Abstract
Autism Spectrum Disorder (ASD) is defined as polygenetic developmental and neurobiological disorders that cover a variety of development delays in social interactions. In recent years, computational methods using gene expression data have been proved to be effective in predicting ASD at the early stage. Feature selection methods directly affect the prediction performance of the ASD prognosis methods. With the advances of computational methods and exploding of high-dimensional ASD gene expression data, there is a need to examine the performance of different computational techniques in predicting ASD. In this paper, we review and conduct a comparison study of 22 different feature selection methods for predicting ASD from gene expression data. The methods are categorised into traditional methods (14 methods) and network-based methods (8 methods). The experimental results have shown that the network-based methods generally outperform the traditional feature selection methods in all three accuracy measures, including AUC (area under the curve), F1-score, and Matthews Correlation Coefficient.
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This work has been supported by the National Natural Science Foundation of China (61702069, 61963001), the Yunnan Fundamental Research Projects (202001AT070024), the NHMRC Grant (1123042), and the Australian Research Council Discovery Grant (DP170101306).
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Zhang, J., Nguyen, T., Truong, B., Liu, L., Li, J., Le, T.D. (2020). Computational Methods for Predicting Autism Spectrum Disorder from Gene Expression Data. In: Yang, X., Wang, CD., Islam, M.S., Zhang, Z. (eds) Advanced Data Mining and Applications. ADMA 2020. Lecture Notes in Computer Science(), vol 12447. Springer, Cham. https://doi.org/10.1007/978-3-030-65390-3_31
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