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Simultaneous clustering of multiple heterogeneous gene expression datasets

Abstract:

Clustering algorithms aim, by definition, at partitioning a given set of objects into a set of clusters such that those objects which belong to the same cluster are similar to each other while being dissimilar to the objects belonging to the other clusters. By application to three case studies of real gene expression data, we demonstrate that the most commonly used algorithms (e.g. k-means and Markov clustering) do not always meet the objective of clustering as per the definition of clusterin...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/AEECT.2017.8257763

Authors


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Institution:
University of Oxford
Division:
MPLS Division
Department:
Plant Sciences
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Plant Sciences
Role:
Author
ORCID:
0000-0001-8583-5362
Bill and Melinda Gates Foundation More from this funder
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT 2017) Journal website
Host title:
IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT 2017)
Publication date:
2018-01-15
Acceptance date:
2017-07-06
DOI:
ISSN:
2378-4903
Source identifiers:
834995
Keywords:
Pubs id:
pubs:834995
UUID:
uuid:3e031ddb-340d-404d-aa73-86480cf30971
Local pid:
pubs:834995
Deposit date:
2018-04-30

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