Bioinformatics resources facilitate understanding and harnessing clinical research of SARS-CoV-2

Ahsan, Asif; Liu, Yongjing; Cong, Feng; Zhou, Yincong; Ma, Guangyuan; Bai, Youhuang; Chen, Ming

doi:10.1093/bib/bbaa416

Cited by 18 publications

(10 citation statements)

References 65 publications

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“…Our experiments and obtained results show that AF methods provide an efficient framework for the analysis of COVID-19 genome sequences to get useful insights about this virus. As discussed in [24], including more COVID-19 data and applying more algorithms will allow researchers to obtain results that may guide the clinical research for this pandemic.…”

Section: Discussionmentioning

confidence: 99%

COVID-19 Genome Analysis Using Alignment-Free Methods

Nawaz

Fournier‐Viger

Niu

et al. 2021

Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices

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Examining the genome sequences of the novel coronavirus (COVID-19) strains is critical to properly understand this disease and its functionalities. In bioinformatics, alignment-free (AF) sequence analysis methods offer a natural framework to investigate and understand the patterns and inherent properties of biological sequences. Thus, AF methods are used in this paper for the analysis and comparison of COVID-19 genome sequences. First, frequent patterns of nucleotide base(s) in COVID-19 genome sequences are extracted. Second, the similarity/dissimilarity between COVID-19 genome sequences are measured with different AF methods. This allows to compare sequences and evaluate the performance of various distance measures employed in AF methods. Lastly, the phylogeny for the COVID-19 genome sequences are constructed with various AF methods as well as the consensus tree that shows the level of support (agreement) among phylogenetic trees built by various AF methods. Obtained results show that AF methods can be used efficiently for the analysis of COVID-19 genome sequences.

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Section: Discussionmentioning

confidence: 99%

COVID-19 Genome Analysis Using Alignment-Free Methods

Nawaz

Fournier‐Viger

Niu

et al. 2021

Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices

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“…The specific task of drug development and clinical trials and sales will take more time, and all these can be narrowed by AI and ML programmes. Hence, it needs programmers with deep knowledge of thinking abilities of AI- and ML-based techniques and skilled data scientists and also a transparent company business target that can be adapted to use the full potential of AI and ML platforms [ 184 ].…”

Section: Challenges and Scopementioning

confidence: 99%

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

Selvaraj

Chandra

2021

Mol Divers

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The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. Graphic abstract From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

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“…The Novel Coronavirus Outbreak (COVID-19) has rapidly spread across the globe since around 2020 [1] , [2] , bringing a heavy blow to countries worldwide by causing large numbers of casualties, interrupting people’s lives and social orders as well as inciting international conflicts and compromising economic growth [3] , [4] . In April 2020, the World Health Organization (WHO) defined the COVID-19 epidemic as a “pandemic of acute respiratory infectious disease” [5] , [6] , which drawed people’s attention worldwide to biosafety-relevant fields.…”

Section: Introductionmentioning

confidence: 99%

Biosafety chemistry and biosafety materials: A new perspective to solve biosafety problems

Ding

Zhou

et al. 2022

Biosafety and Health

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The Novel Coronavirus Disease (COVID-19) has rapidly swept around the globe since its first emergence near 2020. However, people have failed to fully understand its origin or mutation. Defined as an international biosafety incident, COVID-19 has again encouraged worldwide attention to reconsider the importance of biosafety due to the adverse impact on personal well-being and social stability. Most countries have already taken measures to advocate progress in biosafety-relevant research, aiming to prevent and solve biosafety problems with more advanced techniques and products. Herein, we propose a new concept of biosafety chemistry and reiterate the notion of biosafety materials, which refer to the interdisciplinary integration of biosafety and chemistry or materials. Here, we attempt to illustrate the exquisite association that chemistry and material science possess with biosafety fields and we hope to provide a pragmatic perspective on approaches to utilize the knowledge of these two subjects to handle specific biosafety issues such as detection and disinfection of pathogenic microorganisms, personal and collective protective equipment, vaccine adjuvants and specific drugs, preservation of biogenetic resources for human, animals, and plants. In addition, we hope to convey and promote the idea of multidisciplinary cooperation to strengthen biosafety research and development of relevant products for establishing possibly specific majors to defend national security in the future.

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Bioinformatics resources facilitate understanding and harnessing clinical research of SARS-CoV-2

Cited by 18 publications

References 65 publications

COVID-19 Genome Analysis Using Alignment-Free Methods

COVID-19 Genome Analysis Using Alignment-Free Methods

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

Biosafety chemistry and biosafety materials: A new perspective to solve biosafety problems

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