## 3 Methods and Materials
### Integrative Statistical and Machine Learning Analysis  
#### 3.4.1 Differential Expression Analysis  
(i) Statistical approach: Edge  
Figure 1: The framework for the differential expression analysis of miRNA in the blood of AD patients and that of normal controls. miRNA sequencing was conducted on the samples after appropriate preparation. The raw count data were analysed using bioinformatics. Differential miRNA expression analysis was carried out using two independent approaches, i.e., statistical and machine learning. Differentially expressed miRNAs (DEMis) were subjected to miRNA target gene prediction, followed by the evaluation of enriched pathways. Raw counts from the miRNA sequencing dataset were filtered to exclude miRNAs with low expressed counts (<10 counts for every sample). The resulting counts were first scaled according to the library size, followed by normalization using a method known as the trimmed mean of M-values (TMM) [26]. The normalization was based on the log-expression ratio of the read count data [26]. Differential miRNA expression analysis was carried out to compare the AD and control groups, based on a linear model generalized by negative binomial distribution in edgeR [27]. A \(p\)-value of <0.05 was considered significant and was applied as the threshold for selecting the top differentially expressed miRNA (DEMi) candidates. DEMi candidates with log\({}_{2}\) fold-change (FC) values that were >0.5 were considered as upregulated, and those with log\({}_{2}\)FC \(<-0.5\) were considered as downregulated. 1. Machine learning approach  
Step 1: Hybrid _carss-SVMRFE_ feature-selection  
Feature-selection was performed using the normalized miRNA dataset (with low-quality reads filtered out) so as to filter out the uninformative genes and to select a subset of genes with the most relevant features. The input expression data were first normalized and log\({}_{2}\)-transformed according to the trimmed mean of M-values (TMM) in edgeR, which minimized the difference in the miRNAs with low expression counts, creating a fitted dispersion with a weaker bias effect. The present study implemented supervised feature-selection using a hybrid filter-wrapper approach based on the absolute correlation-adjusted regression survival scores (_carss_) and multiple support vector machine recursive feature elimination (_MSVM-RFE_) in packages mlr3 and e1071, R [28; 29]. First, the filter method _carss_ was used to select informative variables based on the measurements of the correlation between the \"decorrelated\" variables, while considering the target outcome (AD/normal control). Subsequently, _MSVM-RFE_ was conducted as the wrapper method to select miRNA subsets that could improve the results for subsequent analysis. A sequential backward elimination procedure was applied in _MSVM-RFE_ recursively (\(k=5\)), and feature-ranking scores were calculated in each fold. The average ranking was computed for each feature, and the best feature subset was selected. At the end of this step, the top 50 ranked features (miRNAs) were selected, and those proceeded to the next step. Step 2: Principal component analysis (PCA) with self-organizing map (SOM)  
Next, SOM [30] was performed using the top 50 miRNAs that were selected in Step 1. SOM is an unsupervised clustering method of neural networks which groups and captures the input pattern of the gene expression data in terms of learning rules and then organizes it to reflect the clustering in the final layer [31]. Therefore, the output of a SOM contains clusters, with each cluster containing features of similar characteristics, and the high dimensionality of the data is reduced. In this study, a SOM with a map size of \(2\times 2\), with hexagonal topology, was applied. Hierarchical clustering (HC) was then applied to the resulting SOM cluster to further define the clusters. The miRNAs were clustered according to their expression values, without the predefined knowledge of the dependent class labels [32].