ArticleSaudi journal of biological sciences2017
An enhanced topologically significant directed random walk in cancer classification using gene expression datasets.
Article in Saudi journal of biological sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- A bibliometric analysis of malignant pleural mesothelioma from 2010 to 2023.Journal of thoracic disease · 2025Article
- Robustness evaluations of pathway activity inference methods on gene expression data.BMC bioinformatics · 2024Article
- Article
- From Infection Clusters to Metal Clusters: Significance of the Lowest Occupied Molecular Orbital (LOMO).ACS omega · 2021Article
- Performance Assessment of the Network Reconstruction Approaches on Various Interactomes.Frontiers in molecular biosciences · 2021Article
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7 authors.
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Abstract
Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway dataset is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between significant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification.
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