Array comparative genomic hybridization (aCGH) is one of the techniques that can be used to detect copy number variations in DNA sequences. It has been identified that abrupt changes in the human genome play a vital role in the progression and development of many diseases. We propose a hybrid algorithm that utilizes both the sequential techniques and the Cross-Entropy method to estimate the number of change points as well as their locations in aCGH data. We applied the proposed hybrid algorithm to both artificially generated data and real data to illustrate the usefulness of the methodology. Our results show that the proposed algorithm is an effective method to detect multiple change-points in continuous measurements.
|Number of pages||10|
|Journal||AIP Conference Proceedings|
|Publication status||Published - 2013|
|Event|| International Symposium on Computational Models for Life Sciences|
Duration: 27 Nov 2013 → 29 Nov 2013