology ; 2401
    Springer protocols
    Collection
    Keywords DNA microarrays/Data processing
    Subject code 572.8636
    Language English
    Size xi, 317 Seiten, Illustrationen, Diagramme, 26 cm
    Publisher Humana Press
    Publishing place New York, NY
    Publishing country United States
    Document type Book
    HBZ-ID HT021186020
    ISBN 978-1-0716-1838-7 ; 9781071618394 ; 1-0716-1838-5 ; 1071618393
    Database Catalogue ZB MED Medicine, Health

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    Zs A 7042 -2401- not available for loan Lesesaal EG

    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.

  2. Article ; Online: Computer Tools to Analyze Microarray Data.

    Agapito, Giuseppe

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

    2019  Volume 1986, Page(s) 267–282

    Abstract: Microarrays are broadly used in genomic analyses and find several applications in biology and medicine, providing a significant amount of data from a single experiment. Different kinds of microarrays are available which are identifiable by ... ...

    Abstract Microarrays are broadly used in genomic analyses and find several applications in biology and medicine, providing a significant amount of data from a single experiment. Different kinds of microarrays are available which are identifiable by characteristics such as the type of probes, the surface used as support, and the method used for target detection. Although microarrays have been applied in many biological areas, their management, and investigation require advanced computational tools to speed up data analysis and at the same time make the interpretation of the results easier. To better deal with microarray datasets of large size, the development of analysis tools that are simple to use as well as able to produce accurate predictions, and of comprehensible models is essential. The tools have to provide an exhaustive collection of information to discriminate and identify SNPs, which are associated with the activity of particular genes affecting biological functions (e.g., a particular drug response), or involved in the development of complex diseases. The object of this chapter is to provide a review of software tools that are easy to use even for nonexperts of the domain, and that are able to efficiently deal with microarray data.
    MeSH term(s) Data Analysis ; Humans ; Oligonucleotide Array Sequence Analysis/methods ; Software
    Language English
    Publishing date 2019-05-21
    Publishing country United States
    Document type Journal Article ; Review
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-4939-9442-7_13
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text 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.

  3. Article ; Online: Bioinformatics and High-Performance Computing Methods for Deciphering and Fighting COVID-19-Editorial.

    Cannataro, Mario / Agapito, Giuseppe

    Biotech (Basel (Switzerland))

    2022  Volume 11, Issue 4

    Abstract: The COVID-19 disease (Coronavirus Disease 19), caused by the SARS-CoV-2 virus (Severe Acute Respiratory Syndrome Coronavirus 2), has posed many challenges worldwide at various levels, with special focus to the biological, medical, and epidemiological ... ...

    Abstract The COVID-19 disease (Coronavirus Disease 19), caused by the SARS-CoV-2 virus (Severe Acute Respiratory Syndrome Coronavirus 2), has posed many challenges worldwide at various levels, with special focus to the biological, medical, and epidemiological ones [...].
    Language English
    Publishing date 2022-10-15
    Publishing country Switzerland
    Document type Editorial
    ISSN 2673-6284
    ISSN (online) 2673-6284
    DOI 10.3390/biotech11040047
    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.

  4. Article ; Online: Clustering Methods for Microarray Data Sets.

    Agapito, Giuseppe / Fedele, Giuseppe

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

    2021  Volume 2401, Page(s) 249–261

    Abstract: Microarrays are experimental methods that can provide information about gene expression and SNP data that hold great potential for new understanding, driving advances in functional genomics and clinical and molecular biology. Cluster analysis is used to ... ...

    Abstract Microarrays are experimental methods that can provide information about gene expression and SNP data that hold great potential for new understanding, driving advances in functional genomics and clinical and molecular biology. Cluster analysis is used to analyze data that are not a priori to contain any specific subgroup. The goal is to use the data itself to recognize meaningful and informative subgroups. Also, cluster analysis helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. This chapter outlines a collection of cluster methods suitable for the analysis of microarray data sets.
    MeSH term(s) Algorithms ; Cluster Analysis ; Gene Expression ; Gene Expression Profiling ; Genomics ; Microarray Analysis ; Oligonucleotide Array Sequence Analysis
    Language English
    Publishing date 2021-12-13
    Publishing country United States
    Document type Journal Article
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-0716-1839-4_16
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text 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. Article ; Online: SEDEG

    Giuseppe Agapito / Marianna Milano / Pietro Cinaglia / Mario Cannataro

    Informatics in Medicine Unlocked, Vol 44, Iss , Pp 101432- (2024)

    An automatic method for preprocessing and selection of seed genes from gene expression data

    1481  

    Abstract: Select Essential Differential Expressed Genes (SEDEG) is a software pipeline designed to simplify the time-consuming and error-prone task of preparing Differential Expressed Genes (DEGs) for Pathway Enrichment Analysis (PEA). It automatically ... ...

