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. 2021 Mar 22;22(1):143.
doi: 10.1186/s12859-021-04062-2.

Weighted minimum feedback vertex sets and implementation in human cancer genes detection

Ruiming Li  1 , Chun-Yu Lin  1   2   3 , Wei-Feng Guo  4 , Tatsuya Akutsu  5
Affiliations

Weighted minimum feedback vertex sets and implementation in human cancer genes detection

Ruiming Li et al. BMC Bioinformatics. .

Abstract

Background: Recently, many computational methods have been proposed to predict cancer genes. One typical kind of method is to find the differentially expressed genes between tumour and normal samples. However, there are also some genes, for example, 'dark' genes, that play important roles at the network level but are difficult to find by traditional differential gene expression analysis. In addition, network controllability methods, such as the minimum feedback vertex set (MFVS) method, have been used frequently in cancer gene prediction. However, the weights of vertices (or genes) are ignored in the traditional MFVS methods, leading to difficulty in finding the optimal solution because of the existence of many possible MFVSs.

Results: Here, we introduce a novel method, called weighted MFVS (WMFVS), which integrates the gene differential expression value with MFVS to select the maximum-weighted MFVS from all possible MFVSs in a protein interaction network. Our experimental results show that WMFVS achieves better performance than using traditional bio-data or network-data analyses alone.

Conclusion: This method balances the advantage of differential gene expression analyses and network analyses, improves the low accuracy of differential gene expression analyses and decreases the instability of pure network analyses. Furthermore, WMFVS can be easily applied to various kinds of networks, providing a useful framework for data analysis and prediction.

Keywords: Cancer gene; Differential gene expression; Feedback vertex set.

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Conflict of interest statement

TA is an Associate Editor of BMC Bioinformatics.

Figures

Fig. 1
Fig. 1
The experiment flowchart. The red, blue and green lines correspond to the WMFVS, WFVS and random MFVS pipelines, respectively
Fig. 2
Fig. 2
Distributions of the recalls for the random MFVSs (boxplot), the WMFVS method (orange circle), and the WFVS method (cyan circle) on different cancer gene data sets
Fig. 3
Fig. 3
The recalls and precisions of all the methods
Fig. 4
Fig. 4
The enrichment score of each data set
Fig. 5
Fig. 5
Comparison of the precision of the all-DEG set, top-463 DEG set, WMFVS set and WFVS set in five different cancer gene data sets. ‘DG’ and ‘NDG’ represent the ratios of dark genes and non-dark genes, respectively. Note that the all-DEG and the top-463 DEG sets contain no dark genes
Fig. 6
Fig. 6
A simple example. In this case, the total weight may be more important than the size of an FVS
See this image and copyright information in PMC

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