International Journal of Statistics and Reliability Engineering

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Vol 10, No 2:

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Design of Stochastic Model for Mutated Malignant Gene Sequences using Genomic Data Mining

Abstract

This paper mainly deals with detecting the disease earlier by the gene sequences. A novel machine learning approach is ventured to predict the gene sequences; accompanied by the conventional prediction methods like Markov chain, Hidden Markov Model. This work attempts to predict the malignancy before it happens through mutated TP53 gene sequences. For the performance verification of Markov chain, the existence of CpG islands, fitting of distribution has been implemented. As the extension of Markov chain, the HMM is introduced; the hidden states and their corresponding probabilities are estimated. The optimal path for each sequence is determined. To identify the probabilities of each hidden and visible state, the profile HMM is implemented. PHMM is to provide the indication of how well a new sequence fit the model and whether the sequence may be related to the sequence used to train the model. The performance of the trained model is compared against the previously existing models.

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ISSN(P) 2350-0174

ISSN(O) 2456-2378

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