Gene ranking: An entropy &amp; decision tree based approach

Seal, Dibyendu Bikash; Saha, Sujay; Mukherjee, Prokriti; Chatterjee, Mayukh; Mukherjee, Aradhita; Dey, Kashi Nath

doi:10.1109/uemcon.2016.7777837

Cited by 4 publications

(1 citation statement)

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“…Here we also consider a set of alternative scores for selecting informative genes, i.e., genes affected by the subtype. Aside SD, other examples within the category of variability scores include, e.g., the interquartile range (IQR) and measures based on entropy (Liu et al, 2005;Seal et al, 2016). If instead it is assumed that informative genes are likely to be expressed at a relatively high level it makes sense to select highly expressed genes.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Methods for Feature Selection in Clustering of High-Dimensional RNA-Sequencing Data to Identify Cancer Subtypes

2021

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Cancer subtype identification is important to facilitate cancer diagnosis and select effective treatments. Clustering of cancer patients based on high-dimensional RNA-sequencing data can be used to detect novel subtypes, but only a subset of the features (e.g., genes) contains information related to the cancer subtype. Therefore, it is reasonable to assume that the clustering should be based on a set of carefully selected features rather than all features. Several feature selection methods have been proposed, but how and when to use these methods are still poorly understood. Thirteen feature selection methods were evaluated on four human cancer data sets, all with known subtypes (gold standards), which were only used for evaluation. The methods were characterized by considering mean expression and standard deviation (SD) of the selected genes, the overlap with other methods and their clustering performance, obtained comparing the clustering result with the gold standard using the adjusted Rand index (ARI). The results were compared to a supervised approach as a positive control and two negative controls in which either a random selection of genes or all genes were included. For all data sets, the best feature selection approach outperformed the negative control and for two data sets the gain was substantial with ARI increasing from (−0.01, 0.39) to (0.66, 0.72), respectively. No feature selection method completely outperformed the others but using the dip-rest statistic to select 1000 genes was overall a good choice. The commonly used approach, where genes with the highest SDs are selected, did not perform well in our study.

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Section: Introductionmentioning

confidence: 99%