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Integration of Biomedical Big Data Requires Efficient Batch Effect Reduction
Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi. (Bioinformatics)ORCID-id: 0000-0003-4697-0590
Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi. (Bioinformatics)ORCID-id: 0000-0003-2942-6702
SciCross AB.ORCID-id: 0000-0002-4613-2952
2018 (engelsk)Inngår i: 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, s. 76-82Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2018. s. 76-82
HSV kategori
Forskningsprogram
Bioinformatik; INF501 Integrering av -omicsdata
Identifikatorer
URN: urn:nbn:se:his:diva-15850Scopus ID: 2-s2.0-85048592521ISBN: 978-1-943436-11-8 (tryckt)ISBN: 978-1-5108-5866-4 (tryckt)OAI: oai:DiVA.org:his-15850DiVA, id: diva2:1228354
Konferanse
10th International Conference on Bioinformatics and Computational Biology (BICOB) March 19 - 21, 2018, Las Vegas, NV, USA
Tilgjengelig fra: 2018-06-28 Laget: 2018-06-28 Sist oppdatert: 2019-09-04bibliografisk kontrollert

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