In view of the challenges of the group Lasso penalty methods for multicancer microarray data analysis, e.g., dividing genes into groups in advance and biological interpretability, we propose a robust adaptive multinomial regression with sparse group Lasso penalty (RAMRSGL) model. By adopting the overlapping clustering strategy, affinity propagation clustering is employed to obtain each cancer gene subtype, which explores the group structure of each cancer subtype and merges the groups of all subtypes. In addition, the data-driven weights based on noise are added to the sparse group Lasso penalty, combining with the multinomial log-likelihood function to perform multiclassification and adaptive group gene selection simultaneously. The experimental results on acute leukemia data verify the effectiveness of the proposed method.

中文翻译：

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RAMRSGL：用于多癌分类的稳健自适应多项回归模型

鉴于组套索惩罚方法对多癌微阵列数据分析的挑战，例如，提前将基因分组和生物可解释性，我们提出了一种具有稀疏组套索惩罚（RAMRSGL）模型的鲁棒自适应多项式回归。通过采用重叠聚类策略，通过亲和传播聚类获得每个癌症基因亚型，探索每个癌症亚型的组结构，合并所有亚型的组。此外，在稀疏组套索惩罚中加入基于噪声的数据驱动权重，结合多项式对数似然函数，同时进行多分类和自适应组基因选择。急性白血病数据的实验结果验证了该方法的有效性。