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X-WR-CALDESC:Mr. Héctor Climente-González (Institut Curie): Block HSIC La
 sso: model-free biomarker detection for ultra-high dimensional data
X-WR-CALNAME:Mr. Héctor Climente-González (Institut Curie): Block HSIC La
 sso: model-free biomarker detection for ultra-high dimensional data
X-WR-TIMEZONE:Asia/Tokyo
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DTSTART:19700101T000000
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:JST
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BEGIN:VEVENT
UID:742854@techplay.jp
SUMMARY:Mr. Héctor Climente-González (Institut Curie): Block HSIC Lasso: 
 model-free biomarker detection for ultra-high dimensional data
DTSTART;TZID=Asia/Tokyo:20190731T140000
DTEND;TZID=Asia/Tokyo:20190731T150000
DTSTAMP:20260405T160829Z
CREATED:20190724T140042Z
DESCRIPTION:イベント詳細はこちら\nhttps://techplay.jp/event/74285
 4?utm_medium=referral&utm_source=ics&utm_campaign=ics\n\nSpeaker: Mr. Hé
 ctor Climente-González (Institut Curie\, France)\n\nAbstract\nMotivation
 : Finding nonlinear relationships between biomolecules and a biological o
 utcome is computationally expensive and statistically challenging. Existi
 ng methods have important drawbacks\, including among others lack of pars
 imony\, non-convexity\, and computational overhead. Here we propose block
  HSIC Lasso\, a nonlinear feature selector that does not present the prev
 ious drawbacks.\nResults: We compare block HSIC Lasso to other state-of-t
 he-art feature selection techniques in both synthetic and real data\, inc
 luding experiments over three common types of genomic data: gene-expressi
 on 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 sel
 ected 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 f
 unction are involved in the biological differences between the types of n
 eurons. \nAvailability: Block HSIC Lasso is implemented in the Python 2/3
  package pyHSICLasso\, available on PyPI. Source code is available on Git
 Hub (https://github.com/riken-aip/pyHSICLasso).\n\nBio:\nhttp://hclimente
 .eu
LOCATION:理化学研究所・革新知能統合研究センタ－　日本
 橋オフィス　会議室5 東京都中央区日本橋1-4-1 日本橋
 一丁目三井ビルディング 15階 
URL:https://techplay.jp/event/742854?utm_medium=referral&utm_source=ics&utm
 _campaign=ics
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