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1.
Indian J Pharmacol ; 54(1): 58-62, 2022.
Article in English | MEDLINE | ID: covidwho-1766047

ABSTRACT

The decline in human performance with age at 5000 m, an athletic event requiring high VO2 max, is remarkably precise, and unavoidable, and related to entropy, even at an individual level. Women and men show an identical age-related decline, up to ~100 years old. The precision of the decline shows the limitations for therapy of aging. Mortality incidence for COVID-19 shows a similar relationship. We propose that initial VO2 max has a critical role in COVID sensitivity because of the direct relationship of disease severity with oxygen use, and the parallel decline in aging.


Subject(s)
COVID-19 , Sports , Aged, 80 and over , Aging , Entropy , Female , Humans , Male , Oxygen Consumption
2.
PLoS One ; 16(12): e0261307, 2021.
Article in English | MEDLINE | ID: covidwho-1598199

ABSTRACT

Medical images commonly exhibit multiple abnormalities. Predicting them requires multi-class classifiers whose training and desired reliable performance can be affected by a combination of factors, such as, dataset size, data source, distribution, and the loss function used to train deep neural networks. Currently, the cross-entropy loss remains the de-facto loss function for training deep learning classifiers. This loss function, however, asserts equal learning from all classes, leading to a bias toward the majority class. Although the choice of the loss function impacts model performance, to the best of our knowledge, we observed that no literature exists that performs a comprehensive analysis and selection of an appropriate loss function toward the classification task under study. In this work, we benchmark various state-of-the-art loss functions, critically analyze model performance, and propose improved loss functions for a multi-class classification task. We select a pediatric chest X-ray (CXR) dataset that includes images with no abnormality (normal), and those exhibiting manifestations consistent with bacterial and viral pneumonia. We construct prediction-level and model-level ensembles to improve classification performance. Our results show that compared to the individual models and the state-of-the-art literature, the weighted averaging of the predictions for top-3 and top-5 model-level ensembles delivered significantly superior classification performance (p < 0.05) in terms of MCC (0.9068, 95% confidence interval (0.8839, 0.9297)) metric. Finally, we performed localization studies to interpret model behavior and confirm that the individual models and ensembles learned task-specific features and highlighted disease-specific regions of interest. The code is available at https://github.com/sivaramakrishnan-rajaraman/multiloss_ensemble_models.


Subject(s)
Algorithms , Diagnostic Imaging , Image Processing, Computer-Assisted/classification , Area Under Curve , Entropy , Humans , Lung/diagnostic imaging , ROC Curve , Thorax/diagnostic imaging , X-Rays
3.
Infect Genet Evol ; 97: 105154, 2022 01.
Article in English | MEDLINE | ID: covidwho-1521408

ABSTRACT

The pandemic of COVID-19 has been haunting us for almost the past two years. Although, the vaccination drive is in full swing throughout the world, different mutations of the SARS-CoV-2 virus are making it very difficult to put an end to the pandemic. The second wave in India, one of the worst sufferers of this pandemic, can be mainly attributed to the Delta variant i.e. B.1.617.2. Thus, it is very important to analyse and understand the mutational trajectory of SARS-CoV-2 through the study of the 26 virus proteins. In this regard, more than 17,000 protein sequences of Indian SARS-CoV-2 genomes are analysed using entropy-based approach in order to find the monthly mutational trajectory. Furthermore, Hellinger distance is also used to show the difference of the mutation events between the consecutive months for each of the 26 SARS-CoV-2 protein. The results show that the mutation rates and the mutation events of the viral proteins though changing in the initial months, start stabilizing later on for mainly the four structural proteins while the non-structural proteins mostly exhibit a more constant trend. As a consequence, it can be inferred that the evolution of the new mutative configurations will eventually reduce.


