\u7b2c26\u56de\u30a4\u30f3\u30b7\u30ea\u30b3\u30fb\u30e1\u30ac\u30d0\u30f3\u30af\u7814\u7a76\u4f1a\u3092\u4e0b\u8a18\u306e\u3068\u304a\u308a\u884c\u3044\u307e\u3059\u306e\u3067\u3054\u6848\u5185\u3044\u305f\u3057\u307e\u3059\u3002\u4eca\u56de\u306f\u6771\u4eac\u5927\u5b66\u533b\u79d1\u5b66\u7814\u7a76\u6240 George Chalkidis\u3055\u3093\u3092\u8b1b\u5e2b\u3068\u3057\u3066\u304a\u8fce\u3048\u3057\u3001\u300c\u907a\u4f1d\u5b50\u767a\u73fe\u5236\u5fa1\u6a5f\u5e8f\u3068\u3057\u3066\u306e\u9577\u9396\u975e\u30b3\u30fc\u30c9RNA\u306e\u60c5\u5831\u7406\u8ad6\u5206\u6790\u300d\u306b\u3064\u3044\u3066\u8b1b\u6f14\u3057\u3066\u3044\u305f\u3060\u304d\u307e\u3059\u3002<\/p>\n
\u30fb\u65e5\u6642\uff1a\u5e73\u621025\u5e744\u670819\u65e5(\u91d1)\u300017\uff1a00\u201018\uff1a30 \u30fb\u6f14\u984c\uff1aInformation Theoretic Analysis of Long non-coding RNA as a gene expression control mechanism \u30fb\u6982\u8981\uff1aAn emerging theme from multiple model systems is that lncRNAs form novel dependency measure for two-variable relationships of the class of \u30fb\u4e16\u8a71\u4eba\uff1a\u9577\ufa11\u6b63\u6717<\/p>\n","protected":false},"excerpt":{"rendered":" \u7b2c26\u56de\u30a4\u30f3\u30b7\u30ea\u30b3\u30fb\u30e1\u30ac\u30d0\u30f3\u30af\u7814\u7a76\u4f1a\u3092\u4e0b\u8a18\u306e\u3068\u304a\u308a\u884c\u3044\u307e\u3059\u306e\u3067\u3054\u6848\u5185\u3044\u305f\u3057\u307e\u3059\u3002 … \u7d9a\u304d\u3092\u8aad\u3080
\n\u30fb\u5834\u6240\uff1a\u6771\u5317\u30e1\u30c7\u30a3\u30ab\u30eb\u30fb\u30e1\u30ac\u30d0\u30f3\u30af\u6a5f\u69cb2\u968e\u4f1a\u8b70\u5ba41 http:\/\/www.megabank.tohoku.ac.jp\/info\/access.html<\/a><\/p>\n
\n\u30fb\u8b1b\u5e2b\uff1aGeorge Chalkidis ( Institute of Medical Science, The University of Tokyo)<\/p>\n
\nextensive networks of regulatory mechanisms that control the higher-order
\norganization of the transcriptome. The importance of these modes of
\nregulation is underscored by the newly recognized roles of long non-coding
\nRNAs for proper gene control across all kingdoms of life.
\nThe means by which such lncRNAs regulate transcription are speculated to
\nencompass a diversity of mechanisms and the identification and
\ncharacterization of these mechanisms is an active area of research.
\nThere will be 30-50 RNA-seq and TSS-seq data samples from human cultured
\ncells from various cell lines and various conditions. The objective will be
\nto discover potential control mechanisms and model them from small, noisy
\nsamples. The Minimum Description Length Principle (MDL) together with the
\nmaximal information coefficient (MIC) are particularly suited for that
\ntask, because MDL shows excellent non- asymptotical performance in terms of
\nlearning the statistics of finite size context sources from finite length
\ntraining data and thus enables the de-noising and correct model
\nidentification even from small sample sizes. Furthermore, MIC is a powerful<\/p>\n
\nmaximal information-based nonparametric exploration statistics that
\ncaptures a wide range of associations both functional and non-functional
\nand provides a score close to 1 for functional and non-functional
\nrelationships and score close to 0 for statistically independent random
\nvariables.<\/p>\n