Background: With the rapid development of high-throughput genomic technologies and the accumulation of genome-wide datasets for gene expression profiling and biological networks, the impact of diseases and drugs on gene expression can be comprehensively characterized. Drug repositioning offers the possibility of reduced risks in the drug discovery process, thus it is an essential step in drug development. Results: Computational prediction of drug-disease interactions using gene expression profiling datasets and biological networks is a new direction in drug repositioning that has gained increasing interest. We developed a computational framework to build disease-drug networks using drug- and disease-specific subnetworks. The framework incorporates protein networks to refine drug and disease associated genes and prioritize genes in disease and drug specific networks. For each drug and disease we built multiple networks using gene expression profiling and text mining. Finally a logistic regression model was used to build functional associations between drugs and diseases. Conclusions: We found that representing drugs and diseases by genes with high centrality degree in gene networks is the most promising representation of drug or disease subnetworks.

The Social Internet of Things (SIoT) paradigm incorporates social networking concepts with the Internet of Things (IoT) solutions to support novel services. The massive amount of data (big data) produced by SIoT necessitates efficient information processing frameworks to exploit social relationships and comprehend actionable information from real-world observations. Data from AI-enabled sensors (AIS) is typically geo-tagged, thus demanding geo-textual processing for information retrieval and analysis. Social media applications are the main source of geo-textual data as mobile users connect with millions of posts daily. The processing of big geo-textual data requires resource-efficient algorithms and frameworks. Clustering algorithms are often applied to geo-textual data to examine spatial, textual, and temporal information for event detection, sentiment analysis, and search query response. Clustering algorithms on big data are resource-hungry requiring comparisons among all data points to calculate similarity and distance metrics. Existing hybrid clustering techniques execute algorithms collectively on geo-textual data resulting in a enormous footprint for big data. We propose a resource-efficient clustering framework for AIS that hierarchically performs geo-textual clustering without significantly lowering the clustering quality. The proposed framework achieves substantial time and memory efficiency while reducing the overall resource requirements for constrained end-user and edge devices compared to the standard hybrid geo-textual clustering framework. Moreover, we augment the research work by developing open-source scripts for both hierarchical and hybrid clustering frameworks.

With the rapid development of virtualization techniques, cloud data centers allow for cost-effective, flexible, and customizable deployments of applications on virtualized infrastructure. Virtual machine (VM) placement aims to assign each virtual machine to a server in the cloud environment. VM Placement is of paramount importance to the design of cloud data centers. Typically, VM placement involves complex relations and multiple design factors as well as local policies that govern the assignment decisions. It also involves different constituents including cloud administrators and customers that might have disparate preferences while opting for a placement solution. Thus, it is often valuable to return not only an optimized solution to the VM placement problem but also a solution that reflects the given preferences of the constituents. In this article, we provide a detailed review on the role of preferences in the recent literature on VM placement. We examine different preference representations found in the literature, explain their existing usage, and explain the adopted solving approaches. We further discuss key challenges and identify possible research opportunities to better incorporate preferences within the context of VM placement.

Background: A substantial amount of COVID-19–related data is generated by Twitter users every day. Self-reports of COVID-19 symptoms on Twitter can reveal a great deal about the disease and its prevalence in the community. In particular, self-reports can be used as a valuable resource to learn more about common symptoms and whether their order of appearance differs among different groups in the community. These data may be used to develop a COVID-19 risk assessment system that is tailored toward a specific group of people. Objective: The aim of this study was to identify the most common symptoms reported by patients with COVID-19, as well as the order of symptom appearance, by examining tweets in Arabic. Methods: We searched Twitter posts in Arabic for personal reports of COVID-19 symptoms from March 1 to May 27, 2020. We identified 463 Arabic users who had tweeted about testing positive for COVID-19 and extracted the symptoms they associated with the disease. Furthermore, we asked them directly via personal messaging to rank the appearance of the first 3 symptoms they had experienced immediately before (or after) their COVID-19 diagnosis. Finally, we tracked their Twitter timeline to identify additional symptoms that were mentioned within ±5 days from the day of the first tweet on their COVID-19 diagnosis. In total, 270 COVID-19 self-reports were collected, and symptoms were (at least partially) ranked. Results: The collected self-reports contained 893 symptoms from 201 (74%) male and 69 (26%) female Twitter users. The majority (n=270, 82%) of the tracked users were living in Saudi Arabia (n=125, 46%) and Kuwait (n=98, 36%). Furthermore, 13% (n=36) of the collected reports were from asymptomatic individuals. Of the 234 users with symptoms, 66% (n=180) provided a chronological order of appearance for at least 3 symptoms. Fever (n=139, 59%), headache (n=101, 43%), and anosmia (n=91, 39%) were the top 3 symptoms mentioned in the self-reports. Additionally, 28% (n=65) reported that their COVID-19 experience started with a fever, 15% (n=34) with a headache, and 12% (n=28) with anosmia. Of the 110 symptomatic cases from Saudi Arabia, the most common 3 symptoms were fever (n=65, 59%), anosmia (n=46, 42%), and headache (n=42, 38%). Conclusions: This study identified the most common symptoms of COVID-19 from tweets in Arabic. These symptoms can be further analyzed in clinical settings and may be incorporated into a real-time COVID-19 risk estimator.