Detection of HIV-1 protease cleavages sites via hidden Markov model and physicochemical properties of aminoacids

Date

2020-01-01

Author

Doğan Dar, Elif
Purutçuoğlu Gazi, Vilda

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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.

URI

https://hdl.handle.net/11511/78811

Relation

Numerical Solutions of Real-Life Nonlinear Phenomena

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Department of Statistics, Book / Book chapter

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Citation Formats

E. Doğan Dar and V. Purutçuoğlu Gazi, Detection of HIV-1 protease cleavages sites via hidden Markov model and physicochemical properties of aminoacids. 2020, p. 193.