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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. | LitMetric

AI Article Synopsis

  • The study focuses on improving the stage prediction of kidney renal clear cell carcinoma (KIRC) for better diagnosis, treatment, and patient prognosis using a new model called MLW-BFECF.
  • This model leverages multimodal gene datasets (RNA-seq, CNA, and methylation) and employs a dynamic cascade approach with voting techniques to enhance gene feature selection and reduce redundancy.
  • Experiments show that the MLW-BFECF model significantly outperforms previous methods, achieving a high accuracy rate of 88.92% in predicting KIRC stages.

Article Abstract

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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Source
http://dx.doi.org/10.1109/TCBB.2024.3486742DOI Listing

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