KISL: knowledge-injected semi-supervised learning for biological co-expression network modules

Xiao，Gangyi1; Guan，Renchu1; Cao，Yangkun2; Huang，Zhenyu1; Xu，Ying3

2023

10.3389/fgene.2023.1151962

Frontiers in Genetics   影响因子和分区

1664-8021

The exploration of important biomarkers associated with cancer development is crucial for diagnosing cancer, designing therapeutic interventions, and predicting prognoses. The analysis of gene co-expression provides a systemic perspective on gene networks and can be a valuable tool for mining biomarkers. The main objective of co-expression network analysis is to discover highly synergistic sets of genes, and the most widely used method is weighted gene co-expression network analysis (WGCNA). With the Pearson correlation coefficient, WGCNA measures gene correlation, and uses hierarchical clustering to identify gene modules. The Pearson correlation coefficient reflects only the linear dependence between variables, and the main drawback of hierarchical clustering is that once two objects are clustered together, the process cannot be reversed. Hence, readjusting inappropriate cluster divisions is not possible. Existing co-expression network analysis methods rely on unsupervised methods that do not utilize prior biological knowledge for module delineation. Here we present a method for identification of outstanding modules in a co-expression network using a knowledge-injected semi-supervised learning approach (KISL), which utilizes apriori biological knowledge and a semi-supervised clustering method to address the issue existing in the current GCN-based clustering methods. To measure the linear and non-linear dependence between genes, we introduce a distance correlation due to the complexity of the gene-gene relationship. Eight RNA-seq datasets of cancer samples are used to validate its effectiveness. In all eight datasets, the KISL algorithm outperformed WGCNA when comparing the silhouette coefficient, Calinski-Harabasz index and Davies-Bouldin index evaluation metrics. According to the results, KISL clusters had better cluster evaluation values and better gene module aggregation. Enrichment analysis of the recognition modules demonstrated their effectiveness in discovering modular structures in biological co-expression networks. In addition, as a general method, KISL can be applied to various co-expression network analyses based on similarity metrics. Source codes for the KISL and the related scripts are available online at https://github.com/Mowonhoo/KISL.git.

biological co-expression network factor analysis feature selection network modules identification semi-supervised learning algorithm

SCI

英语

通讯

National Key Research and Development Program of China[2021YFF1201200] ; National Natural Science Foundation of China["62172187","61972174"] ; Liaoning Provincial Archives Science and Technology Project["2021-X-012","2022-X-017"] ; Guangdong Universities' Innovation Team Project[2021KCXTD015] ; Guangdong Key Disciplines Project[2021ZDJS138]

Genetics & Heredity

Genetics & Heredity

WOS:000999288600001

FRONTIERS MEDIA SA

2-s2.0-85159368938

Scopus

1.College of Computer Science and Technology,Jilin University,Changchun,China
2.School of Artificial Intelligence,Jilin University,Changchun,China
3.School of Medicine,Southern University of Science and Technology,Shenzhen,Guangdong,China

Xiao，Gangyi,Guan，Renchu,Cao，Yangkun,et al. KISL: knowledge-injected semi-supervised learning for biological co-expression network modules[J]. Frontiers in Genetics,2023,14.

Xiao，Gangyi,Guan，Renchu,Cao，Yangkun,Huang，Zhenyu,&Xu，Ying.(2023).KISL: knowledge-injected semi-supervised learning for biological co-expression network modules.Frontiers in Genetics,14.

Xiao，Gangyi,et al."KISL: knowledge-injected semi-supervised learning for biological co-expression network modules".Frontiers in Genetics 14(2023).