Subject(s)
COVID-19/epidemiology , Genome, Viral , Mutation Rate , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/genetics , Viral Nonstructural Proteins/genetics , Viral Structural Proteins/genetics , COVID-19/virology , Entropy , Epidemiological Monitoring , Evolution, Molecular , Gene Expression , Humans , India/epidemiology , Phylogeny , SARS-CoV-2/classification , SARS-CoV-2/pathogenicity , Spike Glycoprotein, Coronavirus/metabolism , Viral Nonstructural Proteins/classification , Viral Nonstructural Proteins/metabolism , Viral Structural Proteins/classification , Viral Structural Proteins/metabolism
4.
J Chem Inf Model ; 61(11): 5320-5326, 2021 11 22.
Article in English | MEDLINE | ID: covidwho-1493001

ABSTRACT

We describe a step-by-step protocol for the computation of absolute dissociation free energy with GROMACS code and PLUMED library, which exploits a combination of advanced sampling techniques and nonequilibrium alchemical methodologies. The computational protocol has been automated through an open source Python middleware (HPC_Drug) which allows one to set up the GROMACS/PLUMED input files for execution on high performing computing facilities. The proposed protocol, by exploiting its inherent parallelism and the power of the GROMACS code on graphical processing units, has the potential to afford accurate and precise estimates of the dissociation constants in drug-receptor systems described at the atomistic level. The procedure has been applied to the calculation of the absolute dissociation free energy of PF-07321332, an oral antiviral proposed by Pfizer, with the main protease (3CLpro) of SARS-CoV-2.


Subject(s)
COVID-19 , Molecular Dynamics Simulation , Antiviral Agents , Entropy , Lactams , Leucine , Nitriles , Proline , SARS-CoV-2
5.
Sensors (Basel) ; 21(21)2021 Nov 02.
Article in English | MEDLINE | ID: covidwho-1488707

ABSTRACT

In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 disease caused many deaths. Computer vision researchers support medical doctors by employing deep learning techniques on medical images to diagnose COVID-19 patients. Various methods were proposed for COVID-19 case classification. A new automated technique is proposed using parallel fusion and optimization of deep learning models. The proposed technique starts with a contrast enhancement using a combination of top-hat and Wiener filters. Two pre-trained deep learning models (AlexNet and VGG16) are employed and fine-tuned according to target classes (COVID-19 and healthy). Features are extracted and fused using a parallel fusion approach-parallel positive correlation. Optimal features are selected using the entropy-controlled firefly optimization method. The selected features are classified using machine learning classifiers such as multiclass support vector machine (MC-SVM). Experiments were carried out using the Radiopaedia database and achieved an accuracy of 98%. Moreover, a detailed analysis is conducted and shows the improved performance of the proposed scheme.


Subject(s)
COVID-19 , Deep Learning , Animals , Artificial Intelligence , Entropy , Fireflies , Humans , SARS-CoV-2 , Tomography, X-Ray Computed
6.
J Comput Biol ; 28(11): 1113-1129, 2021 11.
Article in English | MEDLINE | ID: covidwho-1483349

ABSTRACT

The availability of millions of SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus-2) sequences in public databases such as GISAID (Global Initiative on Sharing All Influenza Data) and EMBL-EBI (European Molecular Biology Laboratory-European Bioinformatics Institute) (the United Kingdom) allows a detailed study of the evolution, genomic diversity, and dynamics of a virus such as never before. Here, we identify novel variants and subtypes of SARS-CoV-2 by clustering sequences in adapting methods originally designed for haplotyping intrahost viral populations. We asses our results using clustering entropy-the first time it has been used in this context. Our clustering approach reaches lower entropies compared with other methods, and we are able to boost this even further through gap filling and Monte Carlo-based entropy minimization. Moreover, our method clearly identifies the well-known Alpha variant in the U.K. and GISAID data sets, and is also able to detect the much less represented (<1% of the sequences) Beta (South Africa), Epsilon (California), and Gamma and Zeta (Brazil) variants in the GISAID data set. Finally, we show that each variant identified has high selective fitness, based on the growth rate of its cluster over time. This demonstrates that our clustering approach is a viable alternative for detecting even rare subtypes in very large data sets.


Subject(s)
Cluster Analysis , Computational Biology/methods , Brazil , Databases, Genetic , Entropy , Humans , Monte Carlo Method , South Africa , United Kingdom , United States
7.
J Chem Inf Model ; 61(9): 4733-4744, 2021 09 27.
Article in English | MEDLINE | ID: covidwho-1467035

