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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Bioimage-Based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks
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Bioimage-Based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks

机译：基于生物贴图的人体组织中蛋白质亚细胞位置的预测与集合特征和深网络

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摘要

Prediction of protein subcellular location has currently become a hot topic because it has been proven to be useful for understanding both the disease mechanisms and novel drug design. With the rapid development of automated microscopic imaging technology in recent years, classification methods of bioimage-based protein subcellular location have attracted considerable attention for images can describe the protein distribution intuitively and in detail. In the current study, a prediction method of protein subcellular location was proposed based on multi-view image features that are extracted from three different views, including the four texture features of the original image, the global and local features of the protein extracted from the protein channel images after color segmentation, and the global features of DNA extracted from the DNA channel image. Finally, the extracted features were combined together to improve the performance of subcellular localization prediction. From the performance comparison of different combination features under the same classifier, the best ensemble features could be obtained. In this work, a classifier based on Stacked Auto-encoders and the random forest was also put forward. To improve the prediction results, the deep network was combined with the traditional statistical classification methods. Stringent cross-validation and independent validation tests on the benchmark dataset demonstrated the efficacy of the proposed method.
机译：蛋白质亚细胞位置的预测目前已成为一个热门话题，因为已被证明是有助于理解疾病机制和新型药物设计。随着近年来自动微观成像技术的快速发展，基于生物显微镜的蛋白质亚细胞位置的分类方法引起了可观的图像，可以对图像进行直观和详细描述蛋白质分布。在本研究中，基于从三种不同视图中提取的多视图图像特征提出了一种蛋白质亚细胞位置的预测方法，包括原始图像的四个纹理特征，蛋白质的全局和局部特征蛋白质通道图像在彩色分割后，以及从DNA通道图像提取的DNA的全局特征。最后，将提取的特征组合在一起以提高亚细胞定位预测的性能。从不同组合特征的性能比较在相同的分类器下，可以获得最佳集合特征。在这项工作中，还提出了一种基于堆叠的自动编码器和随机林的分类器。为了提高预测结果，深网络与传统的统计分类方法相结合。基准数据集上的严格交叉验证和独立验证测试展示了所提出的方法的功效。

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