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Application of Computational and Bioinformatics Techniques in Drug Repurposing for Effective Development of Potential Drug Candidate for the Management of COVID-19
Published in Hajiya Mairo Inuwa, Ifeoma Maureen Ezeonu, Charles Oluwaseun Adetunji, Emmanuel Olufemi Ekundayo, Abubakar Gidado, Abdulrazak B. Ibrahim, Benjamin Ewa Ubi, Medical Biotechnology, Biopharmaceutics, Forensic Science and Bioinformatics, 2022
Charles Oluwaseun Adetunji, Olaniyan Tope Olugbemi, Muhammad Akram, Umme Laila, Michael Olugbenga Samuel, Ayomide Michael Oshinjo, Juliana Bunmi Adetunji, Gloria E. Okotie, Nwadiuto (Diuto) Esiobu, Omotayo Opemipo Oyedara, Folasade Muibat Adeyemi
Bioinformatics is a biological science that deals with statistics, mathematics, information engineering, and computer science. It has been found very helpful in understanding, integrating, and analyzing data using different software and databases. It is used widely in research and in many other fields. It is applied for the purpose of developing diagnostic kits for SARS-CoV-2, study viral mutation, and predict drug targets, which are relevant for COVID-19 treatment. Biological databases and software tools are two important aspects of bioinformatics. Genomic and Proteomic information on SARS-CoV-2 or COVID-19 archived in several databases such as PubChem, GenBank, UniProt, PubMed, among others, can be accessed and processed using bioinformatics tools to generate meaningful information that can be applied in the treatment and management of COVID-19.
Machine Learning for Solving a Plethora of Internet of Things Problems
Published in Kamal Kumar Sharma, Akhil Gupta, Bandana Sharma, Suman Lata Tripathi, Intelligent Communication and Automation Systems, 2021
Sparsh Sharma, Abrar Ahmed, Mohd Naseem, Surbhi Sharma
Bioinformatics is a field of study that combines computer science (CS), statistics, mathematics and engineering for analyzing and interpreting biological data [72]. In bioinformatics, one of the most challenging aspects is data. Due to the large volume of patient data, sometimes it's difficult to implement this data as a knowledge for the machine. Working with clean and meaningful data, the machine model accurately predicts disease and recommends prescriptions based on the data [73]. ML covers various applications of bioinformatics, such as genomics, proteomics, microarray and system biology. Machine learning is applied for solving various bioinformatics problems: (1) gene finding, (2) gene expression where clustering algorithms are used, (3) population stratification in which PCA, multi-dimension scaling (MDS) and manifold learning techniques have been adopted and (4) DNA sequencing. Konstantina Kourou et al. [74] applied machine learning for determining cancer prognosis.
Trends in Biotechnology
Published in Firdos Alam Khan, Biotechnology Fundamentals, 2020
A protein domain is a conserved part of a given protein sequence and (tertiary) structure that can evolve, function, and exist independently of the rest of the protein chain. Each domain forms a compact three-dimensional structure and often can be independently stable and folded. Many proteins consist of several structural domains. One domain may appear in a variety of different proteins. Molecular evolution uses domains as building blocks and these may be recombined in different arrangements to create proteins with different functions. The shortest domains such as zinc fingers are stabilized by metal ions or disulfide bridges. Domains often form functional units, such as the calcium-binding EF hand domain of calmodulin. Because they are independently stable, domains can be swapped by genetic engineering between one protein and another to make chimeric proteins. The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other methods, however, is its focus on developing and applying computationally intensive techniques such as pattern recognition, data mining, machine learning algorithms, and visualization to achieve this goal. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein–protein interactions, genome-wide association studies, the modeling of evolution and cell division/mitosis. Based on the application of biological data, bioinformatics is classified into various sub-types, which are discussed as below:
Bayesian regularized neural network decision tree ensemble model for genomic data classification
Published in Applied Artificial Intelligence, 2018
Bioinformatics is one of the interdisciplinary fields which develop algorithms and methods for analyzing biological data to produce meaningful information (Christopher et al. 2009). During past few decades, in the area of feature-based classification, binary classification problems have been studied extensively but multi category classification has its own importance. Research also shows that multi-class classification is much complex than binary classification and accuracy of classifier drops significantly as the number of classes increases. In literature, various multi-class classifier algorithms have been devised by researchers such as decision tree (DT) (Asria et al. 2016; Patil, Joshi, and Toshniwal 2010), artificial neural network (ANN) (Thein and Tun 2015), support vector machine (SVM) (Bazazeh and Shubair 2017; Barale and Shirke 2016; Megha. and Pareek 2016; Salama, Abdelhalim, and Zeid 2012), and Bayesian regularized artificial network (BRNN) (Burden and Winkler 2008).
Characterization of experimental complex fungal bioaerosols: Impact of analytical method on fungal composition measurements
Published in Aerosol Science and Technology, 2019
Jodelle Degois, Xavier Simon, Cyril Bontemps, Pierre Leblond, Philippe Duquenne
The measurement of bioaerosols has received a lot of attention during last decades, and a wide variety of methods has been proposed for that purpose. Indeed, airborne microorganisms and compounds can be collected using different sampling methods such as filtration, impaction, impingment and electrostatic samplers that were previously investigated and reviewed (Reponen 2011; Reponen et al. 2011; Haig et al. 2016). The analysis of bioaerosol samples is performed using culture-based and culture-independent methods including chemical, biochemical, microscopic and molecular assays (Reponen et al. 2011; Duquenne, Marchand, and Duchaine 2013; Mbareche et al. 2017). Various methods are used to study biodiversity, and include culture-dependent methods with morphological, microscopic and biochemical identification, as well as molecular methods such as high-throughput sequencing (HTS). Each of these methods has specific advantages and limitations that have been reviewed (Yoo et al. 2017; Duquenne 2018). Culture-based methods are known to underestimate the true biodiversity of environmental samples for a number of reasons, the main one of which is the viable but non-culturable state of most of microorganisms, selective pressure during culture conditions, slow growth rates for certain microbial strains, and difficulties identifying other ones (Amann, Ludwig, and Schleifer 1995; Heidelberg et al. 1997; Yoo et al. 2017). The advantages of culture-based methods include identification at the species level, based on macroscopic and microscopic observations at biochemical, chemical and bio-molecular tests. In addition, microbial isolates can be obtained for further study (Duquenne 2018). In contrast, molecular-based methods such as HTS avoid the need for a culture step, consisting rather of several analytical steps (DNA extraction, library preparation, sequencing and bioinformatics analysis). These multiple steps may introduce biases. For example, species may be favorably detected due to the preferential extraction of DNA from their cells (Haugland, Heckman and Wymer 1999), or DNA amplification of some fungi may be easier due to the primers used to prepare the library (Bellemain et al. 2010). A number of bioinformatics tools are available to analyze sequencing data, such as mothur (Schloss et al. 2009), QIIME (Caporaso et al. 2010) and FHiTINGS (Dannemiller et al. 2014). Sequence assignment may also be performed by a range of methods or using databases. All these different options may lead to differences in biodiversity results.