ABSTRACT

Covalent inhibitors are assuming central importance in drug discovery projects, especially in this pandemic scenario. Many research groups have focused their attention on inhibiting viral proteases or human proteases such as cathepsin L (hCatL). The inhibition of these critical enzymes may impair viral replication. However, molecular modeling of covalent ligands is challenging since covalent and noncovalent ligand-bound states must be considered in the binding process. In this work, we evaluated the suitability of free energy perturbation (FEP) calculations as a tool for predicting the binding affinity of reversible covalent inhibitors of hCatL. Our strategy relies on the relative free energy calculated for both covalent and noncovalent complexes and the free energy changes have been compared with experimental data for eight nitrile-based inhibitors, including three new inhibitors of hCatL. Our results demonstrate that the covalent complex can be employed to properly rank the inhibitors. Nevertheless, a comparison of the free energy changes in both noncovalent and covalent states is valuable to interpret the effect triggered by the formation of the covalent bond on the interactions played by functional groups distant from the warhead. Overall, FEP can be employed as a powerful predictor tool in developing and understanding the activity of reversible covalent inhibitors.


Subject(s)
Drug Discovery , Entropy , Humans , Ligands , Models, Molecular , Thermodynamics
8.
Int J Biol Macromol ; 191: 934-955, 2021 Nov 30.
Article in English | MEDLINE | ID: covidwho-1433283

ABSTRACT

The spike (S) protein is a critical determinant of the infectivity and antigenicity of SARS-CoV-2. Several mutations in the S protein of SARS-CoV-2 have already been detected, and their effect in immune system evasion and enhanced transmission as a cause of increased morbidity and mortality are being investigated. From pathogenic and epidemiological perspectives, S proteins are of prime interest to researchers. This study focused on the unique variants of S proteins from six continents: Asia, Africa, Europe, Oceania, South America, and North America. In comparison to the other five continents, Africa had the highest percentage of unique S proteins (29.1%). The phylogenetic relationship implies that unique S proteins from North America are significantly different from those of the other five continents. They are most likely to spread to the other geographic locations through international travel or naturally by emerging mutations. It is suggested that restriction of international travel should be considered, and massive vaccination as an utmost measure to combat the spread of the COVID-19 pandemic. It is also further suggested that the efficacy of existing vaccines and future vaccine development must be reviewed with careful scrutiny, and if needed, further re-engineered based on requirements dictated by new emerging S protein variants.


Subject(s)
COVID-19/epidemiology , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/genetics , Amino Acid Substitution/immunology , COVID-19/immunology , Entropy , Humans , Isoelectric Point , Mutation/immunology , Pandemics/statistics & numerical data , Phylogeny , Spike Glycoprotein, Coronavirus/immunology
9.
Sci Rep ; 11(1): 15972, 2021 08 05.
Article in English | MEDLINE | ID: covidwho-1345575

ABSTRACT

India became one of the most COVID-19 affected countries with more than 4 million infected cases and 71,000 deaths by September 2020. We studied the temporal dynamics and geographic distribution of SARS-CoV-2 subtypes in India. Moreover, we analysed the RGD motif and D614G mutation in the spike protein of SARS-CoV-2. We used a previously proposed viral subtyping method based upon informative subtype markers (ISMs). The ISMs were identified on the basis of information entropy using 94,515 genome sequences of SARS-CoV-2 available publicly at the Global Initiative on Sharing All Influenza Data (GISAID). We identified 11 distinct positions in the SARS-CoV-2 genomes for defining ISMs resulting in 798 unique ISMs. The most abundant ISM in India was transferred from European countries. In contrast, the second most abundant ISM in India was found to be transferred via Australia. Moreover, the eastern regions in India were infected by the ISM most abundant in China due to geographical linkage. Our analysis confirmed higher rates of new cases in the countries abundant with S-G614 strain compared to countries with abundant S-D614 strain. In India, overall S-G614 was most prevalent compared to S-D614, except a few regions including New Delhi, Bihar, and Rajasthan.


