VISUALIZATION OF FEATURE MAPS AND TRAINED FILTERS OF BI-

In this section, we visualized the trained filters and feature maps from intermediate convolutional hidden layers of our trained bi-stream CNN model. The bi-stream CNN model had a few advantages when compared with traditional machine learning algorithms.

First of all, we used chromosome maps to represent the genotyping array information, which converted one-dimensional genome data to images. Secondly, we used 16 convolutional 3x3 size kernels to capture local genomic features and detect patterns from adjacent genes and SNP sites from two chromosome SNP maps. Thirdly, two-branch CNN model could capture the genomic features from two chromosomes at the same time. Figure 4.4 A and B showed the output feature maps and their corresponding trained filters from convolutional layer C1 of each branch CNN models. Some trained filters could highlight the most important and informative SNP sites from the chromosome SNP maps, and neglect less informative ones (marked as yellow squares). The green rectangles showed that our trained filters could sharpen input images and capture local motifs, which represented the correlated variations patterns in genome regions. The bi-stream model could also detect continuous gene and SNP intensity variations by capturing adjacent variation patterns in line (marked as white rectangles).

Our bi-stream CNN model could detect the simultaneous or causal SNPs variations in human genomes. These genome characterizations and extracted genomic patterns provided signals to classify DS and normal samples. However, traditional machine learning algorithms tended to build models with a global view from all available features and treated each feature independently. Therefore, they were hard to extract signals from regional genomic patterns and correlations between adjacent genes and SNPs sites.

Figure 4. 4 Visualization of feature maps and trained filter weights from convolutional layer C1 of Figure 4.3. Figure a, b, c and d in figure (A) represent four feature maps from convolutional layer C1 of lower branch CNN model (shown in Figure1). Figure e, f, g, and h in figure (A) are the corresponding 3x3 kernels weights of Figure a, b, c, and d. Figure a, b, c and d in Figure (B) represent four feature maps from convolutional layer C1 of the upper branch CNN model. Figure e, f, g, and h in figure (B) are the corresponding 3x3 kernels weights for Figure a, b, c, and d.

4.8 DISCUSSION

Previous studies illustrated that gene expressions and SNP variations were highly correlated within local genome regions (Bulik-Sullivan et al., 2015; Duerr et al., 2006; Farh et al., 2015). Genome-wide association studies also demonstrated that human DS was usually associated with many gene copy number and SNPs variations, and many unidentified genomic abnormalities (Jain et al., 2016; Petry et al., 2017; Sailani et al., 2013). In this study, our bi-stream CNN model could learn the genomic features and associated

variations among adjacent genes and SNP sites from chromosome SNP maps. Currently, human DS treatments only have limited effects. They cannot cure DS fundamentally either. At the same time, there isn’t any clear effect or benefit on human DS treatments by using traditional drugs (K. J. Gardiner, 2015; Kishnani et al., 2010; Lott et al., 2011; Mohan, Bennett, & Carpenter, 2009). The feature maps and extracted genome features could identify DS related markers and pathway components. These genome features explained the genomic characteristics and pathological mechanisms of human DS, which could be further applied in gene therapy and genetic medicine developments.

An accurate non-invasive DS screening method offers a low-risk way to screen human DS. It helps low-risk patients avoid taking further invasive diagnostic procedures, which could result in fetal loss. Nowadays, genotyping array analyses on fetal genomes could be performed on the trophoblast cells with non-invasive procedures after the fifth week gestation (Jain et al., 2016; Petry et al., 2017). In this study, we developed a novel method to construct accurate DS screening model by using bi-stream CNN and genotyping array data. The results showed that our bi-stream CNN model had the best performance in every evaluation metric when compared with two single-stream CNN models and three traditional machine learning models. The CNN model achieved over 99.3% accuracies, as well as very low false positive and false negative rates. It was very important to disease prediction and medical practice. Even though traditional machine learning algorithms obtained over 96% accuracies. Their high false-negative rates are not suitable for clinical screening tests. Traditional machine learning algorithms treated each SNP sites as single feature independently. They were hard to extract signals from regional genomic patterns and variation correlations between adjacent genes and SNPs sites. Although the single-

stream models could extract features and patterns from local genome features and adjacent SNP sites, they could only learn these features from one single chromosome, which completely neglected the genomic patterns of the other one. In deep learning studies, large datasets were great obstacles in the model construction and optimization. We used each pixel to represent the intensities of SNP site, and used chromosome SNP maps to represent the genome information, which significantly reduced data and model complexity. Furthermore, our bi-stream CNN architecture could learn local genomic patterns and extracted regional features, which could also be applied to build prediction models from genotyping array data for more diseases.

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