Hunan University, Changsha, China.
Comput Biol Med. 2010 Feb;40(2):179-89. doi: 10.1016/j.compbiomed.2009.11.014. Epub 2009 Dec 30.
Since Golub applied gene expression profiles (GEP) to the molecular classification of tumor subtypes for more accurately and reliably clinical diagnosis, a number of studies on GEP-based tumor classification have been done. However, the challenges from high dimension and small sample size of tumor dataset still exist. This paper presents a new tumor classification approach based on an ensemble of probabilistic neural network (PNN) and neighborhood rough set model based gene reduction. Informative genes were initially selected by gene ranking based on an iterative search margin algorithm and then were further refined by gene reduction to select many minimum gene subsets. Finally, the candidate base PNN classifiers trained by each of the selected gene subsets were integrated by majority voting strategy to construct an ensemble classifier. Experiments on tumor datasets showed that this approach can obtain both high and stable classification performance, which is not too sensitive to the number of initially selected genes and competitive to most existing methods. Additionally, the classification results can be cross-verified in a single biomedical experiment by the selected gene subsets, and biologically experimental results also proved that the genes included in the selected gene subsets are functionally related to carcinogenesis, indicating that the performance obtained by the proposed method is convincing.
自 Golub 将基因表达谱（GEP）应用于肿瘤亚类的分子分类以实现更准确和可靠的临床诊断以来，已经有许多基于 GEP 的肿瘤分类研究。然而，肿瘤数据集的高维性和小样本量仍然存在挑战。本文提出了一种新的基于概率神经网络（PNN）集成和基于邻域粗糙集模型的基因约简的肿瘤分类方法。通过基于迭代搜索边距算法的基因排序，最初选择有信息的基因，然后通过基因约简进一步选择许多最小的基因子集。最后，通过多数投票策略集成由每个所选基因子集训练的候选基 PNN 分类器，构建集成分类器。在肿瘤数据集上的实验表明，该方法可以获得高且稳定的分类性能，对最初选择的基因数量不太敏感，并且与大多数现有方法具有竞争力。此外，通过所选基因子集可以在单个生物医学实验中交叉验证分类结果，并且生物学实验结果也证明了所选基因子集中包含的基因与肿瘤发生功能相关，表明所提出的方法获得的性能是令人信服的。
