2026-09-18T19:13:04Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/430682022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Bonnie Berger. Schmid, Patrick R. (Patrick Raphael) Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2008-11-07T18:58:31Z 2008-11-07T18:58:31Z 2008 2008 http://hdl.handle.net/1721.1/43068 244111903 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008. Includes bibliographical references (leaves 69-73). The objective of this thesis is to present the foundation of an automated large-scale disease prediction system. Unlike previous work that has typically focused on a small self-contained dataset, we explore the possibility of combining a large amount of heterogeneous data to perform gene selection and phenotype classification. First, a subset of publicly available microarray datasets was downloaded from the NCBI Gene Expression Omnibus (GEO) [18, 5]. This data was then automatically tagged with Unified Medical Language System (UMLS) concepts [7]. Using the UMLS tags, datasets related to several phenotypes were obtained and gene selection was performed on the expression values of this tagged microarray data. Using the tagged datasets and the list of genes selected in the previous step, classifiers that can predict whether or not a new sample is also associated with a given UMLS concept based solely on the expression data were created. The results from this work show that it is possible to combine a large heterogeneous set of microarray datasets for both gene selection and phenotype classification, and thus lays the foundation for the possibility of automatic classification of disease types based on gene expression data in a clinical setting. by Patrick R. Schmid. S.M. 73 leaves eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Large scale disease prediction Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="2e034cea-5856-4f01-b646-49069f2821d2"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Large scale disease prediction</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2008</PublicationDate> <Authors> <Author> <DisplayName>Schmid, Patrick R. (Patrick Raphael)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>The objective of this thesis is to present the foundation of an automated large-scale disease prediction system. Unlike previous work that has typically focused on a small self-contained dataset, we explore the possibility of combining a large amount of heterogeneous data to perform gene selection and phenotype classification. First, a subset of publicly available microarray datasets was downloaded from the NCBI Gene Expression Omnibus (GEO) [18, 5]. This data was then automatically tagged with Unified Medical Language System (UMLS) concepts [7]. Using the UMLS tags, datasets related to several phenotypes were obtained and gene selection was performed on the expression values of this tagged microarray data. Using the tagged datasets and the list of genes selected in the previous step, classifiers that can predict whether or not a new sample is also associated with a given UMLS concept based solely on the expression data were created. The results from this work show that it is possible to combine a large heterogeneous set of microarray datasets for both gene selection and phenotype classification, and thus lays the foundation for the possibility of automatic classification of disease types based on gene expression data in a clinical setting.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>