Volume 18, Issue 1 - Special Issue: COVID; Guest Editor: Jens Allmer

COVID-19 pandemic has flooded all triage stations, making it difficult to carefully select those most likely infected. Data on total patients tested, infected, and hospitalized is fragmentary making it difficult to easily select those most likely to be infected. The Israeli Ministry of Health made public its registry of immediate clinical data and the respective status of infected/not infected for all viral DNA tests performed up to Apr. 18th, 2020 including almost 120,000 tests. We used a machine-learning algorithm to find out which immediate clinical elements mattered the most in identifying the true status of the tested persons including age or gender matter, to enable future better allocation of surveillance policy for those belonging to high-risk groups. In addition to the analyses applied on the first batch of the available data (Apr. 11th), we further tested the algorithm on the independent second batch (Apr. 12th to 18th). Fever, cough and headache were the most diagnostic, differing in degree of importance in different subgroups. Higher percentage of men were found positive (9.3 vs. 7.3%), but gender did not matter for the clinical presentation. The prediction power of the model was high, with accuracy of 0.84 and area under the curve 0.92. We provide a hand-held short checklist with verbal description of importance for the leading symptoms, which should expedite the triage and enable proper selection of people for further follow-up.

Outbreaks of COVID-19 caused by the novel coronavirus SARS-CoV-2 is still a threat to global human health. In order to understand the biology of SARS-CoV-2 and developing drug against COVID-19, a vast amount of genomic, proteomic, interatomic, and clinical data is being generated, and the bioinformatics researchers produced databases, webservers and tools to gather those publicly available data and provide an opportunity of analyzing such data. However, these bioinformatics resources are scattered and researchers need to find them from different resources discretely. To facilitate researchers in finding the resources in one frame, we have developed an integrated web portal called OverCOVID (http://bis.zju.edu.cn/overcovid/). The publicly available webservers, databases and tools associated with SARS-CoV-2 have been incorporated in the resource page. In addition, a network view of the resources is provided to display the scope of the research. Other information like SARS-CoV-2 strains is visualized and various layers of interaction resources is listed in distinct pages of the web portal. As an integrative web portal, the OverCOVID will help the scientist to search the resources and accelerate the clinical research of SARS-CoV-2.

SARS-CoV-2 has spread worldwide and caused social, economic, and health turmoil. The first genome assembly of SARS-CoV-2 was produced in Wuhan, and it is widely used as a reference. Subsequently, more than a hundred additional SARS-CoV-2 genomes have been sequenced. While the genomes appear to be mostly identical, there are variations. Therefore, an alignment of all available genomes and the derived consensus sequence could be used as a reference, better serving the science community. Variations are significant, but representing them in a genome browser can become, especially if their sequences are largely identical. Here we summarize the variation in one track. Other information not currently found in genome browsers for SARS-CoV-2, such as predicted miRNAs and predicted TRS as well as secondary structure information, were also added as tracks to the consensus genome. We believe that a genome browser based on the consensus sequence is better suited when considering worldwide effects and can become a valuable resource in the combating of COVID-19. The genome browser is available at http://cov.iaba.online.

The pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected millions of people and claimed thousands of lives. Starting in China, it is arguably the most precipitous global health calamity of modern times. The entire world has rocked back to fight against the disease and the COVID-19 vaccine is the prime weapon. Even though the conventional vaccine development pipeline usually takes more than a decade, the escalating daily death rates due to COVID-19 infections have resulted in the development of fast-track strategies to bring in the vaccine under a year’s time. Governments, companies, and universities have networked to pool resources and have come up with a number of vaccine candidates. Also, international consortia have emerged to address the distribution of successful candidates. Herein, we summarize these unprecedented developments in vaccine science and discuss the types of COVID-19 vaccines, their developmental strategies, and their roles as well as their limitations.

Different types of noncoding RNAs like microRNAs (miRNAs) and circular RNAs (circRNAs) have been shown to take part in various cellular processes including post-transcriptional gene regulation during infection. MiRNAs are expressed by more than 200 organisms ranging from viruses to higher eukaryotes. Since miRNAs seem to be involved in host–pathogen interactions, many studies attempted to identify whether human miRNAs could target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNAs as an antiviral defence mechanism. In this work, a machine learning based miRNA analysis workflow was developed to predict differential expression patterns of human miRNAs during SARS-CoV-2 infection. In order to obtain the graphical representation of miRNA hairpins, 36 features were defined based on the secondary structures. Moreover, potential targeting interactions between human circRNAs and miRNAs as well as human miRNAs and viral mRNAs were investigated.

To date, many proteins generated by large-scale genome sequencing projects are still uncharacterized and subject to intensive investigations by both experimental and computational means. Knowledge of protein subcellular localization (SCL) is of key importance for protein function elucidation. However, it remains a challenging task, especially for multiple sites proteins known to shuttle between cell compartments to perform their proper biological functions and proteins which do not have significant homology to proteins of known subcellular locations. Due to their low-cost and reasonable accuracy, machine learning-based methods have gained much attention in this context with the availability of a plethora of biological databases and annotated proteins for analysis and benchmarking. Various predictive models have been proposed to tackle the SCL problem, using different protein sequence features pertaining to the subcellular localization, however, the overwhelming majority of them focuses on single localization and cover very limited cellular locations. The prediction was basically established on sorting signals, amino acids compositions, and homology. To improve the prediction quality, focus is actually on knowledge information extracted from annotation databases, such as protein–protein interactions and Gene Ontology (GO) functional domains annotation which has been recently a widely adopted and essential information for learning systems. To deal with such problem, in the present study, we considered SCL prediction task as a multi-label learning problem and tried to label both single site and multiple sites unannotated bacterial protein sequences by mining proteins homology relationships using both GO terms of protein homologs and PSI-BLAST profiles. The experiments using 5-fold cross-validation tests on the benchmark datasets showed a significant improvement on the results obtained by the proposed consensus multi-label prediction model which discriminates six compartments for Gram-negative and five compartments for Gram-positive bacterial proteins.

As stated by World Health Organization (WHO) report, 246 million individuals have suffered with diabetes disease over worldwide and it is anticipated that by 2025 this estimation can cross 380 million. So, the proper and quick diagnosis of this disease is turned into a significant challenge for the machine learning researchers. This paper aims to design a robust model for diagnosis of diabetes using a hybrid approach of Chaotic-Jaya (CJaya) algorithm with Extreme Learning Machine (ELM), which is named as CJaya-ELM. In this paper, Jaya algorithm with Chaotic learning approach is used to optimize the random parameters of ELM classifier. Here, to assess the efficacy of the designed model, Pima Indian diabetes dataset is considered. Here, the designed model CJaya-ELM, has been compared with basic ELM, Teaching Learning Based Optimization algorithm (TLBO) optimized ELM (TLBO-ELM), Multi-Layer Perceptron (MLP), Jaya algorithm optimized MLP (Jaya-MLP), TLBO algorithm optimized MLP (TLBO-MLP) and CJaya algorithm optimized MLP models. CJaya-ELM model resulted in the highest testing accuracy of 0.9687, sensitivity of 1, specificity of 0.9688 with 0.9782 area under curve (AUC) value. Results reveal that CJaya-ELM model effectively classifies both the positive and negative samples of Pima and outperforms the competitors.