Blazadonakis Michalis E, Zervakis Michalis
Department of Electronic and Computer Engineering, Technical University of Crete, University Campus, Chania Crete 73100, Greece.
Comput Biol Med. 2008 Aug;38(8):894-912. doi: 10.1016/j.compbiomed.2008.05.005. Epub 2008 Jul 24.
The problem of marker selection in DNA microarray analysis has been addressed so far by two basic types of approaches, the so-called filter and wrapper methods. Wrapper methods operate in a recursive fashion where feature (gene) weights are re-evaluated and dynamically changing from iteration to iteration, while in filter methods feature weights remain fixed. Our objective in this study is to show that the application of filter criteria in a recursive fashion, where weights are potentially adjusted from cycle to cycle, produces noticeable improvement on the generalization performance measured on independent test sets.
Toward this direction we explore the behavior of two well known and broadly accepted pattern recognition approaches namely the support vector machines (SVM) and a single linear neuron (LN), properly adapted to the problem of marker selection. Within this context we also show how the kernel ability of SVM could be employed in a practical manner to provide alternative ways to approach the problem of reliable marker selection.
We explore how the proposed approaches behave in two application domains (breast cancer and leukemia), achieving comparable or even better results than those reported in the related bibliography. An important advantage of these approaches is their ability to derive stable performance without deteriorating due to the complexity of the application domain. Validation is performed using internal leave one out (ILOO) and 10-fold cross validation as well as independent test set evaluation.
Results show that the proposed methodologies achieve remarkable performance and indicate that applying filter criteria in a wrapper fashion ('wrapper filtering criteria') provides a useful tool for marker selection. The contribution of this study is threefold. First it provides a methodology to apply filter criteria in a wrapper way (which is a new approach), second it introduces a fundamental pattern recognition component namely the single neuron (which is a linear estimator) and explores its behavior on marker selection and third it demonstrates an approach to exploit the kernel ability of SVMs in a practical and effective manner.
到目前为止，DNA微阵列分析中的标记选择问题已通过两种基本类型的方法来解决，即所谓的过滤法和包装法。包装法以递归方式运行，其中特征（基因）权重会被重新评估，并且每次迭代都会动态变化，而在过滤法中，特征权重保持固定。我们在本研究中的目标是表明，以递归方式应用过滤标准，权重可能会逐轮调整，这会在独立测试集上测量的泛化性能方面产生显著改进。
朝着这个方向，我们探索了两种广为人知且被广泛接受的模式识别方法的行为，即支持向量机（SVM）和单个线性神经元（LN），它们经过适当调整以适应标记选择问题。在此背景下，我们还展示了如何以实际方式利用SVM的核能力，以提供解决可靠标记选择问题的替代方法。
我们探索了所提出的方法在两个应用领域（乳腺癌和白血病）中的表现，取得了与相关文献中报道的结果相当甚至更好的结果。这些方法的一个重要优点是它们能够获得稳定的性能，而不会因应用领域的复杂性而恶化。使用内部留一法（ILOO）、10折交叉验证以及独立测试集评估进行验证。
结果表明，所提出的方法具有卓越的性能，并表明以包装方式应用过滤标准（“包装过滤标准”）为标记选择提供了一个有用的工具。本研究的贡献有三个方面。首先，它提供了一种以包装方式应用过滤标准的方法（这是一种新方法）；其次，它引入了一个基本模式识别组件，即单个神经元（这是一种线性估计器），并探索了其在标记选择方面的行为；第三，它展示了一种以实际且有效的方式利用SVM核能力的方法。
