Four layers image representation for prediction of lung cancer genetic mutations based on 2DPCA

Genetic mutations are the first warning to the onset of lung cancer. The ability to early predict these mutations could open the door for a targeted treatment options for lung cancer patients. Three top candidate genes previously reported to have the highest frequency of lung cancer mutations. Each gene is encoded as a symbolic sequence of four letters. A novel method for gene representation is introduced in this paper, where each letter in gene sequence is represented by a layer image. The final four layers are integrated with Two Dimensional Principle Component Analysis (2DPCA) to build an algorithm for prediction of lung cancer. Furthermore, the algorithm is capable to identify the substitution type in somatic mutations of lung cancer with high accuracy. The high dimensionality and computational complexity of prediction are reduced by employing 2DPCA, which allows a high-dimensional space to be represented in a low-dimensional one. Experimental results confirm that, the proposed algorithm achieved accuracy of 98.55% in early prediction of lung cancer and accuracy of 88.18% in identification of the substitution type in gene sequence.