Block HSIC Lasso

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Climente-González, Héctor
dc.contributor.author Azencott, Chloé Agathe
dc.contributor.author Kaski, Samuel
dc.contributor.author Yamada, Makoto
dc.date.accessioned 2019-07-30T07:21:13Z
dc.date.available 2019-07-30T07:21:13Z
dc.date.issued 2019-07-15
dc.identifier.citation Climente-González , H , Azencott , C A , Kaski , S & Yamada , M 2019 , ' Block HSIC Lasso : Model-free biomarker detection for ultra-high dimensional data ' Bioinformatics , vol. 35 , no. 14 , btz333 , pp. i427-i435 . https://doi.org/10.1093/bioinformatics/btz333 en
dc.identifier.issn 1367-4803
dc.identifier.issn 1460-2059
dc.identifier.other PURE UUID: e685b30b-ecca-472b-a09f-0e94b4905db9
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/block-hsic-lasso(e685b30b-ecca-472b-a09f-0e94b4905db9).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85068898430&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/35580981/btz333.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/39521
dc.description | openaire: EC/H2020/666003/EU//IC-3i-PhD
dc.description.abstract Motivation: Finding non-linear relationships between biomolecules and a biological outcome is computationally expensive and statistically challenging. Existing methods have important drawbacks, including among others lack of parsimony, non-convexity and computational overhead. Here we propose block HSIC Lasso, a non-linear feature selector that does not present the previous drawbacks. Results: We compare block HSIC Lasso to other state-of-the-art feature selection techniques in both synthetic and real data, including experiments over three common types of genomic data: gene-expression microarrays, single-cell RNA sequencing and genome-wide association studies. In all cases, we observe that features selected by block HSIC Lasso retain more information about the underlying biology than those selected by other techniques. As a proof of concept, we applied block HSIC Lasso to a single-cell RNA sequencing experiment on mouse hippocampus. We discovered that many genes linked in the past to brain development and function are involved in the biological differences between the types of neurons. en
dc.format.extent i427-i435
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher OXFORD UNIV PRESS INC
dc.relation info:eu-repo/grantAgreement/EC/H2020/666003/EU//IC-3i-PhD
dc.relation.ispartofseries Bioinformatics en
dc.relation.ispartofseries Volume 35, issue 14 en
dc.rights openAccess en
dc.subject.other Statistics and Probability en
dc.subject.other Biochemistry en
dc.subject.other Molecular Biology en
dc.subject.other Computer Science Applications en
dc.subject.other Computational Theory and Mathematics en
dc.subject.other Computational Mathematics en
dc.subject.other 113 Computer and information sciences en
dc.title Block HSIC Lasso en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Institut Curie
dc.contributor.department Department of Computer Science
dc.contributor.department RIKEN
dc.contributor.department Department of Computer Science en
dc.subject.keyword Statistics and Probability
dc.subject.keyword Biochemistry
dc.subject.keyword Molecular Biology
dc.subject.keyword Computer Science Applications
dc.subject.keyword Computational Theory and Mathematics
dc.subject.keyword Computational Mathematics
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201907304576
dc.identifier.doi 10.1093/bioinformatics/btz333
dc.type.version publishedVersion


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