Essential interpretations of bioinformatics in COVID-19 pandemic

Real-time infectious disease endurance indicator system for scientific decisions using machine learning and rapid data processing

The SARS-CoV-2 virus, which induces an acute respiratory illness commonly referred to as COVID-19, had been designated as a pandemic by the World Health Organization due to its highly infectious nature and the associated public health risks it poses globally. Identifying the critical factors for predicting mortality is essential for improving patient therapy. Unlike other data types, such as computed tomography scans, x-radiation, and ultrasounds, basic blood test results are widely accessible and can aid in predicting mortality. The present research advocates the utilization of machine learning (ML) methodologies for predicting the likelihood of infectious disease like COVID-19 mortality by leveraging blood test data. Age, LDH (lactate dehydrogenase), lymphocytes, neutrophils, and hs-CRP (high-sensitivity C-reactive protein) are five extremely potent characteristics that, when combined, can accurately predict mortality in 96% of cases. By combining XGBoost feature importance with neural network classification, the optimal approach can predict mortality with exceptional accuracy from infectious disease, along with achieving a precision rate of 90% up to 16 days before the event. The studies suggested model's excellent predictive performance and practicality were confirmed through testing with three instances that depended on the days to the outcome. By carefully analyzing and identifying patterns in these significant biomarkers insightful information has been obtained for simple application. This study offers potential remedies that could accelerate decision-making for targeted medical treatments within healthcare systems, utilizing a timely, accurate, and reliable method.

COVID Variants, Villain and Victory: A Bioinformatics Perspective

The SARS-CoV-2 virus, a novel member of the Coronaviridae family, is responsible for the viral infection known as Coronavirus Disease 2019 (COVID-19). In response to the urgent and critical need for rapid detection, diagnosis, analysis, interpretation, and treatment of COVID-19, a wide variety of bioinformatics tools have been developed. Given the virulence of SARS-CoV-2, it is crucial to explore the pathophysiology of the virus. We intend to examine how bioinformatics, in conjunction with next-generation sequencing techniques, can be leveraged to improve current diagnostic tools and streamline vaccine development for emerging SARS-CoV-2 variants. We also emphasize how bioinformatics, in general, can contribute to critical areas of biomedicine, including clinical diagnostics, SARS-CoV-2 genomic surveillance and its evolution, identification of potential drug targets, and development of therapeutic strategies. Currently, state-of-the-art bioinformatics tools have helped overcome technical obstacles with respect to genomic surveillance and have assisted in rapid detection, diagnosis, and delivering precise treatment to individuals on time.

Johann Gregor Mendel: the victory of statistics over human imagination

In 2022, we celebrated 200 years since the birth of Johann Gregor Mendel. Although his contributions to science went unrecognized during his lifetime, Mendel not only described the principles of monogenic inheritance but also pioneered the modern way of doing science based on precise experimental data acquisition and evaluation. Novel statistical and algorithmic approaches are now at the center of scientific work, showing that work that is considered marginal in one era can become a mainstream research approach in the next era. The onset of data-driven science caused a shift from hypothesis-testing to hypothesis-generating approaches in science. Mendel is remembered here as a promoter of this approach, and the benefits of big data and statistical approaches are discussed.

Analysis of Persian Bioinformatics Research with Topic Modeling

Purpose

As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively.

Conclusion

The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other.

Modern drug discovery applications for the identification of novel candidates for COVID-19 infections

In early December 2019, a large pneumonia epidemic occurred in Wuhan, China. The World Health Organization is concerned about the outbreak of another coronavirus with the powerful, rapid, and contagious transmission. Anyone with minor symptoms like fever and cough or travel history to contaminated places might be suspected of having COVID-19. COVID-19 therapy focuses on treating the disease's symptoms. So far, no such therapeutic molecule has been shown effective in treating this condition. So the treatment is mostly supportive and plasma. Globally, numerous studies and researchers have recently started fighting this virus. Vaccines and chemical compounds are also being investigated against infection. COVID-19 was successfully diagnosed using RNA detection and very sensitive RT-PCR (reverse transcription-polymerase chain reaction). The evolution of particular vaccinations is required to reduce illness severity and spread. Numerous computational analyses and molecular docking have predicted various target compounds that might stop this condition. This paper examines the main characteristics of coronavirus and the computational analyses necessary to avoid infection.

Prediction of the Effects of Variants and Differential Expression of Key Host Genes ACE2, TMPRSS2, and FURIN in SARS-CoV-2 Pathogenesis: An In Silico Approach

A new strain of the beta coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is solely responsible for the ongoing coronavirus disease 2019 (COVID-19) pandemic. Although several studies suggest that the spike protein of this virus interacts with the cell surface receptor, angiotensin-converting enzyme 2 (ACE2), and is subsequently cleaved by TMPRSS2 and FURIN to enter into the host cell, conclusive insight about the interaction pattern of the variants of these proteins is still lacking. Thus, in this study, we analyzed the functional conjugation among the spike protein, ACE2, TMPRSS2, and FURIN in viral pathogenesis as well as the effects of the mutations of the proteins through the implementation of several bioinformatics approaches. Analysis of the intermolecular interactions revealed that T27A (ACE2), G476S (receptor-binding domain [RBD] of the spike protein), C297T (TMPRSS2), and P812S (cleavage site for TMPRSS2) coding variants may render resistance in viral infection, whereas Q493L (RBD), S477I (RBD), P681R (cleavage site for FURIN), and P683W (cleavage site for FURIN) may lead to increase viral infection. Genotype-specific expression analysis predicted several genetic variants of ACE2 (rs2158082, rs2106806, rs4830971, and rs4830972), TMPRSS2 (rs458213, rs468444, rs4290734, and rs6517666), and FURIN (rs78164913 and rs79742014) that significantly alter their normal expression which might affect the viral spread. Furthermore, we also found that ACE2, TMPRSS2, and FURIN proteins are functionally co-related with each other, and several genes are highly co-expressed with them, which might be involved in viral pathogenesis. This study will thus help in future genomics and proteomics studies of SARS-CoV-2 and will provide an opportunity to understand the underlying molecular mechanism during SARS-CoV-2 pathogenesis.

Bioinformatics Accelerates the Major Tetrad: A Real Boost for the Pharmaceutical Industry

With advanced technology and its development, bioinformatics is one of the avant-garde fields that has managed to make amazing progress in the pharmaceutical-medical field by modeling the infrastructural dimensions of healthcare and integrating computing tools in drug innovation, facilitating prevention, detection/more accurate diagnosis, and treatment of disorders, while saving time and money. By association, bioinformatics and pharmacovigilance promoted both sample analyzes and interpretation of drug side effects, also focusing on drug discovery and development (DDD), in which systems biology, a personalized approach, and drug repositioning were considered together with translational medicine. The role of bioinformatics has been highlighted in DDD, proteomics, genetics, modeling, miRNA discovery and assessment, and clinical genome sequencing. The authors have collated significant data from the most known online databases and publishers, also narrowing the diversified applications, in order to target four major areas (tetrad): DDD, anti-microbial research, genomic sequencing, and miRNA research and its significance in the management of current pandemic context. Our analysis aims to provide optimal data in the field by stratification of the information related to the published data in key sectors and to capture the attention of researchers interested in bioinformatics, a field that has succeeded in advancing the healthcare paradigm by introducing developing techniques and multiple database platforms, addressed in the manuscript.