论文标题
AFP-SRC：使用稀疏表示分类器鉴定防冻蛋白
AFP-SRC: Identification of Antifreeze Proteins Using Sparse Representation Classifier
论文作者
论文摘要
生活在极端寒冷环境中的物种使用抗冻蛋白（AFP）对抗恶劣条件，以多种方式操纵水的冻结机制。 AFP的这种惊人性质在几种工业和医疗应用中非常有用。其结构和序列缺乏相似性使他们的预测是一项艰巨的任务，并且在湿lab中通过实验识别它们是耗时且昂贵的。在这项研究中，我们提出了一个用于预测AFP的计算框架，该框架基本上基于使用稀疏重建的特定于样本的分类方法。已知AFP的线性模型和过度完整的字典矩阵用于预测提供样本关联评分的稀疏类标签矢量。使用样品缔合矢量的下部和上部对两个伪示例进行重建，并根据最低恢复得分进行重建，分配了类标签。我们将我们的方法与标准数据集上的现代方法进行比较，并且发现所提出的方法在平衡准确性和Youden的索引方面均优于表现。提出方法的MATLAB实现可在作者的GitHub页面（\ {https://github.com/shujaat123/afp-src} {https://github.com/shujaat123/afp-src}）上获得。
Species living in the extreme cold environment fight against the harsh conditions using antifreeze proteins (AFPs), that manipulates the freezing mechanism of water in more than one way. This amazing nature of AFP turns out to be extremely useful in several industrial and medical applications. The lack of similarity in their structure and sequence makes their prediction an arduous task and identifying them experimentally in the wet-lab is time-consuming and expensive. In this research, we propose a computational framework for the prediction of AFPs which is essentially based on a sample-specific classification method using the sparse reconstruction. A linear model and an over-complete dictionary matrix of known AFPs are used to predict a sparse class-label vector that provides a sample-association score. Delta-rule is applied for the reconstruction of two pseudo-samples using lower and upper parts of the sample-association vector and based on the minimum recovery score, class labels are assigned. We compare our approach with contemporary methods on a standard dataset and the proposed method is found to outperform in terms of Balanced accuracy and Youden's index. The MATLAB implementation of the proposed method is available at the author's GitHub page (\{https://github.com/Shujaat123/AFP-SRC}{https://github.com/Shujaat123/AFP-SRC}).
