Predicting Autism Spectrum Disorder using Gene Expression Signatures and Machine Learning
- Title
- Predicting Autism Spectrum Disorder using Gene Expression Signatures and Machine Learning
- Other Titles
- 유전자발현특징과 기계학습을 이용한 자폐스펙트럼장애 예측
- Author
- 김일빈
- Alternative Author(s)
- Kim, Il Bin
- Advisor(s)
- 안동현
- Issue Date
- 2015-08
- Publisher
- 한양대학교
- Degree
- Master
- Abstract
- Objective: Genomic data may be a source that aids the identification of individuals with autism spectrum disorder (ASD) posing high heritability. Here we applied a genomic approach to detect a biological signature from peripheral blood with promising performance in the prediction of ASD individuals.
Methods: We utilized the published microarray data GSE26415 from Gene Expression Omnibus (GEO) database, including 21 ASD individuals and 21 controls. Thirty differentially expressed probes were identified by LIMMA package in R language (corrected p-value < 0.05) and were further analyzed using machine learning methods.
Results: The hierarchical cluster analysis was found to categorize 1 ASD individual and 17 controls into one group, and 20 ASD individuals and 4 controls into the other, respectively. For robustness of classification, we adopted supervised machine learning models. Support vector machine showed both sensitivity and specificity of 100% for classifying ASD individuals from controls. Both linear discriminant analysis and K-nearest neighbor indicated sensitivity of 100% and specificity of 88.9%, respectively.
Conclusion: Our findings demonstrate that gene expression profiles identified in peripheral blood from ASD individuals can be utilized for a biological signature of ASD.
- URI
- https://repository.hanyang.ac.kr/handle/20.500.11754/127600http://hanyang.dcollection.net/common/orgView/200000427070
- Appears in Collections:
- GRADUATE SCHOOL[S](대학원) > MEDICINE(의학과) > Theses (Master)
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