Abstract：
:With the development of cancer research, various gene expression datasets containing cancer information show an explosive growth trend. In addition, due to the continuous maturity of single-cell RNA sequencing (scRNA-seq) technology, the protein information and pedigree information of a single cell are also continuously mined. It is a technical problem of how to classify these high-dimensional data correctly. In recent years, Extreme Learning Machine (ELM) has been widely used in the field of supervised learning and unsupervised learning. However, the traditional ELM does not consider the robustness of the method. To improve the robustness of ELM, in this paper, a novel ELM method based on L2,1-norm named L2,1-Extreme Learning Machine (L2,1 -ELM) has been proposed. The method introduces L2,1-norm on loss function to improve the robustness, and minimizes the influence of noise and outliers. Firstly, we evaluate the new method on five UCI datasets. The experiment results prove that our method can achieve competitive results. Next, the novel method is applied to the problem of classification of cancer samples and single-cell RNA sequencing datasets. The experimental results on The Cancer Genome Atlas (TCGA) datasets and scRNA-seq datasets prove that ELM and its variants has great potential in the classification of cancer samples.
journal_name
Comput Biol Chemjournal_title
Computational biology and chemistryauthors
Ren LR,Gao YL,Liu JX,Zhu R,Kong XZdoi
10.1016/j.compbiolchem.2020.107368subject
Has Abstractpub_date
2020-12-01 00:00:00pages
107368eissn
1476-9271issn
1476-928Xpii
S1476-9271(19)31035-7journal_volume
89pub_type
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