his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Integration of Biomedical Big Data Requires Efficient Batch Effect Reduction
University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre. (Bioinformatics)ORCID iD: 0000-0003-4697-0590
University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre. (Bioinformatics)ORCID iD: 0000-0003-2942-6702
SciCross AB.
2018 (English)In: 10th International Conference on Bioinformatics and Computational Biology (BICOB): Las Vegas, Nevada, USA 19 – 21 March 2018 / [ed] Hisham Al-Mubaid, Qin Ding, Oliver Eulenstein, 2018, p. 76-82Conference paper, Published paper (Refereed)
Abstract [en]

 Efficiency in dealing with batch effects will be the next frontier in large-scale biological data analysis, particularly when involving the integration of different types of datasets. Large-scale omics techniques have quickly developed during the last decade and huge amounts of data are now generated, which has started to revolutionize the area of medical research. With the increase in the volume of data across the whole spectrum of biology, problems related to data analytics are continuously increasing as analysis and interpretation of these large volumes of molecular data has become a real challenge. Tremendous efforts have been made to obtain data from various molecular levels and the most recent trends show that more and more researchers now are trying to integrate data of various molecular types to inform hypotheses and biological questions. Tightly connected to this work are the batch-related biases that commonly are apparent between different datasets, but these problems are often not tackled. In present study the ComBat algorithm was applied and evaluated on two different data integration problems. Results show that the batch effects present in the integrated datasets efficiently could be removed by applying the ComBat algorithm.

Place, publisher, year, edition, pages
2018. p. 76-82
National Category
Bioinformatics (Computational Biology)
Research subject
Bioinformatics
Identifiers
URN: urn:nbn:se:his:diva-15850Scopus ID: 2-s2.0-85048592521ISBN: 978-1-943436-11-8 ISBN: 978-1-5108-5866-4 OAI: oai:DiVA.org:his-15850DiVA, id: diva2:1228354
Conference
10th International Conference on Bioinformatics and Computational Biology (BICOB) March 19 - 21, 2018, Las Vegas, NV, USA
Available from: 2018-06-28 Created: 2018-06-28 Last updated: 2018-07-02Bibliographically approved

Open Access in DiVA

No full text in DiVA

Scopus

Authority records BETA

Synnergren, JaneGhosheh, Nidal

Search in DiVA

By author/editor
Synnergren, JaneGhosheh, Nidal
By organisation
School of BioscienceThe Systems Biology Research Centre
Bioinformatics (Computational Biology)

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 6 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf