###### Abstract  
Thanks to high-throughput data technology, microRNA analysis studies have evolved in early disease detection. This work introduces two complete models to detect the biomarkers of two autoimmune diseases, multiple sclerosis and rheumatoid arthritis, via miRNA analysis. Based on work the authors published previously, both introduced models involve complete pipelines of text mining methods, integrated with traditional machine learning methods, and LSTM deep learning. This work also studies the fragmentation of miRNA sequences to reduce the needed processing time and computational power. Moreover, this work studies the impact of obtaining two different library preparation kits (NEBNEXT and NEXTFLEX) on the detection accuracy for rheumatoid arthritis. Additional experiments are applied to the proposed models based on three different transcriptomic datasets. The results denote that the transcriptomic fragmentation model reported a biomarker detection accuracy of 96.45% on a sequence fragment size of 0.2, indicating a significant reduction in execution power while retaining biomarker detection accuracy. On the other hand, the LSTM model obtained a promising detection accuracy of 72%, implying savings in feature engineering processing. Additionally, the fragmentation model and the LSTM model reported 22.4% and 87.5% less execution time than work in the literature, respectively, denoting a considerable execution power reduction. m 2022  
evolved, and biological science has been revolutionized. NGS technology generates significant amounts of output data. Hence, studying and analyzing these sequenced data and applying this analysis in genetic studies have significantly developed and grown in importance accordingly [6,7]. RNA sequencing (RNA-Seq) utilizes the competencies of the high-throughput sequencing methods to provide an insight into the transcriptome of a studied cell. RNA-Seq avails a significantly higher coverage and superior resolution of the dynamic nature of the transcriptome, and consequently provides higher early disease biomarker detection compared to the microarray-based methods and Sanger sequencing. miRNA are small non-coding molecules that primarily regulate the expression of genes at the transcript level. Sequencing miRNAs isolated from exosomes represent a high potential for disease biomarkers' detection. However, exosomes with a low RNA amount obstruct proper quantification and analysis. Hence, library preparation protocols are utilized with miRNA NGS to enhance the samples and, consequently, the miRNA analysis results [8,9]. Hence, different sequencing preparation protocols involve studying their influence on the sequenced data and how these various protocols impact the disease biomarker detection accuracy and the results of genetic studies [10,11]. This work proposes two complete miRNA analysis models for early MS and RA biomarker detection based on a comprehensive MS disease biomarker detection model that the authors published earlier. The presented model, introduced in the authors' previous work, primarily converted the NGS analysis problem into a text mining problem; promising results have been reported upon studying an miRNA dataset of multiple sclerosis (MS) patients. In addition, a novel feature extraction method (kmerFIDF) for detecting disease biomarkers was introduced in our previous work [11]. In this work, the first model introduces transcriptomic sample fragmentation and studies the feasibility of fragmenting transcriptomic samples while retaining the high disease prediction accuracy. The second model utilizes LSTM as a deep learning model in analyzing these type of data. Both models are additionally examined against three different datasets. In addition, the model that was introduced previously is also tested on the same three datasets to study its robustness with more extensive datasets [11]. Comparative analysis is applied to the results of the three models.