Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D
Phages have seriously affected the biochemical systems of the world, and not only are phages related to our health, but medical treatments for many cancers and skin infections are related to phages; therefore, this paper sought to identify phage proteins. In this paper, a Pseudo-188D model was estab...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:b21d9d0d18af439296eaad48107c46ea2021-12-02T00:10:47ZPseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D1664-802110.3389/fgene.2021.796327https://doaj.org/article/b21d9d0d18af439296eaad48107c46ea2021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.796327/fullhttps://doaj.org/toc/1664-8021Phages have seriously affected the biochemical systems of the world, and not only are phages related to our health, but medical treatments for many cancers and skin infections are related to phages; therefore, this paper sought to identify phage proteins. In this paper, a Pseudo-188D model was established. The digital features of the phage were extracted by PseudoKNC, an appropriate vector was selected by the AdaBoost tool, and features were extracted by 188D. Then, the extracted digital features were combined together, and finally, the viral proteins of the phage were predicted by a stochastic gradient descent algorithm. Our model effect reached 93.4853%. To verify the stability of our model, we randomly selected 80% of the downloaded data to train the model and used the remaining 20% of the data to verify the robustness of our model.Xiaomei GuXiaomei GuXiaomei GuXiaomei GuLina GuoBo LiaoBo LiaoBo LiaoQinghua JiangQinghua JiangQinghua JiangFrontiers Media S.A.articlemodel pseudo-188Dphagestochastic gradient descentdimensional disasterdigital characteristicsGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021) |
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model pseudo-188D phage stochastic gradient descent dimensional disaster digital characteristics Genetics QH426-470 |
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model pseudo-188D phage stochastic gradient descent dimensional disaster digital characteristics Genetics QH426-470 Xiaomei Gu Xiaomei Gu Xiaomei Gu Xiaomei Gu Lina Guo Bo Liao Bo Liao Bo Liao Qinghua Jiang Qinghua Jiang Qinghua Jiang Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D |
description |
Phages have seriously affected the biochemical systems of the world, and not only are phages related to our health, but medical treatments for many cancers and skin infections are related to phages; therefore, this paper sought to identify phage proteins. In this paper, a Pseudo-188D model was established. The digital features of the phage were extracted by PseudoKNC, an appropriate vector was selected by the AdaBoost tool, and features were extracted by 188D. Then, the extracted digital features were combined together, and finally, the viral proteins of the phage were predicted by a stochastic gradient descent algorithm. Our model effect reached 93.4853%. To verify the stability of our model, we randomly selected 80% of the downloaded data to train the model and used the remaining 20% of the data to verify the robustness of our model. |
format |
article |
author |
Xiaomei Gu Xiaomei Gu Xiaomei Gu Xiaomei Gu Lina Guo Bo Liao Bo Liao Bo Liao Qinghua Jiang Qinghua Jiang Qinghua Jiang |
author_facet |
Xiaomei Gu Xiaomei Gu Xiaomei Gu Xiaomei Gu Lina Guo Bo Liao Bo Liao Bo Liao Qinghua Jiang Qinghua Jiang Qinghua Jiang |
author_sort |
Xiaomei Gu |
title |
Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D |
title_short |
Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D |
title_full |
Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D |
title_fullStr |
Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D |
title_full_unstemmed |
Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D |
title_sort |
pseudo-188d: phage protein prediction based on a model of pseudo-188d |
publisher |
Frontiers Media S.A. |
publishDate |
2021 |
url |
https://doaj.org/article/b21d9d0d18af439296eaad48107c46ea |
work_keys_str_mv |
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