An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks

Hui, Tay Xin; Kasim, Shahreen; Sutikno, Tole; Fudzee, Mohd Farhan Md; Hassan, Rohayanti; Akhir, Emelia Akashah Patah; Aziz, Norshakirah; Seah, Choon Sen

doi:10.3390/genes14030574

Cited by 3 publications

(1 citation statement)

References 64 publications

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“…It aims to enhance the sensitivity of cancer prediction in conventional sDRW algorithm by introducing a walker network to identify significant genes in both networks. Meanwhile, entropy-based Directed Random Walk (e-DRW) [70] performs RWR on two separated networks to prioritise disease genes. It constructs separate biological networks from different pathway databases to improve the coverage of pathway information for random walking.…”

Section: ) Random Walk Methods Based On Node Classificationmentioning

confidence: 99%

A Review of Random Walk-Based Method for the Identification of Disease Genes and Disease Modules

Hui,

Kasim,

Fudzee

et al. 2023

IEEE Access

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Traditional techniques for identifying disease genes and disease modules involve high-cost clinical experiments and unpredictable time consumption for analysis. Network-based computational approaches usually focus on the systematic study of molecular networks to predict the associations between diseases and genes. The random walk-based method is a network-based approach that utilises biological networks for analysis. As the random walk models efficiently capture the complex interplay among molecules in diseases, it is extensively applied in biological problem-solving based on networks. Despite their comprehensive employment, the fundamentals of random walk and overall background may not be fully understood, leading to misinterpretation of results. This review aims to cover the fundamental knowledge of random walk models for biological network analysis. This study reviewed diffusion-based random walk methods for disease gene prediction and disease module identification. The random walk-based disease gene prediction methods are categorised into node classification and link prediction tasks. This study details the advantages and limitations of each method. Finally, the potential challenges and research directions for future studies on random walk models are highlighted.

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Section: ) Random Walk Methods Based On Node Classificationmentioning

confidence: 99%

A Review of Random Walk-Based Method for the Identification of Disease Genes and Disease Modules

Hui,

Kasim,

Fudzee

et al. 2023

IEEE Access

Self Cite

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Robustness evaluations of pathway activity inference methods on gene expression data

Hui,

Kasim,

Aziz

et al. 2024

BMC Bioinformatics

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Background With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profiles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and Pathway Topology-Based (PTB) methods. Although some review and survey articles discussed the topic from different aspects, there is a lack of systematic assessment and comparisons on the robustness of these approaches. Results Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The first assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identified informative pathways and genes were evaluated. Based on the first assessment, the mean reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk (e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment shows that no methods provide satisfactory results across datasets. Conclusion However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared to non-TB methods.

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An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks

Hui

Kasim

Sutikno

et al. 2023

Genes

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The integration of microarray technologies and machine learning methods has become popular in predicting the pathological condition of diseases and discovering risk genes. Traditional microarray analysis considers pathways as a simple gene set, treating all genes in the pathway identically while ignoring the pathway network’s structure information. This study proposed an entropy-based directed random walk (e-DRW) method to infer pathway activities. Two enhancements from the conventional DRW were conducted, which are (1) to increase the coverage of human pathway information by constructing two inputting networks for pathway activity inference, and (2) to enhance the gene-weighting method in DRW by incorporating correlation coefficient values and T-test statistic scores. To test the objectives, gene expression datasets were used as input datasets while the pathway datasets were used as reference datasets to build two directed graphs. The within-dataset experiments indicated that e-DRW method demonstrated robust and superior performance in terms of classification accuracy and robustness of the predicted risk-active pathways compared to the other methods. In conclusion, the results revealed that e-DRW not only improved the prediction performance, but also effectively extracted topologically important pathways and genes that were specifically related to the corresponding cancer types.

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An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks

Cited by 3 publications

References 64 publications

A Review of Random Walk-Based Method for the Identification of Disease Genes and Disease Modules

A Review of Random Walk-Based Method for the Identification of Disease Genes and Disease Modules

Robustness evaluations of pathway activity inference methods on gene expression data

An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks

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