Subject(s)
COVID-19/transmission , SARS-CoV-2/genetics , COVID-19/diagnosis , COVID-19/epidemiology , Entropy , Genome, Viral , Humans , India/epidemiology , Mutation , Phylogeny , SARS-CoV-2/isolation & purification , Spike Glycoprotein, Coronavirus/genetics
10.
Int J Mol Sci ; 22(13)2021 Jun 26.
Article in English | MEDLINE | ID: covidwho-1288896

ABSTRACT

Herein, we have generated ssRNA aptamers to inhibit SARS-CoV-2 Mpro, a protease necessary for the SARS-CoV-2 coronavirus replication. Because there is no aptamer 3D structure currently available in the databanks for this protein, first, we modeled an ssRNA aptamer using an entropic fragment-based strategy. We refined the initial sequence and 3D structure by using two sequential approaches, consisting of an elitist genetic algorithm and an RNA inverse process. We identified three specific aptamers against SARS-CoV-2 Mpro, called MAptapro, MAptapro-IR1, and MAptapro-IR2, with similar 3D conformations and that fall in the dimerization region of the SARS-CoV-2 Mpro necessary for the enzymatic activity. Through the molecular dynamic simulation and binding free energy calculation, the interaction between the MAptapro-IR1 aptamer and the SARS-CoV-2 Mpro enzyme resulted in the strongest and the highest stable complex; therefore, the ssRNA MAptapro-IR1 aptamer was selected as the best potential candidate for the inhibition of SARS-CoV-2 Mpro and a perspective therapeutic drug for the COVID-19 disease.


Subject(s)
Aptamers, Nucleotide/metabolism , COVID-19/drug therapy , SARS-CoV-2/metabolism , Viral Matrix Proteins/metabolism , Aptamers, Nucleotide/chemistry , Binding Sites , COVID-19/pathology , COVID-19/virology , DNA, Single-Stranded/chemistry , Drug Design , Entropy , Humans , Hydrogen Bonding , Molecular Docking Simulation , Molecular Dynamics Simulation , SARS-CoV-2/isolation & purification , Viral Matrix Proteins/chemistry
11.
Ciênc. Saúde Colet ; 26(4): 1419-1428, abr. 2021. tab
Article in English | WHO COVID, LILACS (Americas) | ID: covidwho-1197442

ABSTRACT

Abstract The present study was an effort to assess the mitigation interventions carried out, so far, by the nations to fight the pandemic COVID-19. The novelty of the study was that it had considered the issue of pandemic mitigation strategy as a decision making problem. The performances of the twenty nations were to be ranked. The problem considered in the study was essentially a Multi-Criteria Decision Analysis (MCDA) problem. The available alternatives were the 20 countries and the 8 traits were the criteria. The Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) was used in the present study. The study used Entropy method for assignment of weights to all the criteria. The performance score obtained in respect of the countries considered in the study and the corresponding ranks indicated the relative performances of the countries in their efforts to mitigate the COVID-19 pandemic. The results show that New Zealand is the best performing country and India is the worst one. Brazil ranked 17th, while the rank of UK was 15. The performance of the USA stood at 18th position.


Resumo O presente estudo foi um esforço para avaliar as intervenções de mitigação realizadas, até o momento, pelas nações para combater a pandemia COVID-19. A novidade do estudo é que considerou a questão da estratégia de mitigação da pandemia como um problema de tomada de decisão. As performances das vinte nações deveriam ser classificadas. O problema considerado no estudo era essencialmente um problema de Análise de Decisão Multi-Critério (MCDA). As alternativas disponíveis eram os 20 países e as 8 características eram os critérios. A Técnica de Similaridade de Preferência de Pedido com a Solução Ideal (TOPSIS) foi utilizada no presente estudo. O estudo utilizou o método da Entropia para atribuição de pesos a todos os critérios. A pontuação de desempenho obtida em relação aos países considerados no estudo e as classificações correspondentes indicaram os desempenhos relativos dos países em seus esforços para mitigar a pandemia COVID-19. Os resultados mostram que a Nova Zelândia é o país com melhor desempenho e a Índia o pior. O Brasil ficou em 17º, enquanto o Reino Unido ficou em 15. O desempenho dos EUA ficou na 18ª posição.


Subject(s)
Humans , Pandemics/prevention & control , COVID-19/prevention & control , United States/epidemiology , Brazil/epidemiology , Decision Support Techniques , Entropy , United Kingdom/epidemiology , India/epidemiology , New Zealand/epidemiology
12.
Molecules ; 26(8)2021 Apr 20.
Article in English | MEDLINE | ID: covidwho-1194692

