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Data-driven Definition of Cell Types Based on Single-cell Gene Expression Data
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Biology Education Centre.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Place, publisher, year, edition, pages
2016.
Keyword [en]
Single-cell, Neural Networks, Machine Learning, Autoencoders, Cell identity, Cell type definition
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:uu:diva-297498OAI: oai:DiVA.org:uu-297498DiVA: diva2:942241
Educational program
Master Programme in Bioinformatics
Supervisors
Available from: 2016-06-23 Created: 2016-06-23 Last updated: 2016-06-23Bibliographically approved

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fulltext(1869 kB)209 downloads
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File name FULLTEXT01.pdfFile size 1869 kBChecksum SHA-512
e502af875628755c5da630f42c596d3996e6d2a4a10d8a369c23acc3ccd81b4539258c84f0919b4bfbad9178ab88fd3e8d2cb1bebf5c7a825f79b20e8b83770e
Type fulltextMimetype application/pdf

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Biology Education Centre
Bioinformatics and Systems Biology

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
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