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Machine Learning Model for ASD Gene Prediction by ahfn79 is a document available to read on EtoBox.

This paper proposes a machine learning model to predict genes associated with autism spectrum disorder (ASD) risk. The model uses gene expression data from brain development and applies feature extraction with wavelet transforms, discretization, and Bayes network classification. It compares the proposed model to other classification methods on long non-coding RNA gene data. Experimental results show the model achieves sensitivity of 0.902, area under the ROC curve of 0.839, Matthews correlation coefficient

Author

ahfn79

Language

EN