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BioMed Research International
Volume 2013 (2013), Article ID 701317, 13 pages
iEzy-Drug: A Web Server for Identifying the Interaction between Enzymes and Drugs in Cellular Networking
1Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333046, China
2Information School, ZheJiang Textile & Fashion College, NingBo 315211, China
3Gordon Life Science Institute, Belmont, MA 02478, USA
4Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
Received 7 August 2013; Accepted 17 September 2013
Academic Editor: Tatsuya Akutsu
Copyright © 2013 Jian-Liang Min 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.
Citations to this Article [13 citations]
The following is the list of published articles that have cited the current article.
- Avijit Podder, Nidhi Jatana, and N. Latha, “Human Dopamine Receptors Interaction Network (DRIN): A systems biology perspective on topology, stability and functionality of the network,” Journal of Theoretical Biology, vol. 357, pp. 169–183, 2014.
- Wei Chen, Peng-Mian Feng, En-Ze Deng, Hao Lin, and Kuo-Chen Chou, “iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition,” Analytical Biochemistry, 2014.
- Liqi Li, Sanjiu Yu, Weidong Xiao, Yongsheng Li, Maolin Li, Lan Huang, Xiaoqi Zheng, Shiwen Zhou, and Hua Yang, “Prediction of bacterial protein subcellular localization by incorporating various features into Chou's PseAAC and a backward feature selection approach,” Biochimie, 2014.
- Loris Nanni, Alessandra Lumini, and Sheryl Brahnam, “A set of descriptors for identifying the protein–drug interaction in cellular networking,” Journal of Theoretical Biology, 2014.
- Mohammad Reza Bakhtiarizadeh, Mohammad Moradi-Shahrbabak, Mansour Ebrahimi, and Esmaeil Ebrahimie, “Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology,” Journal of Theoretical Biology, 2014.
- Yan Xu, Xin Wen, Xiao-Jian Shao, Nai-Yang Deng, and Kuo-Chen Chou, “iHyd-PseAAC: Predicting Hydroxyproline and Hydroxylysine in Proteins by Incorporating Dipeptide Position-Specific Propensity into Pseudo Amino Acid Composition,” International Journal of Molecular Sciences, vol. 15, no. 5, pp. 7594–7610, 2014.
- Sukanta Mondal, and Priyadarshini P. Pai, “Chou's pseudo amino acid composition improves sequence-based antifreeze protein prediction,” Journal of Theoretical Biology, 2014.
- Shuyan Ding, Shoujiang Yan, Shuhua Qi, Yan Li, and Yuhua Yao, “A protein structural classes prediction method based on PSI-BLAST profile,” Journal of Theoretical Biology, 2014.
- James Lyons, Alok Sharma, Abdollah Dehzangi, and Kuldip K. Paliwal, “Protein Fold Recognition by Alignment of Amino Acid Residues Using Kernelized Dynamic Time Warping,” Journal of Theoretical Biology, 2014.
- Yue-Nong Fan, Xuan Xiao, Jian-Liang Min, and Kuo-Chen Chou, “iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking,” International Journal of Molecular Sciences, vol. 15, no. 3, pp. 4915–4937, 2014.
- Shao-Wu Zhang, Li-Yang Hao, and Ting-He Zhang, “Prediction of Protein–Protein Interaction with Pairwise Kernel Support Vector Machine,” International Journal of Molecular Sciences, vol. 15, no. 2, pp. 3220–3233, 2014.
- Wang-Ren Qiu, Xuan Xiao, and Kuo-Chen Chou, “iRSpot-TNCPseAAC: Identify Recombination Spots with Trinucleotide Composition and Pseudo Amino Acid Components,” International Journal of Molecular Sciences, vol. 15, no. 2, pp. 1746–1766, 2014.
- Hui Ding, En-Ze Deng, Lu-Feng Yuan, Li Liu, Hao Lin, Wei Chen, and Kuo-Chen Chou, “iCTX-Type: A Sequence-Based Predictor for Identifying the Types of Conotoxins in Targeting Ion Channels,” BioMed Research International, vol. 2014, pp. 1–10, 2014.