HIGHER ORDER ANALYZES OF ASD GENETIC DATA USING PREFIX SPAN AND PCA METHODS
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Abstract
The most important aim of data mining is to extract useful information from the datasets. Data mining can extract meaningful patterns from large datasets and it can analyze the dataset to predict and classify the dataset based on user specification. This paper deals with medical database called Gene Expression Omnibus from NCBI database, analysed using data mining techniques. The Microarray data of Autism Spectrum Disorder (ASD), contains 100 genes from 21 ASD children, analysed using unsupervised pattern mining algorithm called PREFIXSPAN to find the sequence pattern and dimensionality reduction as Principal Component Analysis (PCA) algorithm, to find the positively and negatively correlated genes for ASD. From the comparison of algorithms, it infers the genes that are Highly Influence by Autism Spectrum Disorder from the 100 genes.
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