Oluliste transkriptsioonifaktorite tuvastamine lineaarsete mudelite abil
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Date
2010
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Tartu Ülikool
Abstract
Transkriptsioonifaktorite tuvastamine on aktuaalne probleem molekulaarbioloogias. Tänapäeval võimaldavad erinevad tehnoloogilised saavutused jälgida rakus toimuvaid protsesse, kuigi nende analüüs ei ole triviaalne ülesanne, mis vajab erinevate teaduste kaasamist.
Töös kirjeldatakse lineaarsete mudelite kasutamise võimalusi oluliste transkriptsioonifaktorite tuvastamiseks mikrokiibi andmetest. Lineaarse mudeli parameetreid võib käsitleda kui transkriptsioonifaktorite olulisust määravaid näitajaid. Töös on vaadeldud erinevad lineaarregressiooni meetodid koos nende iseärasuste põhjaliku kirjeldusega ning on analüüsitud nende sobivus bioloogiliseks rakenduseks.
With the recent development of the high throughput DNA microarray technology, it became possible to measure the levels of gene activity on a large scale. The data collected from a microarray usually requires sophisticated analysis involving biological knowledge and the application of statistical techniques. In this work the problem of inferring ‘influential’ transcription factors from microarray data using linear models is addressed. Linear models are easy to understand and are able to produce interpretable solutions. The state-of-the-art methods for solving linear regression problems and their applicability to biological data are described in the paper.
With the recent development of the high throughput DNA microarray technology, it became possible to measure the levels of gene activity on a large scale. The data collected from a microarray usually requires sophisticated analysis involving biological knowledge and the application of statistical techniques. In this work the problem of inferring ‘influential’ transcription factors from microarray data using linear models is addressed. Linear models are easy to understand and are able to produce interpretable solutions. The state-of-the-art methods for solving linear regression problems and their applicability to biological data are described in the paper.