Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, the coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that RTs model outperforms other models.

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The coronavirus disease-2019 (COVID-19) pandemic has caused an enormous loss of lives. Various clinical trials of vaccines and drugs are being conducted worldwide; nevertheless, as of today, no effective drug exists for COVID-19. The identification of key genes and pathways in this disease may lead to finding potential drug targets and biomarkers. Here, we applied weighted gene co-expression network analysis and LIME as an explainable artificial intelligence algorithm to comprehensively characterize transcriptional changes in bronchial epithelium cells (primary human lung epithelium (NHBE) and transformed lung alveolar (A549) cells) during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Our study detected a network that significantly correlated to the pathogenicity of COVID-19 infection based on identified hub genes in each cell line separately. The novel hub gene signature that was detected in our study, including PGLYRP4 and HEPHEL1, may shed light on the pathogenesis of COVID-19, holding promise for future prognostic and therapeutic approaches. The enrichment analysis of hub genes showed that the most relevant biological process and KEGG pathways were the type I interferon signaling pathway, IL-17 signaling pathway, cytokine-mediated signaling pathway, and defense response to virus categories, all of which play significant roles in restricting viral infection. Moreover, according to the drug–target network, we identified 17 novel FDA-approved candidate drugs, which could potentially be used to treat COVID-19 patients through the regulation of four hub genes of the co-expression network. In conclusion, the aforementioned hub genes might play potential roles in translational medicine and might become promising therapeutic targets. Further in vitro and in vivo experimental studies are needed to evaluate the role of these hub genes in COVID-19.

Deep learning (DL) is a branch of machine learning (ML) capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant improvements across a range of domains and applications. Single-cell (SC) omics are often high-dimensional, sparse, and complex, making DL techniques ideal for analyzing and processing such data. We examine DL applications in a variety of single-cell omics (genomics, transcriptomics, proteomics, metabolomics and multi-omics integration) and address whether DL techniques will prove to be advantageous or if the SC omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized or addressed the most pressing challenges of the SC omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) but lacking the needed biological interpretability in many cases. Although such developments have generally been gradual, recent advances reveal that DL methods can offer valuable resources in fast-tracking and advancing research in SC.Abstract Figure

With the rapid size and complexity growth of computer networks, network supervisors are now facing a new problem, which is to analyze and manage the large amounts of security alerts that can be generated by security devices. Alert correlation systems attempt to solve this problem by finding the similarity and causality relationships between raw alerts and providing high-level view of the network under surveillance. Several alert correlation methods have been proposed recently to detect known attack scenarios. This paper focuses on how to develop an intrusion-alert correlation system according to the information existed in the raw alerts without using any predefined knowledge. For this purpose, first, we define the concept of alert partial entropy to find the alert clusters with the same information. Then, we represent the alert clusters by an intelligible notation called hyper-alerts. The network supervisor can reduce the number of hyper-alerts based on the principle of maximum entropy or by using the concept of hyper-alerts partial entropy. For more visualization, we define the hyper-alerts graph, which provides a global view of intrusion alerts. Our results show that the proposed entropy-based alert correlation system (E-correlator) can simplify the analysis of large number of alerts. We achieved the promising reduction ratio of 99.98% in LLS_DDOS_1.0 attack scenario in DARPA2000 dataset while the constructed hyper-alerts have enough information to discover the attacker, the victim, and the attack scenario.