An Improved Isomap Algorithm for Predicting Protein Localization

In this paper, a system based on the MDM-Isomap (Minimax Distance Metric-based neighborhood selection algorithm for Isomap) is proposed to improve the performance of protein subcellular localization prediction. First of all, the protein sequences are quantized into a high dimension space using an effective sequence encoding scheme. However, the problems caused by such representation are computation complexity and complicated classifier design. To sort out this problem, a new dimension reduction algorithm, MDM-Isomap, is introduced. It is an improved isomap algorithm, which can acquire a suitable neighborhood size for Isomap. It extracts the essential features from the high dimension feature space. Then, an efficient classifier is employed to recognize the subcellular localization of proteins according to the new features after dimension reduction.