ABSTRACT

The binding free energy calculation of protein-ligand complexes is necessary for research into virus-host interactions and the relevant applications in drug discovery. However, many current computational methods of such calculations are either inefficient or inaccurate in practice. Utilizing implicit solvent models in the molecular mechanics generalized Born surface area (MM/GBSA) framework allows for efficient calculations without significant loss of accuracy. Here, GBNSR6, a new flavor of the generalized Born model, is employed in the MM/GBSA framework for measuring the binding affinity between SARS-CoV-2 spike protein and the human ACE2 receptor. A computational protocol is developed based on the widely studied Ras-Raf complex, which has similar binding free energy to SARS-CoV-2/ACE2. Two options for representing the dielectric boundary of the complexes are evaluated: one based on the standard Bondi radii and the other based on a newly developed set of atomic radii (OPT1), optimized specifically for protein-ligand binding. Predictions based on the two radii sets provide upper and lower bounds on the experimental references: -14.7(ΔGbindBondi)<-10.6(ΔGbindExp.)<-4.1(ΔGbindOPT1) kcal/mol. The consensus estimates of the two bounds show quantitative agreement with the experiment values. This work also presents a novel truncation method and computational strategies for efficient entropy calculations with normal mode analysis. Interestingly, it is observed that a significant decrease in the number of snapshots does not affect the accuracy of entropy calculation, while it does lower computation time appreciably. The proposed MM/GBSA protocol can be used to study the binding mechanism of new variants of SARS-CoV-2, as well as other relevant structures.


Subject(s)
Angiotensin-Converting Enzyme 2/metabolism , SARS-CoV-2/metabolism , Spike Glycoprotein, Coronavirus/metabolism , Algorithms , Angiotensin-Converting Enzyme 2/chemistry , COVID-19/pathology , COVID-19/virology , Entropy , Humans , Ligands , Molecular Dynamics Simulation , Protein Binding , SARS-CoV-2/isolation & purification , Spike Glycoprotein, Coronavirus/chemistry , raf Kinases/chemistry , raf Kinases/metabolism , ras Proteins/chemistry , ras Proteins/metabolism
13.
Biosens Bioelectron ; 178: 113015, 2021 Apr 15.
Article in English | MEDLINE | ID: covidwho-1039299

ABSTRACT

Dependable, specific and rapid diagnostic methods for severe acute respiratory syndrome ß-coronavirus (SARS-CoV-2) detection are needed to promote public health interventions for coronavirus disease 2019 (COVID-19). Herein, we have established an entropy-driven amplified electrochemiluminescence (ECL) strategy to detect the RNA-dependent RNA polymerase (RdRp) gene of SARS-CoV-2 known as RdRp-COVID which as the target for SARS-CoV-2 plays an essential role in the diagnosis of COVID-19. For the construction of the sensors, DNA tetrahedron (DT) is modified on the surface of the electrode to furnish robust and programmable scaffolds materials, upon which target DNA-participated entropy-driven amplified reaction is efficiently conducted to link the Ru (bpy)32+ modified S3 to the linear ssDNA at the vertex of the tetrahedron and eventually present an "ECL on" state. The rigid tetrahedral structure of the DT probe enhances the ECL intensity and avoids the cross-reactivity between single-stranded DNA, thus increasing the sensitivity of the assays. The enzyme-free entropy-driven reaction prevents the use of expensive enzyme reagents and facilitates the realization of large-scale screening of SARS-CoV-2 patients. Our DT-based ECL sensor has demonstrated significant specificity and high sensitivity for SARS-CoV-2 with a limit of detection (LOD) down to 2.67 fM. Additionally, our operational method has achieved the detection of RdRp-COVID in human serum samples, which supplies a reliable and feasible sensing platform for the clinical bioanalysis.


Subject(s)
Biosensing Techniques/instrumentation , COVID-19 Nucleic Acid Testing/instrumentation , COVID-19/diagnosis , COVID-19/virology , Coronavirus RNA-Dependent RNA Polymerase/genetics , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification , Biosensing Techniques/statistics & numerical data , COVID-19 Nucleic Acid Testing/statistics & numerical data , Coronavirus RNA-Dependent RNA Polymerase/blood , DNA/chemistry , Electrochemical Techniques , Entropy , Genes, Viral , Humans , Limit of Detection , Luminescence , Nucleic Acid Conformation , Pandemics , Sensitivity and Specificity
14.
Biosens Bioelectron ; 176: 112942, 2021 Mar 15.
Article in English | MEDLINE | ID: covidwho-1009324

