Dinç İmren, Sigdel Madhav, Dinç Semih, Sigdel Madhu S, Pusey Marc L, Aygün Ramazan S
DataMedia Research Lab, Computer Science Department, University of Alabama in Huntsville, Huntsville Alabama 35899.
iXpressGenes Inc., 601 Genome Way, Huntsville, Alabama 35806.
Proc IEEE Southeastcon. 2014 Mar;2014. doi: 10.1109/SECON.2014.6950744.
In this paper, we investigate the performance of classification of protein crystallization images captured during protein crystal growth process. We group protein crystallization images into 3 categories: noncrystals, likely leads (conditions that may yield formation of crystals) and crystals. In this research, we only consider the subcategories of noncrystal and likely leads protein crystallization images separately. We use 5 different classifiers to solve this problem and we applied some data preprocessing methods such as principal component analysis (PCA), min-max (MM) normalization and z-score (ZS) normalization methods to our datasets in order to evaluate their effects on classifiers for the noncrystal and likely leads datasets. We performed our experiments on 1606 noncrystal and 245 likely leads images independently. We had satisfactory results for both datasets. We reached 96.8% accuracy for noncrystal dataset and 94.8% accuracy for likely leads dataset. Our target is to investigate the best classifiers with optimal preprocessing techniques on both noncrystal and likely leads datasets.
在本文中，我们研究了在蛋白质晶体生长过程中捕获的蛋白质结晶图像的分类性能。我们将蛋白质结晶图像分为3类：非晶体、可能的晶核（可能产生晶体形成的条件）和晶体。在本研究中，我们仅分别考虑非晶体和可能的晶核蛋白质结晶图像的子类别。我们使用5种不同的分类器来解决这个问题，并对我们的数据集应用了一些数据预处理方法，如主成分分析（PCA）、最小-最大（MM）归一化和z分数（ZS）归一化方法，以评估它们对非晶体和可能的晶核数据集的分类器的影响。我们分别对1606张非晶体图像和245张可能的晶核图像进行了实验。两个数据集都取得了令人满意的结果。非晶体数据集的准确率达到了96.8%，可能的晶核数据集的准确率达到了94.8%。我们的目标是在非晶体和可能的晶核数据集上研究采用最优预处理技术的最佳分类器。
