An Efficient Contiguous Pattern Mining technique to Predict Mutations in Breast Cancer for DNA Data Sequences

In data mining, one of the most important tasks is sequential pattern mining (SPM). This SPM is used to mine most interesting subsequences in a set of sequences. The various real-life applications of SPM is bioinformatics, market basket analysis, web stream analysis and many more. The development of applications using data mining techniques to solve biological problems plays an important role in bioinformatics. This paper aims to propose mining of contiguous patterns in Deoxyribonucleic Acid (DNA) to identify breast cancer disease. The CSpan (Contiguous Sequential Pattern Mining) method is used to find contiguous patterns of DNA sequence database. Instead of mining all the patterns in a given sequence only contiguous patterns are mined i.e., compact patterns. The contiguous patterns with greater homogeneity are considered as biomarker to identify breast cancer disease. The patterns frequency occurrence of normal DNA is compared with mutated patterns of breast cancer gene (BRCA1) for identifying the disease. The mutation ratio is calculated to identify the level of change in the contiguous pattern between normal and mutated patterns.