ABSTRACT

2019 novel coronavirus (2019-nCoV) with strong contagion in the crowd, has ravaged worldwide and severely impacts the human health and epidemic prevention system, by producing a series of significant stress reactions in the body to induce further cytokine storm. Transcription factors (TFs) served as essential DNA binding proteins play an integral role in regulating cytokine storm, and the detection of it in the human coronavirus environment provides especially valuable approaches to diagnosis and treatment of 2019-nCoV and development of antiviral drugs. In this work, an entropy-driven electrochemiluminescence (ECL) biosensor was constructed for ultra-sensitive bioassay of NF-κB p50. The strategy primarily capitalizing the splendid double-stranded DNA (dsDNA) binding properties of transcription factors, employing GOAu-Ru composite material as ECL emitter, utilizing entropy-driven reactions for signal amplification method, offered a repeatable proposal for TFs detection. In the absence of TFs, the released DNA1 further went in the entropy-driven reaction, contributing to an "ECL off" state. However, in the presence of TFs, the dsDNA avoided being digested, which blocked DNA1 for participating in the entropy-driven reaction, and the system exhibited an "ECL on" state. Most importantly, the ECL bioanalytical method denoted broad application prospects for NF-κB p50 detection with a lower detection limit (9.1 pM).


Subject(s)
Biosensing Techniques/methods , COVID-19/immunology , Cytokine Release Syndrome/immunology , NF-kappa B p50 Subunit/analysis , Biosensing Techniques/statistics & numerical data , COVID-19/complications , Cytokine Release Syndrome/etiology , Electrochemical Techniques/methods , Electrochemical Techniques/statistics & numerical data , Entropy , Humans , Limit of Detection , Luminescent Measurements/methods , Luminescent Measurements/statistics & numerical data , Pandemics , SARS-CoV-2 , Sensitivity and Specificity
15.
J Bioinform Comput Biol ; 19(1): 2050043, 2021 02.
Article in English | MEDLINE | ID: covidwho-936942

ABSTRACT

This paper has developed and described a detailed method for selecting inhibitors based on modified natural peptides for the SARS-CoV BJ01 spike-glycoprotein. The selection of inhibitors is carried out by increasing the affinity of the peptide to the active center of the protein. This paper also provides a step-by-step algorithm for analyzing the affinity of protein interactions and presents an analysis of energy interactions between the active center of a protein and the wild-type peptide interacting with it, taking into account modifications of the latter. A description of the software package that implements the presented algorithm is given on the website https://binomlabs.com/covid19.


Subject(s)
Antiviral Agents/pharmacology , Drug Evaluation, Preclinical/methods , Spike Glycoprotein, Coronavirus/antagonists & inhibitors , Spike Glycoprotein, Coronavirus/chemistry , Algorithms , Amino Acid Substitution , Catalytic Domain , Entropy , Protein Interaction Domains and Motifs/genetics , Proteins/chemistry , Proteins/genetics , Proteins/metabolism , Proteins/pharmacology , Software , Spike Glycoprotein, Coronavirus/metabolism
16.
Comb Chem High Throughput Screen ; 25(3): 392-400, 2022.
Article in English | MEDLINE | ID: covidwho-740472

ABSTRACT

AIM AND OBJECTIVE: Aim and Objective: Sequence analysis is one of the foundations in bioinformatics. It is widely used to find out the feature metrics hidden in the sequence. Otherwise, the graphical representation of the biologic sequence is an important tool for sequencing analysis. This study is undertaken to find out a new graphical representation of biosequences. MATERIALS AND METHODS: The transition probability is used to describe amino acid combinations of protein sequences. The combinations are composed of amino acids directly adjacent to each other or separated by multiple amino acids. The transition probability graph is built up by the transition probabilities of amino acid combinations. Next, a map is defined as a representation from the transition probability graph to transition probability vector by the k-order transition probability graph. Transition entropy vectors are developed by the transition probability vector and information entropy. Finally, the proposed method is applied to two separate applications, 499 HA genes of H1N1, and 95 coronaviruses. RESULTS: By constructing a phylogenetic tree, it was found that the results of each application are consistent with other studies. CONCLUSION: The graphical representation proposed in this article is a practical and correct method.


Subject(s)
Influenza A Virus, H1N1 Subtype , Algorithms , Amino Acid Sequence , Entropy , Phylogeny , Probability
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