資料載入中.....
|
請使用永久網址來引用或連結此文件:
https://ir.lib.ncu.edu.tw/handle/987654321/105794
|
| 題名: | Incorporating amino acids composition and functional domains for identifying bacterial toxin proteins |
| 作者: | 吳忻怡;Su, Min-Gang;Huang, Chien-Hsun;Lee, Tzong-Yi;Chen, Yu-Ju;Wu, Hsin-Yi |
| 貢獻者: | 總教學中心通識教育中心 |
| 關鍵詞: | Accuracy;Amino acids;Amino Acids - chemistry;Amino Acids - genetics;Amino Acids - isolation & purification;Bacteria;Bacterial toxins;Bacterial Toxins - chemistry;Bacterial Toxins - genetics;Bacterial Toxins - isolation & purification;Computational Biology;Databases, Protein;Endotoxins - chemistry;Endotoxins - genetics;Endotoxins - isolation & purification;Exotoxins - chemistry;Exotoxins - genetics;Exotoxins - isolation & purification;Genetic aspects;Humans;Lipopolysaccharides;Methods;Plant cell walls;Protein Structure, Tertiary;Proteins;Sequence Analysis, Protein;Studies;Support Vector Machine;Toxins |
| 日期: | 2014-01-01 |
| 上傳時間: | 2026-04-23 12:53:14 (UTC+8) |
| 出版者: | Hindawi Publishing Corporation;Cairo, Egypt: Hindawi Puplishing Corporation |
| 摘要: | 摘要： Aside from pathogenesis, bacterial toxins also have been used for medical purpose such as drugs for cancer and immune diseases. Correctly identifying bacterial toxins and their types (endotoxins and exotoxins) has great impact on the cell biology study and therapy development. However, experimental methods for bacterial toxins identification are time-consuming and labor-intensive, implying an urgent need for computational prediction. Thus, we are motivated to develop a method for computational identification of bacterial toxins based on amino acid sequences and functional domain information. In this study, a nonredundant dataset of 167 bacterial toxins including 77 exotoxins and 90 endotoxins is adopted to learn the predictive model by using support vector machines (SVMs). The cross-validation evaluation shows that the SVM models trained with amino acids and dipeptides composition could yield an accuracy of 96.07% and 92.50%, respectively. For discriminating endotoxins from exotoxins, the SVM models trained with amino acids and dipeptides composition have achieved an accuracy of 95.71% and 92.86%, respectively. After incorporating functional domain information, the predictive performance is further improved. The proposed method has been demonstrated to be able to more effectively identify and classify bacterial toxins than the other two features on independent dataset, which may aid in bacterial biomedical development.
其他題名： Biomed Res Int
出版者： Cairo, Egypt: Hindawi Puplishing Corporation
出版日期： 2014-01-01
出處： BioMed research international, 2014-01, Vol.2014 (2014), p.1-7
資源來源： EBSCOhost Academic Search Premier
版權： Copyright © 2014 Min-Gang Su et al.
版權： COPYRIGHT 2014 John Wiley & Sons, Inc.
版權： Copyright © 2014 Min-Gang Su et al. Min-Gang Su et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
版權： Copyright © 2014 Min-Gang Su et al. 2014
識別號： ISSN: 2314-6133
識別號： ISSN: 2314-6141
識別號： EISSN: 2314-6141
識別號： DOI: 10.1155/2014/972692
識別號： PMID: 25110714 |
| 顯示於類別: | [通識教育中心] 期刊論文
|
文件中的檔案:
| 檔案 |
描述 |
大小 | 格式 | 瀏覽次數 |
|---|
| index.html | | 0Kb | HTML | 39 | 檢視/開啟 |
|
在NCUIR中所有的資料項目都受到原著作權保護.
|
