Algamal Zakariya Yahya, Lee Muhammad Hisyam
Department of Mathematical Sciences, Universiti Teknologi Malaysia 81310 Skudai, Johor, Malaysia.
Comput Biol Med. 2015 Dec 1;67:136-45. doi: 10.1016/j.compbiomed.2015.10.008. Epub 2015 Oct 24.
Cancer classification and gene selection in high-dimensional data have been popular research topics in genetics and molecular biology. Recently, adaptive regularized logistic regression using the elastic net regularization, which is called the adaptive elastic net, has been successfully applied in high-dimensional cancer classification to tackle both estimating the gene coefficients and performing gene selection simultaneously. The adaptive elastic net originally used elastic net estimates as the initial weight, however, using this weight may not be preferable for certain reasons: First, the elastic net estimator is biased in selecting genes. Second, it does not perform well when the pairwise correlations between variables are not high. Adjusted adaptive regularized logistic regression (AAElastic) is proposed to address these issues and encourage grouping effects simultaneously. The real data results indicate that AAElastic is significantly consistent in selecting genes compared to the other three competitor regularization methods. Additionally, the classification performance of AAElastic is comparable to the adaptive elastic net and better than other regularization methods. Thus, we can conclude that AAElastic is a reliable adaptive regularized logistic regression method in the field of high-dimensional cancer classification.
癌症分类和高维数据中的基因选择一直是遗传学和分子生物学领域的热门研究课题。最近，使用弹性网络正则化的自适应正则化逻辑回归（称为自适应弹性网络）已成功应用于高维癌症分类，以同时解决估计基因系数和进行基因选择的问题。自适应弹性网络最初使用弹性网络估计作为初始权重，然而，由于某些原因，使用此权重可能并不理想：首先，弹性网络估计器在选择基因时存在偏差。其次，当变量之间的成对相关性不高时，它的表现不佳。为了解决这些问题并同时鼓励分组效应，提出了调整后的自适应正则化逻辑回归（AAElastic）。实际数据结果表明，与其他三种竞争正则化方法相比，AAElastic在选择基因方面具有显著的一致性。此外，AAElastic的分类性能与自适应弹性网络相当，且优于其他正则化方法。因此，我们可以得出结论，AAElastic是高维癌症分类领域中一种可靠的自适应正则化逻辑回归方法。
