State space and graphical models for estimating networks dynamics

Anani Lotsi

State space and graphical models for estimating networks dynamics

Anani Lotsi

Probability and Statistics

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Aanvankelijk werd de sequencing van het genoom werd door velen leken gezien alsof alles over de genetische werkelijkheid bekend geworden was. Echter, sequencing stelde ons alleen maar voor aan de spelers van een zeer complex genomisch spel, waarvan we de regels nu pas beginnen te begrijpen. Een manier om dit spel te begrijpen is door het uitvinden van hoe de spelers met elkaar omgaan: genomische netwerk reconstructie is een aantrekkelijk paradigma van genomische wetenschap, maar inhomogene monsters en complexe tijdsdynamiek betekent dat de regels van de genomische interactie spel verschillend kunnen zijn voor verschillende subgroepen of zelfs veranderen in de tijd. Dit proefschrift ontwikkelt nieuwe methoden om te gaan met deze twee scenario's.

Originele taal-2	English
Kwalificatie	Doctor of Philosophy
Toekennende instantie	Rijksuniversiteit Groningen
Begeleider(s)/adviseur	Wit, Ernst, Supervisor
Datum van toekenning	17-feb.-2014
Plaats van publicatie	[S.l.]
Uitgever	[S.n.]
Gedrukte ISBN's	9789036767118
Status	Published - 2014

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Complete dissertationFinal publisher's version, 1,29 MB
ThesesFinal publisher's version, 15,5 KB

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title = "State space and graphical models for estimating networks dynamics",

abstract = "Initially, the sequencing of the genome in 2000 was seen by many laymen as if everything about the genetic reality had become known. In fact, sequencing merely introduced us to the players of a very complex genomic game, whose rules we only begin to understand. One way to understand the genomic game is by finding out how the players interact: genomic network reconstruction is an attractive paradigm of genomic science, but inhomogeneous samples and complex time dynamics, means that the rules of the genomic interaction game may be different for different subgroups or even change over time. This thesis develops new methods to deal with these two scenarios.",

keywords = "Proefschriften (vorm), Genoom, Maximaalfuncties, Latente variabelen, Grafische voorstellingen, Normale verdeling, Dynamische modellen, Netwerken, biomathematica, waarschijnlijkheidsrekening",

author = "Anani Lotsi",

note = "Relation: http://www.rug.nl/ Rights: University of Groningen",

year = "2014",

language = "English",

isbn = "9789036767118",

publisher = "[S.n.]",

school = "University of Groningen",

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T1 - State space and graphical models for estimating networks dynamics

AU - Lotsi, Anani

N1 - Relation: http://www.rug.nl/ Rights: University of Groningen

PY - 2014

Y1 - 2014

N2 - Initially, the sequencing of the genome in 2000 was seen by many laymen as if everything about the genetic reality had become known. In fact, sequencing merely introduced us to the players of a very complex genomic game, whose rules we only begin to understand. One way to understand the genomic game is by finding out how the players interact: genomic network reconstruction is an attractive paradigm of genomic science, but inhomogeneous samples and complex time dynamics, means that the rules of the genomic interaction game may be different for different subgroups or even change over time. This thesis develops new methods to deal with these two scenarios.

AB - Initially, the sequencing of the genome in 2000 was seen by many laymen as if everything about the genetic reality had become known. In fact, sequencing merely introduced us to the players of a very complex genomic game, whose rules we only begin to understand. One way to understand the genomic game is by finding out how the players interact: genomic network reconstruction is an attractive paradigm of genomic science, but inhomogeneous samples and complex time dynamics, means that the rules of the genomic interaction game may be different for different subgroups or even change over time. This thesis develops new methods to deal with these two scenarios.

KW - Proefschriften (vorm)

KW - Genoom

KW - Maximaalfuncties

KW - Latente variabelen

KW - Grafische voorstellingen

KW - Normale verdeling

KW - Dynamische modellen

KW - Netwerken

KW - biomathematica

KW - waarschijnlijkheidsrekening

M3 - Thesis fully internal (DIV)

SN - 9789036767118

PB - [S.n.]

CY - [S.l.]

ER -

State space and graphical models for estimating networks dynamics

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