MLW-BFECF:基于双线性特征提取的多加权动态级联森林,用于在多模态基因数据上预测肾透明细胞癌的分期。

IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Liye Jia, Liancheng Jiang, Junhong Yue, Fang Hao, Yongfei Wu, Xilin Liu
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引用次数: 0

摘要

肾透明细胞癌(KIRC)的分期预测对于患者的诊断、个性化治疗和预后都非常重要。目前已提出了许多预测方法,但大多基于单模态基因数据,其准确性难以进一步提高。因此,我们提出了一种基于双线性特征提取的新型多权重动态级联森林(MLW-BFECF)模型,利用多模态基因数据集(RNA-seq、CNA 和甲基化)对 KIRC 进行分期预测。该模型采用动态级联框架和洗牌层,以防止模型的早期退化。在每个级联层中,首先采用基于三种基因选择算法的投票技术,以有效保留与 KIRC 更为相关的基因特征,并消除基因特征中的冗余信息。然后,提出了基于门控注意机制的两个新的双线性模型,以更好地提取新的模内和模间基因特征;最后,基于bagging的思想,提出了多加权集合森林分类器模块,以提取和融合三模态基因数据的概率特征。一系列实验证明,基于三模态 KIRC 数据集的 MLW-BFECF 模型预测准确率高达 88.92%,预测性能最高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MLW-BFECF: a multi-weighted dynamic cascade forest based on bilinear feature extraction for predicting the stage of Kidney Renal Clear Cell Carcinoma on multi-modal gene data.

The stage prediction of kidney renal clear cell carcinoma (KIRC) is important for the diagnosis, personalized treatment, and prognosis of patients. Many prediction methods have been proposed, but most of them are based on unimodal gene data, and their accuracy is difficult to further improve. Therefore, we propose a novel multi-weighted dynamic cascade forest based on the bilinear feature extraction (MLW-BFECF) model for stage prediction of KIRC using multimodal gene datasets (RNA-seq, CNA, and methylation). The proposed model utilizes a dynamic cascade framework with shuffle layers to prevent early degradation of the model. In each cascade layer, a voting technique based on three gene selection algorithms is first employed to effectively retain gene features more relevant to KIRC and eliminate redundant information in gene features. Then, two new bilinear models based on the gated attention mechanism are proposed to better extract new intra-modal and inter-modal gene features; Finally, based on the idea of the bagging, a multi-weighted ensemble forest classifiers module is proposed to extract and fuse probabilistic features of the three-modal gene data. A series of experiments demonstrate that the MLW-BFECF model based on the three-modal KIRC dataset achieves the highest prediction performance with an accuracy of 88.92%.

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来源期刊
CiteScore
7.50
自引率
6.70%
发文量
479
审稿时长
3 months
期刊介绍: IEEE/ACM Transactions on Computational Biology and Bioinformatics emphasizes the algorithmic, mathematical, statistical and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development of biological databases; and important biological results that are obtained from the use of these methods, programs and databases; the emerging field of Systems Biology, where many forms of data are used to create a computer-based model of a complex biological system
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