@article {472946,
	title = {Generative Method to Discover Genetically Driven Image Biomarkers},
	journal = {Information processing in medical imaging : proceedings of the ... conferenceInformation processing in medical imaging : proceedings of the ... conference},
	volume = {24},
	year = {2015},
	month = {Jul},
	pages = {30-42},
	abstract = {


Abstract. We present a generative probabilistic approach to discovery of disease subtypes determined by the genetic variants. In many diseases, multiple types of pathology may present simultaneously in a patient, making quantification of the disease challenging. Our method seeks com- mon co-occurring image and genetic patterns in a population as a way to model these two different data types jointly. We assume that each patient is a mixture of multiple disease subtypes and use the joint gen- erative model of image and genetic markers to identify disease subtypes guided by known genetic influences. Our model is based on a variant of the so-called topic models that uncover the latent structure in a collection of data. We derive an efficient variational inference algorithm to extract patterns of co-occurrence and to quantify the presence of heterogeneous disease processes in each patient. We evaluate the method on simulated data and illustrate its use in the context of Chronic Obstructive Pul- monary Disease (COPD) to characterize the relationship between image and genetic signatures of COPD subtypes in a large patient cohort.\ 


},
	author = {Batmanghelich, Nematollah K and Saeedi, Ardavan and Cho, Michael H and San Jose Est{\'e}par, Ra{\'u}l and Golland, Polina},
	editor = {Ourselin, S{\'e}bastien and Alexander, Daniel C and Westin, Carl-Frederik and Cardoso, M Jorge}
}