ࡱ> Root EntryRoot Entry^ @FileHeaderXDocInfoG5BodyText W W  HwpSummaryInformation.?PrvImage PrvTextDocOptions ^ ^ Scripts ^ ^ JScriptVersion _ DefaultJScript\_LinkDoc`  !"#$%&'()*+,-./0123456789:;<=>@ABCDEFHIJKLMNOPQRSTUVWYZ[]^abcdefghjklmnopqrstuvwxyz{| ] ȩ : Inferring and exploiting latent structure in biomedical data mining Ŭ : D P [DYP] | : 12 5|(|) $ 4:30-5:30 nj :  6218 A recent explosion of biomedical data has enabled researchers to uncover the genetic basis of complex traits such as drug response or disease susceptibility. In this talk, we introduce advanced machine learning techniques for inferring and exploiting latent structure in biomedical data mining. We first consider the problem of finding causal variants that perturb the phenotypic traits of individuals. Sparse structured regression models and information theoretic approaches are employed to utilize the latent structure in either input space or output space. At the later part of this talk, we introduce a non-parametric Bayesian approach for joint segmentation of multiple related sequence data. The proposed model allows key features of the segment definition to be shared across multiple sequences, while retaining the ability for each sequence to have its own segmGIF89aɻxxxkkk]]]PPPCCC555((( d`LH00`Hذ̘`H0|pdXL@`0H `H0`dHL`H0xp`XH@0d LȰ``HH00dLؘȀ`H0xp`XH@d0L `H0d`LH`H0|pdXL@0`$H`dHL00`HذȘdL0xp`XH@0` HdH0ȳ``HHdL0xp`XH@`0H Ը̰ĠऀؘpȈXxHp@h@d8`8X0xT0pH dxddxd`d`p`|``x`p``x`!, H*\ȰÇ#JHŋ3jȱǏ CIɓ(S\ɲ˗0cʜI͛8sɳϟ@ JѣH*]ʴӧPJJիXjʵׯ`ÊKٳhӪ]˶۷<3e(w?t;._zc//E̸ǐ#KL2#мx>9hϦVԧְ>{謻Ǵ[:mKs{|yӫ_8ˇ@A?fg߀( !߂߃>(|9xRW@O8%X*H,bc7+޸"9#9I"e&S`}5K"A1$Uv`)dihx!)ؠ2&AYCFf&LN"M*(S(w֨F8([6'uv:tZy!p~eo2jӖh2J+Q:J饼YSz(1kꂙIe*Ji־Dixbn{n z+f9[jD20D%⒫붙)ީLgʻ^jjnɟ GaQylW6 b .W؊j]'?=3$?]TBYs-8Y.ovзfߜ o*e\tG;=Mgl4RKŠJ^̇[3L .V\u +J4A^ YϷbS6-tύr#;|Q |D}Mү?pB]tv=N G>X[TA}* ϡH֤?'aC% OxʪJx8̡wy@ H"HL&:PH*ZX̢.z` H2hL6pH:x̣> IBL"F:򑐌$'IJ0 ;`a``a`0x  ` l x]ajou2014D 9 22| Ɣ| $ 1:29:43ajou8, 5, 8, 1433 WIN32LEWindows_7@P'@W @NPƿ{P l`$"n&@' aIV>lLƘ@‚х!Section0iY)-͍9͝~mMGa܄KzOm-8}>lܦI?rku-޳`JJy8}grIImL75e:@a![=?Y;T% ݯ`Ti]ȫG s:uw^߶+Q"sVEN}h盀0#Ff~EE'Ff2qHomίt״?at]zkPHܲWsv#[nAoMZsk$ncϷ-[R}6Wavԛoӯ{aɭ+ '<8~ ӱS fBaӘDEds3:wIfu.#K0;)ܣQ4F"ى2wFn;'QPs˩br­4Iюt& d!IÆ I<}P6YOJ]<Ɋ=TP3&U{kQǿe6Qa i[=.EE7-"4 D="Ci詇B=K C/?⡠%i}n<8d~fvvfXU{tFB _ H^Lr6-L2 c5E^ w0,^BQ&R}IZ[wp 6^rs1KccA.SC1@;IE)aGK1(Rرv H+ޡJ= RU ˀL@k;G;)L" 2fwyJ%~z12DA&i3;vl =U_&X)0 K*mL۝eO^:{ntŶ˱ࡇg/7*}K&1&,Ix"OCc\+vE>ܳ&ʱ>Z7e敃ڱ`\Ր沵Ղ:Ȱn:[1#c{xDr|}5VTX{2NgDOk;kg~^k_,!-.>q8:6E>T%Yc:,EuT[B݃"(S Kt@A*G(rbDBnIq9VQK}*%GYitή:2.#yدx/B[bK 2Gv ]dT:9-0{,ȝ<+}x_F0e[UUZ{=#u߳W/]sΩ ȌU^{K!PtۼV#d<!&y讪j}~Zfރ6ʙUislU˲B ֽ{ y/# Y Eb٨:*D(7ɖ%%+W˸AhUD&+Jd~$ s(*{d[[:VZ,h M.u |= 5T%z[@+.MYo\UH#T|T֢J_Y :UNrT-]D,;@⿃"~=Fļ2'yyt::ݙ7}ΰOtiK>>yO[Av=,FOISLդɿzf>^k}/)w