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ìÿí ùÿð íàìàçäà Áþéöê Éàðàäàíäàíäèëÿéèðÿì”, - äåéÿ úàìààòà öðÿê-äèðÿê âåðÿð, îíëàðûíöìäèíè àðòûðàð, äÿéÿðëè ñþùáÿòëÿðè èëÿ ñàêèòëÿøäèðÿðäè.Ñåéèä Éóñèô àüà 2005 -úè èë àâãóñò àéûíûí 26-äà þìðöíöí 79-úó éàéûíäà àðàìûçäàí ýåòäè. Î, äöíéàñûíû äÿéèøñÿ äÿ, îúàüûíäàäÿéÿðëè, àëèúÿíàá îüóëëàðûíû, íÿâÿëÿðèíè ýþðöá òÿñêèíëèê òàïûðûã.Ãÿäèðáèëÿí åëèìèç ñåéèäèí âÿôàòûíäàí ñîíðà îíóí éàøàäûüû Áÿðäÿðàéîíóíóí Úàíàâàðëû êÿíäèíèí àäûíû äÿéèøÿðÿê, î áþéöê èíñàíûíøÿðÿôèíÿ «Ñåéèä Éóñèôëè» àäëàíäûðìûøëàð. Áó ýöí Áÿðäÿ ðàéîíóíóíÑåéèä Éóñèôëè êÿíäèíÿ ýåäÿí ìàøûí êàðâàíûíûí àðäû-àðàñû êÿñèëìèð.Áó êàðâàí ìÿíÿâè òÿñêèíëèéÿ åùòèéàúû îëàíëàðû, ìöøêöë èøëÿðèíèíùÿëëèíè, õÿñòÿëèêëÿðèíèí ÷àðÿñèíè Óëó Òàíðûäàí äèëÿéÿíëÿðè ÑåéèäÉóñèô àüàíûí îúàüûíà àïàðûð. Ñåéèäèí íÿâÿëÿðè áþéöê åùòèðàìëàçÿââàðëàðû ñòîëà äÿâÿò åäèðëÿð. Äóàëàð îõóíóð, ùÿäèñëÿð äàíûøûëûð, ÓëóÒàíðûìûç, ïåéüÿìáÿðëÿðèìèç âÿ èìàìëàðûìûç éàä îëóíóð. ÑåéèäÉóñèô áàáàíûí ùÿéàò òÿðçèíè, àäÿòëÿðèíè õàòûðëàäàí øÿõñè ÿøéàëàðû,êèòàáëàðû ñàõëàíûëàí çÿíýèí ìóçåéè ùÿéÿúàíñûç çèéàðÿò åòìÿêìöìêöí äåéèë.Äÿôÿëÿðëÿ èëûã íÿôÿñè öçöìöçÿ äÿéÿí, äóàëàðû èëÿ áèçëÿðèèíñòèòóòà, ùÿðáè õèäìÿòÿ éîëà ñàëàí Ñåéèä Éóñèô áàáà äöíéàñûíûäÿéèøñÿ äÿ, ðóùó äàèìà áèçèìëÿäèð. Î, ñàü îëñàéäû, éÿãèí èíäè äÿùÿìèøÿêè êèìè èøüàë îëóíìóø òîðïàãëàðûí òåçëèêëÿ àëûíìàñûíû ÓëóÒàíðûäàí äèëÿéÿðäè. Êÿëáÿúÿð äÿéÿðëÿðèíèí ìèíäÿ áèðèíè þçöíäÿÿêñ åòäèðÿí áó êèòàáû éàçìàã ôèêðèìèçè áÿéÿíÿð âÿ áèçÿ óüóðëàðàðçóëàéàðäû. Ãîé, Óëó Òàíðû ñåâèìëè áÿíäÿñè Ñåéèä Éóñèô àüàíûíõÿòðèíÿ áàøëàäûüûìûç áó ìÿñóëèééÿòëè âÿ àüûð èøèìèçäÿ áèçÿ éàðîëñóí! Àìèí!Ñåéèä Éóñèô àüà âÿ íÿâÿëÿðè.4
IntroductionResponse ModellingEach sample has a characteristic or response that we wouldlike <strong>to</strong> predict from our measurements “inside” the celly (n by 1) X (n by p)Say n=100 and p=30,000
Page 2: Talk outline1. Introduction, motiva
Page 9 and 10: Some issues1. Artefacts - apparent
Page 11 and 12: Cross validationBoxplotC ross valid
Page 13 and 14: Parameters in the modelsThe models
Page 15 and 16: The prior - special casesk=1 (Lasso
Page 21 and 22: An EM algorithmWant to find (local)
Page 23 and 24: E- step continuedThe conditional ex
Page 25 and 26: CommentsStructure can be used to ma
Page 27 and 28: GeneRave in Action
Page 29 and 30: Êÿëáÿúÿð ÿðàçèñèíä
Page 31 and 32: Example 1: St Jude’s Leukaemia da
Page 33 and 34: Single Nucleotide PolymorphismsSNPA
Page 35 and 36: Data and modelWe fit a sparse “ma
Page 37 and 38: Two SNP race classifierafd0860639
Page 39 and 40: Independent validation of resultsTh
Page 41 and 42: CodeR Library available for downloa

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