A hybrid approach for hot spot prediction and deep representation of hematological protein – drug interactions

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    In our research work we will collect the data of drugs as well as protein regarding hematic diseases, then applying feature extraction as well as classification, predict hot spot and non-hot spot then we are predicting the hot region using prediction algorithm. Parallelly from the hematological drug we are extracting the feature using molecular finger print then classifying using a classifier and applying deep learning concept to reduce the dimensionality then finally using machine learning algorithm predicting which drug will interact with the help of a hybrid approach.

  • Keywords

    Deep Learning; Drug Prediction; Hot Spots; Hot Region.

  • References

      [1] Buza, K. (2016, May). Drug-target interaction prediction with hubness-aware machine learning. In Applied Computational Intelligence and Informatics (SACI), 2016 IEEE 11th International Symposium on (pp. 437-440). IEEE.https://doi.org/10.1109/SACI.2016.7507416.

      [2] Lin, X., & Zhang, X. (2016, December). Prediction and analysis of hot region in protein-protein interactions. In Bioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference on (pp. 1598-1603). IEEE.https://doi.org/10.1109/BIBM.2016.7822758.

      [3] Hunta, S., Aunsri, N., &Yooyativong, T. (2015, June). Drug-Drug Interactions prediction from enzyme action crossing through machine learning approaches. In Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2015 12th International Conference on (pp. 1-4). IEEE. https://doi.org/10.1109/ECTICon.2015.7207126.

      [4] Tian, K., Shao, M., Wang, Y., Guan, J., & Zhou, S. (2016). Boosting compound-protein interaction prediction by deep learning. Methods, 110, 64-72.https://doi.org/10.1016/j.ymeth.2016.06.024.

      [5] Cheng, F., & Zhao, Z. (2014). Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. Journal of the American Medical Informatics Association, 21(e2), e278-e286.https://doi.org/10.1136/amiajnl-2013-002512.

      [6] Huang, Q. Y., You, Z. H., Li, S., & Zhu, Z. (2014, July). Using Chou's amphiphilic Pseudo-Amino Acid Composition and Extreme Learning Machine for prediction of Protein-protein interactions. In Neural Networks (IJCNN), 2014 International Joint Conference on (pp. 2952-2956). IEEE.https://doi.org/10.1109/IJCNN.2014.6889476.

      [7] You, Z. H., Li, L., Ji, Z., Li, M., & Guo, S. (2013, April). Prediction of protein-protein interactions from amino acid sequences using extreme learning machine combined with auto covariance descriptor. In Memetic Computing (MC), 2013 IEEE Workshop on (pp. 80-85). IEEE.https://doi.org/10.1109/MC.2013.6608211.

      [8] Van Laarhoven, T., &Marchiori, E. (2013). Predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile. PloS one, 8(6), e66952.https://doi.org/10.1371/journal.pone.0066952.

      [9] Wang, Y., & Zeng, J. (2013). Predicting drug-target interactions using restricted Boltzmann machines. Bioinformatics, 29(13), i126-i134.https://doi.org/10.1093/bioinformatics/btt234.

      [10] Mei, J. P., Kwoh, C. K., Yang, P., Li, X. L., & Zheng, J. (2012). Drug–target interaction prediction by learning from local information and neighbors. Bioinformatics, 29(2), 238-245.https://doi.org/10.1093/bioinformatics/bts670.

      [11] Gönen, M. (2012). Predicting drug–target interactions from chemical and genomic kernels using Bayesian matrix factorization. Bioinformatics, 28(18), 2304-2310.https://doi.org/10.1093/bioinformatics/bts360.

      [12] Van Laarhoven, T., Nabuurs, S. B., &Marchiori, E. (2011). Gaussian interaction profile kernels for predicting drug–target interaction. Bioinformatics, 27(21), 3036-3043.https://doi.org/10.1093/bioinformatics/btr500.

      [13] Danger, R., Segura-Bedmar, I., Martínez, P., & Rosso, P. (2010). A comparison of machine learning techniques for detection of drug target articles. Journal of biomedical informatics, 43(6), 902-913.https://doi.org/10.1016/j.jbi.2010.07.010.

      [14] Namboori, P. K. (2009). Machine Learning Approaches to Determine the “Drug-Likeness” of the Proteomic Targets.

      [15] Bleakley, K., &Yamanishi, Y. (2009). Supervised prediction of drug–target interactions using bipartite local models. Bioinformatics, 25(18), 2397-2403.https://doi.org/10.1093/bioinformatics/btp433.

      [16] Sugaya, N., & Ikeda, K. (2009). Assessing the druggability of protein-protein interactions by a supervised machine-learning method. BMC bioinformatics, 10(1), 263.https://doi.org/10.1186/1471-2105-10-263.

      [17] Darnell, S. J., Page, D., & Mitchell, J. C. (2007). An automated decision‐tree approach to predicting protein interaction hot spots. Proteins: Structure, Function, and Bioinformatics, 68(4), 813-823.https://doi.org/10.1002/prot.21474.

      [18] Chan, K. C., & You, Z. H. (2016, July). Large-scale prediction of drug-target interactions from deep representations. In Neural Networks (IJCNN), 2016 International Joint Conference on (pp. 1236-1243). IEEE.

      [19] Likhitha, C. P., Ninitha, P., &Kanchana, V. (2016). DNA Bar-coding: A Novel Approach for Identifying an Individual Using Extended Levenshtein Distance Algorithm and STR analysis. International Journal of Electrical and Computer Engineering, 6(3), 1133.

      [20] Nair, B. B., Khamarudheen, K. S., &Ranjitha, H. S. (2016). An Approach for Identifying The Presence of Factor Ix Gene In Dna Sequences Using Position Vector Ann. Journal of Theoretical And Applied Information Technology, 87(3), 396.




Article ID: 9752
DOI: 10.14419/ijet.v7i1.9.9752

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.