روشی جدید برای تفکیک و طبقه‌بندی توالی‌های سرطانی و غیرسرطانی DNA با استفاده از الگوریتم‌های مبتنی بر LPC و SVD

The growing pace of cancer has encouraged researchers to deliberate several aspects of this malignant disease. Genetic-induced nature of cancer, heighten the importance of studying intra-cell components. This paper has been carried out with the aim of making some specific and unique features clear from those long DNA sequences by employing well-established DNA sequence analysis techniques. The identical part of human body DNA sequences have been used to simulate proposing algorithm. Z-Curve mapping method has been utilized in order to conversion of DNA alphabetic strings to digital signals. This method has made use of Linear Predictive Coding (LPC) model to analyze resultant data for feature extraction. In addition, this paper is beneficiary of a certain singular value decomposition (SVD) computational approaches to select significant features for dimension reduction. Finally, statistical parameters discriminate cancerous samples from non-cancerous ones. This discrimination represents the biological mutation concept which expresses the genetic changes of cancer disease.