    Abstract Select Essential Differential Expressed Genes (SEDEG) is a software pipeline designed to simplify the time-consuming and error-prone task of preparing Differential Expressed Genes (DEGs) for Pathway Enrichment Analysis (PEA). It automatically preprocesses, filters, and selects DEGs, making interpreting results of gene expression microarrays and Genome-Wide Association Studies easier.SEDEG is a Python tool that automates multiple different actions simultaneously, saving researchers significant time and effort. It identifies crucial DEGs and enriched pathways related to the condition being investigated.The SEDEG pipeline is a tool that enhances the consolidation process of DEGs, which is essential for computing PEA. It achieves this by automating several manual steps, resulting in more accurate lists of DEGs for PEA analysis. This automation also improves the relevance and significance of enriched pathways. To download SEDEG, please visit https://gitlab.com/giuseppeagapito/sedeg.
    Keywords Differential Expressed Gene ; Pathway enrichment ; Micorarray ; Gene expression ; Parallel computing ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 004
    Language English
    Publishing date 2024-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; 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: Nine quick tips for pathway enrichment analysis.

    Chicco, Davide / Agapito, Giuseppe

    PLoS computational biology

    2022  Volume 18, Issue 8, Page(s) e1010348

    Abstract: Pathway enrichment analysis (PEA) is a computational biology method that identifies biological functions that are overrepresented in a group of genes more than would be expected by chance and ranks these functions by relevance. The relative abundance of ... ...

    Abstract Pathway enrichment analysis (PEA) is a computational biology method that identifies biological functions that are overrepresented in a group of genes more than would be expected by chance and ranks these functions by relevance. The relative abundance of genes pertinent to specific pathways is measured through statistical methods, and associated functional pathways are retrieved from online bioinformatics databases. In the last decade, along with the spread of the internet, higher availability of computational resources made PEA software tools easy to access and to use for bioinformatics practitioners worldwide. Although it became easier to use these tools, it also became easier to make mistakes that could generate inflated or misleading results, especially for beginners and inexperienced computational biologists. With this article, we propose nine quick tips to avoid common mistakes and to out a complete, sound, thorough PEA, which can produce relevant and robust results. We describe our nine guidelines in a simple way, so that they can be understood and used by anyone, including students and beginners. Some tips explain what to do before starting a PEA, others are suggestions of how to correctly generate meaningful results, and some final guidelines indicate some useful steps to properly interpret PEA results. Our nine tips can help users perform better pathway enrichment analyses and eventually contribute to a better understanding of current biology.
    MeSH term(s) Computational Biology/methods ; Databases, Factual ; Humans ; Software
    Language English
    Publishing date 2022-08-11
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1010348
    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 ; Online: An Exploratory Application of Multilayer Networks and Pathway Analysis in Pharmacogenomics.

    Milano, Marianna / Agapito, Giuseppe / Cannataro, Mario

    Genes

    2023  Volume 14, Issue 10

    Abstract: Over the years, network analysis has become a promising strategy for analysing complex system, i.e., systems composed of a large number of interacting elements. In particular, multilayer networks have emerged as a powerful framework for modelling and ... ...

    Abstract Over the years, network analysis has become a promising strategy for analysing complex system, i.e., systems composed of a large number of interacting elements. In particular, multilayer networks have emerged as a powerful framework for modelling and analysing complex systems with multiple types of interactions. Network analysis can be applied to pharmacogenomics to gain insights into the interactions between genes, drugs, and diseases. By integrating network analysis techniques with pharmacogenomic data, the goal consists of uncovering complex relationships and identifying key genes to use in pathway enrichment analysis to figure out biological pathways involved in drug response and adverse reactions. In this study, we modelled omics, disease, and drug data together through multilayer network representation. Then, we mined the multilayer network with a community detection algorithm to obtain the top communities. After that, we used the identified list of genes from the communities to perform pathway enrichment analysis (PEA) to figure out the biological function affected by the selected genes. The results show that the genes forming the top community have multiple roles through different pathways.
    MeSH term(s) Gene Regulatory Networks ; Pharmacogenetics ; Algorithms
    Language English
    Publishing date 2023-10-07
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2527218-4
    ISSN 2073-4425 ; 2073-4425
    ISSN (online) 2073-4425
    ISSN 2073-4425
    DOI 10.3390/genes14101915
    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: Microarray Data Analysis Protocol.

    Agapito, Giuseppe / Arbitrio, Mariamena

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

    2021  Volume 2401, Page(s) 263–271

    Abstract: Microarrays are broadly used in the omic investigation and have several areas of applications in biology and medicine, providing a significant amount of data for a single experiment. Different kinds of microarrays are available, identifiable by ... ...

