Hybrid Algorithms for Multiple Change-Point Detection in Biological Sequences

摘要: Array comparative genomic hybridization (aCGH) is one of the techniques that can be used to detect copy number variations in DNA sequences high resolution. It has been identified abrupt changes human genome play a vital role progression and development many complex diseases. In this study we propose two distinct hybrid algorithms combine efficient sequential change-point detection procedures (the Shiryaev-Roberts procedure cumulative sum control chart (CUSUM) procedure) with Cross-Entropy method, which an evolutionary stochastic optimization technique estimate both change-points their corresponding locations aCGH data. The proposed are applied artificially generated data real experimental illustrate usefulness. Our results show methodologies effective detecting multiple biological continuous measurements.