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Your search keyword '"Amiroch, Siti"' showing total 2 results
Start Over Author "Amiroch, Siti" Topic viral proteins
2 results on '"Amiroch, Siti"'

1. Analysis of protein-protein interaction to obtain significant protein in influenza virus type A/H9N2.

Author

Amiroch, Siti, Faroby, Mohammad Hamim Zajuli Al, Irawan, Mohammad Isa, Mukhlash, Imam, and Nidhom, A. Chairul

Subjects
*PROTEIN-protein interactions, *VIRAL proteins, *INFLUENZA A virus, *AVIAN influenza A virus, *AVIAN influenza, *INFLUENZA viruses
Abstract

In bioinformatics, graphs are often used to describe cell processes, representing interactions between proteins in a protein-protein interaction network. Some proteins significantly influence these tissues and play an essential role in a regulation called significant proteins. Significant proteins are functional as data for drug discovery. Graph analysis is needed to reduce the graph and find these significant proteins. So far, there have been no studies discussing protein interactions in avian influenza type A/H9N2, a zoonotic subtype of avian influenza. Although this virus is classified as Low Pathogenic Avian Influenza, it is troubling the public because it reduces the production of chicken eggs by 80% and disrupts the reproductive organs of mammals. This study aimed to find a significant protein from avian influenza type A/H9N2. From the results of this study, we found ten network clusters with the ClusterONE algorithm. We perform graph analysis on the first cluster because it is the best cluster with the smallest p-value of 0.000023. The cluster contains 20 nodes representing 20 proteins. The analysis graph (Analysis centrality) that is simulated in MATLAB includes betweenness centrality, closeness centrality, degree centrality, eigenvector centrality, and page rank centrality. Of the 20 proteins, nine significant proteins were obtained, namely CD247, CD48, FCGR2A, FCGR2B, IL10, PTPN22, PTPRC, SLAMF1, and TRL5, the highest score on the FCGR2A protein. The highest score indicates that the FCGR2A protein has the most dominant effect on the protein-protein interaction network of Avian Influenza virus type A/H9N2. [ABSTRACT FROM AUTHOR]

Published
2022
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2. Machine Learning for the Prediction of Antiviral Compounds Targeting Avian Influenza A/H9N2 Viral Proteins.

Author

Amiroch, Siti, Irawan, Mohammad Isa, Mukhlash, Imam, Al Faroby, Mohammad Hamim Zajuli, and Nidom, Chairul Anwar

Subjects
*MACHINE learning, *VIRAL proteins, *RECEIVER operating characteristic curves, *AVIAN influenza, *DNA fingerprinting, *SUPPORT vector machines, *INFLUENZA
Abstract

Avian influenza subtype A/H9N2—which infects chickens, reducing egg production by up to 80%—may be transmissible to humans. In humans, this virus is very harmful since it attacks the respiratory system and reproductive tract, replicating in both. Previous attempts to find antiviral candidates capable of inhibiting influenza A/H9N2 transmission were unsuccessful. This study aims to better characterize A/H9N2 to facilitate the discovery of antiviral compounds capable of inhibiting its transmission. The Symmetry of this study is to apply several machine learning methods to perform virtual screening to identify H9N2 antivirus candidates. The parameters used to measure the machine learning model's quality included accuracy, sensitivity, specificity, balanced accuracy, and receiver operating characteristic score. We found that the extreme gradient boosting method yielded better results in classifying compounds predicted to be suitable antiviral compounds than six other machine learning methods, including logistic regression, k-nearest neighbor analysis, support vector machine, multilayer perceptron, random forest, and gradient boosting. Using this algorithm, we identified 10 candidate synthetic compounds with the highest scores. These high scores predicted that the molecular fingerprint may involve strong bonding characteristics. Thus, we were able to find significant candidates for synthetic H9N2 antivirus compounds and identify the best machine learning method to perform virtual screenings. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
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