    Abstract Microarrays are broadly used in the omic investigation and have several areas of applications in biology and medicine, providing a significant amount of data for a single experiment. Different kinds of microarrays are available, identifiable by characteristics such as the type of probes, the surface used as support, and the method used for the target detection. To better deal with microarray datasets, the development of microarray data analysis protocols simple to use as well as able to produce accurate reports, and comprehensible results arise. The object of this paper is to provide a general protocol showing how to choose the best software tool to analyze microarray data, allowing to efficiently figure out genomic/pharmacogenomic biomarkers.
    MeSH term(s) Data Analysis ; Gene Expression Profiling ; Genomics ; Microarray Analysis ; Oligonucleotide Array Sequence Analysis ; Software
    Language English
    Publishing date 2021-12-13
    Publishing country United States
    Document type Journal Article
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-0716-1839-4_17
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text 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.

  9. Article ; Online: Nine quick tips for pathway enrichment analysis.

    Davide Chicco / Giuseppe Agapito

    PLoS Computational Biology, Vol 18, Iss 8, p e

    2022  Volume 1010348

    Abstract: Pathway enrichment analysis (PEA) is a computational biology method that identifies biological functions that are overrepresented in a group of genes more than would be expected by chance and ranks these functions by relevance. The relative abundance of ... ...

    Abstract Pathway enrichment analysis (PEA) is a computational biology method that identifies biological functions that are overrepresented in a group of genes more than would be expected by chance and ranks these functions by relevance. The relative abundance of genes pertinent to specific pathways is measured through statistical methods, and associated functional pathways are retrieved from online bioinformatics databases. In the last decade, along with the spread of the internet, higher availability of computational resources made PEA software tools easy to access and to use for bioinformatics practitioners worldwide. Although it became easier to use these tools, it also became easier to make mistakes that could generate inflated or misleading results, especially for beginners and inexperienced computational biologists. With this article, we propose nine quick tips to avoid common mistakes and to out a complete, sound, thorough PEA, which can produce relevant and robust results. We describe our nine guidelines in a simple way, so that they can be understood and used by anyone, including students and beginners. Some tips explain what to do before starting a PEA, others are suggestions of how to correctly generate meaningful results, and some final guidelines indicate some useful steps to properly interpret PEA results. Our nine tips can help users perform better pathway enrichment analyses and eventually contribute to a better understanding of current biology.
    Keywords Biology (General) ; QH301-705.5
    Language English
    Publishing date 2022-08-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; 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.

  10. Article ; Online: Using BioPAX-Parser (BiP) to enrich lists of genes or proteins with pathway data.

    Agapito, Giuseppe / Cannataro, Mario

    BMC bioinformatics

    2021  Volume 22, Issue Suppl 13, Page(s) 376

    Abstract: Background: Pathway enrichment analysis (PEA) is a well-established methodology for interpreting a list of genes and proteins of interest related to a condition under investigation. This paper aims to extend our previous work in which we introduced a ... ...

    Abstract Background: Pathway enrichment analysis (PEA) is a well-established methodology for interpreting a list of genes and proteins of interest related to a condition under investigation. This paper aims to extend our previous work in which we introduced a preliminary comparative analysis of pathway enrichment analysis tools. We extended the earlier work by providing more case studies, comparing BiP enrichment performance with other well-known PEA software tools.
    Methods: PEA uses pathway information to discover connections between a list of genes and proteins as well as biological mechanisms, helping researchers to overcome the problem of explaining biological entity lists of interest disconnected from the biological context.
    Results: We compared the results of BiP with some existing pathway enrichment analysis tools comprising Centrality-based Pathway Enrichment, pathDIP, and Signaling Pathway Impact Analysis, considering three cancer types (colorectal, endometrial, and thyroid), for a total of six datasets (that is, two datasets per cancer type) obtained from the The Cancer Genome Atlas and Gene Expression Omnibus databases. We measured the similarities between the overlap of the enrichment results obtained using each couple of cancer datasets related to the same cancer.
    Conclusion: As a result, BiP identified some well-known pathways related to the investigated cancer type, validated by the available literature. We also used the Jaccard and meet-min indices to evaluate the stability and the similarity between the enrichment results obtained from each couple of cancer datasets. The obtained results show that BiP provides more stable enrichment results than other tools.
    MeSH term(s) Computational Biology ; Databases, Factual ; Gene Expression Profiling ; Humans ; Neoplasms/genetics ; Proteins/genetics ; Signal Transduction ; Software
    Chemical Substances Proteins
    Language English
    Publishing date 2021-09-30
    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/s12859-021-04297-z
    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