Multiple sclerosis is an autoimmune disease that causes psychological impacts and severe physical disabilities, including motor disabilities and partial blindness. This work introduces an early detection method for multiple sclerosis disease by analyzing transcriptomic microRNA data. By transforming this phenotype classification problem into a text mining problem, multiple sclerosis disease biomarkers can be obtained. To our knowledge, text mining methods have not been introduced previously in transcriptomic data analysis of multiple sclerosis disease. Hence, this work presents a complete predictive model by combining consecutive transcriptomic data preprocessing procedures, followed by the proposed KmerFIDF method as a feature extraction method and linear discriminant analysis for dimensionality reduction. Predictive machine learning methods can then be obtained accordingly. This study describes experimental work on a transcriptomic dataset of noncoding microRNA sequences denoted from relapsing-remitting multiple sclerosis patients before fingolimod treatment and after six consecutive months of treatment. The experimental results of the predictive methods with the proposed model report sensitivity, specificity, F1-score, and average accuracy scores of 96.4, 96.47, 95.6, and 97% with random forest, 92.89, 92.78, 93.2, and 94% with support vector machine and 91.95, 92.2, 93.1, and 94% with logistic regression, respectively. These promising results support the introduced model and the proposed KmerFIDF method in transcriptomic data analysis. Moreover, comparative experiments are conducted with two referenced studies. The obtained results show that the average reported accuracy scores of the proposed model outperform the referenced literature work.

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.

Detecting anomalous executions in business process data is crucial for safeguarding the efficiency and success of an organization. Unsupervised approaches are commonly used for business process anomaly detection because of the scarcity of labeled anomaly data. However, these approaches often encounter a notable decline in performance because they lack prior knowledge about the anomalies. Additionally, most of them do not perform root cause analysis on the detected anomalies. This study proposes a variational autoencoder-based approach to overcome the performance limitations of existing unsupervised methods and determine the root causes of the detected anomalies. The learning of the variational autoencoder from unlabeled business process data is enhanced in the proposed approach by leveraging different architectural components, namely, the entity embedding technique, the bidirectional long short-term memory network, and the self-attention mechanism. Combining these architectural components in the variational autoencoder architecture leads to learning high-level representations from the business process data and thus improving the reconstruction capability of the variational autoencoder. Furthermore, this study suggests feeding the reconstruction error provided by the variational autoencoder into the logistic regression classifier to improve the accuracy of anomaly detection. The performance of the proposed model was evaluated on real-life and synthetic datasets. The experimental findings indicate that the proposed model outperforms six existing anomaly detection models in terms of precision, recall, and F1-score metrics.

The rapid increase in data volume over the past years has given rise to big data science. Distributed File Systems (DFS) have become widely employed to handle this vast amount of data, such as Google File System and Hadoop Distributed File System (HDFS). The primary objective of a DFS is to ensure data availability and system reliability in the event of failure. Data availability and system reliability are achieved by replicating files across multiple locations, which, however, results in the consumption of storage space and other resources. The significance of these files varies based on their frequency of use within the system. As a result, specific files are deemed less critical and do not warrant extensive replication, as they hold little importance in the overall system. This paper presents a novel approach called "Optimizing HDFS Replication Policies with Predictive Categorization" for storage efficiency. This approach aims to minimize storage consumption while ensuring data availability and system reliability. Experimental results using the Spotify Hit Predictor Dataset (1960-2019) showcase significant improvements in storage utilization and throughput. This approach not only enhances system performance but also positions organizations for substantial cost efficiencies in managing their data-intensive workloads.

Recently, antibodies have been considered essential therapeutics for combating diseases, especially viral infections. However, limited information on antibody structures has impeded their development. The interactions promoting antigen binding are determined by the structures of the six loops that comprise the complementarity-determining regions (CDRs). Artificial Intelligence (AI) technologies are overcoming many limitations by using co-evolution information from homologous proteins to predict protein function and structure and develop drug discovery. This study introduces an overview of computational methods for artificial intelligence for producing antibody structure and design and covers databases used, CDR loops, structural elements crucial to binding, and computer predictors of antibody structure and features; it highlights that Deep Learning (DL) techniques are essential for improving antibody structure prediction. Using various principles and methods, these techniques have significantly advanced the ability to predict novel antibody structures for binding.