Abstract
The detection of the cleavage side in Chip-seq data is one of the main interests to find the lock-and-key relationship between enzymes and prohibits in certain diseases such as AIDS and to produce the proper inhibitors for these illnesses. For this detection, different approaches like support vector machines and artificial neural networks have been suggested. In this study, we use the hidden Markov model (HMM) for the cleavage site detection. In our application, initially, we comprehensively explain the mathematical details of HMM and the inference of the model parameters, and then we discuss the effect of various clustering approaches both in feature selection and state formation. We demonstrate the calculation of each step in a toy and benchmark dataset and evaluate the accuracy of estimates with other approaches in the literature.
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Acknowledgements
The authors would like to thank the EU 7th Framework project, called COSTNET (Project no: CA15109), and the BAP project at the Middle East Technical University (Project no: BAP-08-11-2017-035) for their supports.
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Dar, E.D., Purutçuoğlu, V., Purutçuoğlu, E. (2020). Detection of HIV-1 Protease Cleavage Sites via Hidden Markov Model and Physicochemical Properties of Amino Acids. In: Machado, J., Özdemir, N., Baleanu, D. (eds) Numerical Solutions of Realistic Nonlinear Phenomena. Nonlinear Systems and Complexity, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-37141-8_10
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