Using Chou’s Five-steps Rule to Classify and Predict Glutathione S-transferases with Different Machine Learning Algorithms and Pseudo Amino Acid Composition

Mohabatkar, Hassan; Ebrahimi, Samira; Moradi, Mohammad

doi:10.1007/s10989-020-10087-7

Cited by 11 publications

(4 citation statements)

References 51 publications

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“…More specifically, machine learning can be seen as looking for a function, whose input is the sample data and the output is the expected result. However, this function is too complex to be expressed formally (Mohabatkar and Ebrahimi, 2021 ). It should be noted that the goal of machine learning is to make the learned functions well applicable to the new sample data, not just perform well on the training samples (Ghorbanzadeh et al, 2019 ).…”

Section: Research On Machine Learning and Related Sports Robotsmentioning

confidence: 99%

Return Strategy and Machine Learning Optimization of Tennis Sports Robot for Human Motion Recognition

et al. 2022

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At present, there are many kinds of intelligent training equipment in tennis sports, but they all need human control. If a single tennis player uses the robot to return the ball, it will save some human resources. This study aims to improve the recognition rate of tennis sports robots in the return action and the return strategy. The human-oriented motion recognition of the tennis sports robot is taken as the starting point to recognize and analyze the return action of the tennis sports robot. The OpenPose traversal dataset is used to recognize and extract human motion features of tennis sports robots under different classifications. According to the return characteristics of the tennis sports robot, the method of tennis return strategy based on the support vector machine (SVM) is established, and the SVM algorithm in machine learning is optimized. Finally, the return strategy of tennis sports robots under eight return actions is analyzed and studied. The results reveal that the tennis sports robot based on the SVM-Optimization (SVM-O) algorithm has the highest return recognition rate, and the average return recognition rate is 88.61%. The error rates of the backswing, forward swing, and volatilization are high in the return strategy of tennis sports robots. The preparation action, backswing, and volatilization can achieve more objective results in the analysis of the return strategy, which is more than 90%. With the increase of iteration times, the effect of the model simulation experiment based on SVM-O is the best. It suggests that the algorithm proposed has a reliable accuracy of the return strategy of tennis sports robots, which meets the research requirements. Human motion recognition is integrated with the return motion of tennis sports robots. The application of the SVM-O algorithm to the return action recognition of tennis sports robots has good practicability in the return action recognition of tennis sports robot and solves the problem that the optimization algorithm cannot be applied to the real-time requirements. It has important research significance for the application of an optimized SVM algorithm in sports action recognition.

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Section: Research On Machine Learning and Related Sports Robotsmentioning

confidence: 99%

Return Strategy and Machine Learning Optimization of Tennis Sports Robot for Human Motion Recognition

et al. 2022

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“…Application of in silico methods has revolutionized the field of molecular biology by presenting valuable predictions of biological systems, and this field is growing by advent of new computational tools such as machine learning approaches [ 5 ]. Application of molecular docking for prediction of molecular interactions has helped scientists to present valuable information about the biological systems [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

In silico design of quadruplex aptamers against the spike protein of SARS-CoV-2

Behbahani

Mohabatkar

Hosseini

2021

Informatics in Medicine Unlocked

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“…To present an effective therapeutic against SARS-CoV-2, having a conclusive knowledge about the viral structures is necessary (Mohabatkar et al 2021a , 2021b ; Valle et al 2020 ), which could enable to find a more appropriate candidates for targeting. Regarding the potential target for COVID-19 drug development, some molecular structures have been suggested including spike glycoprotein, main protease, and papain-like protease (Wong et al 2021a , 2021b ).…”

Section: Introductionmentioning

confidence: 99%

“…One of the most highlighted field that could be useful for investigating the efficacy of drugs against various disorders, including COVID-19, is the field of bioinformatics (Nabati et al 2020 ). Application of the computer tests before taking them to the in vitro and in vivo level can diminish the financial burden and the use of experts’ time and energy, along with reducing the need for animal sacrifice (Haghighi 2021 ; Mohabatkar, Ebrahimi, & Moradi 2021a , b ). Bioinformatics method provide an in silico platform for scientist through which they could investigate the efficiency of thousands of potential therapeutic reagents by working on their computers (Ahmadi et al 2021 ; Gharbavi et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

In Silico Analysis of Inhibiting Papain-like Protease from SARS-CoV-2 by Using Plant-Derived Peptides

Moradi

Golmohammadi

Najafi

et al. 2021

Int J Pept Res Ther

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SARS-CoV-2 is a corona virus that has been the cause for one of the deadliest pandemics of history, started since 2019. Suppressing the activity of the critical enzymes in the SARS-CoV-2 could potentially inhibit a vital step in viral life cycle. Papain-like protease (PLpro) could be regarded as a critical enzyme in viral replication of SARS-CoV-2. In this research, it was aimed to suppress the activity of PLpro enzyme by using potential plant-derived protease inhibitor peptides. For this purpose, 11 plant derived peptides that could potentially inhibit protease activity were selected from literature. The structures of the PLpro and the peptide ligands were acquired from PDB (protein data bank) and after structural optimization, were docked by using HADDOCK 2.4 program. Analyzing the results indicated that VcTI from Veronica hederifolia provides effective molecular interactions at both liable Zn site and classic active site of PLpro, making it a potential inhibitory ligand for this enzyme that could be used for halting the replication of SARS-CoV-2. Molecular dynamic assay confirmed that the selected receptor and ligand complex was stable. Future in vitro and in vivo investigations are required to verify the efficiency of this compound as a potential therapeutic against SARS-CoV-2 infection. Supplementary Information The online version contains supplementary material available at 10.1007/s10989-021-10331-8.

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Using Chou’s Five-steps Rule to Classify and Predict Glutathione S-transferases with Different Machine Learning Algorithms and Pseudo Amino Acid Composition

Cited by 11 publications

References 51 publications

Return Strategy and Machine Learning Optimization of Tennis Sports Robot for Human Motion Recognition

Return Strategy and Machine Learning Optimization of Tennis Sports Robot for Human Motion Recognition

In silico design of quadruplex aptamers against the spike protein of SARS-CoV-2

In Silico Analysis of Inhibiting Papain-like Protease from SARS-CoV-2 by Using Plant-Derived Peptides

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