Minimizing Redundancy Among Genes Selected Based on the Overlapping Analysis

Mahmoud, Osama; Harrison, Andrew; Gul, Asma; Khan, Zardad; Metodiev, Metodi V.; Lausen, Berthold

doi:10.1007/978-3-319-25226-1_24

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…To evaluate Slope-Hunter’s estimations of the G 1 class membership probabilities, we calculated the mean probability for the SNPs identified as the G 1 class. Moreover, the misclassification error rate [18, 19] was obtained by comparing Slope-Hunter’s assignments of the SNPs to the G 1 class with their true class status.…”

Section: Methodsmentioning

confidence: 99%

Slope-Hunter: A robust method for index-event bias correction in genome-wide association studies of subsequent traits

Mahmoud

Dudbridge

Smith

et al. 2020

Preprint

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BackgroundStudying genetic associations with prognosis (e.g. survival, disability, subsequent disease events) is problematic due to selection bias - also termed index event bias or collider bias - whereby selection on disease status can induce associations between causes of incidence with prognosis. A current method for adjusting genetic associations for this bias assumes there is no genetic correlation between incidence and prognosis, which may not be a plausible assumption.MethodsWe propose an alternative, the ‘Slope-Hunter’ approach, which is unbiased even when there is genetic correlation between incidence and prognosis. Our approach has two stages. First, we use cluster-based techniques to identify: variants affecting neither incidence nor prognosis (these should not suffer bias and only a random sub-sample of them are retained in the analysis); variants affecting prognosis only (excluded from the analysis). Second, we fit a cluster-based model to identify the class of variants only affecting incidence, and use this class to estimate the adjustment factor.ResultsSimulation studies showed that the Slope-Hunter method reduces type-1 error by between 49%-85%, increases power by 1%-36%, reduces bias by 17%-47% compared to other methods in the presence of genetic correlation and performs as well as previous methods when there is no genetic correlation. Slope-Hunter and the previous methods perform less well as the proportion of variation in incidence explained by genetic variants affecting only incidence decreases.ConclusionsThe key assumption of Slope-Hunter is that the contribution of the set of genetic variants affecting incidence only to the heritability of incidence is at least as large as the contribution of those affecting both incidence and prognosis. When this assumption holds, our approach is unbiased in the presence of genetic correlation between incidence and progression, and performs no worse than alternative approaches even when there is no correlation. Bias-adjusting methods should be used to carry out causal analyses when conditioning on incidence.

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Section: Methodsmentioning

confidence: 99%

Slope-Hunter: A robust method for index-event bias correction in genome-wide association studies of subsequent traits

Mahmoud

Dudbridge

Smith

et al. 2020

Preprint

Self Cite

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“…By this evaluation, one could check the ability of genes that regulate the tissue target class. Authors have used different gene selection methods and have shown that gene selection methods have significant effect on a classifier accuracy 25,26 . The same approach has been utilized in another study 14 .…”

Section: P R O V I S I O N a L L Y A C C E P T E D F O R P U B L I C A T I O Nmentioning

confidence: 99%

Regulatory genes identification within functional genomics experiments for tissue classification into binary classes via machine learning techniques

et al. 2020

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Abstract Objectives: The aim of this study is to filter out the most informative genes that mainly regulate the target tissue class, increase classification accuracy, reduce the curse of dimensionality, and discard redundant and irrelevant genes. Methods: This paper presented the idea of gene selection using bagging sub-forest (BSF). The proposed method provided genes importance grounded on the idea specified in the standard random forest algorithm. The new method is compared with three state-of-the art methods, i.e., Wilcoxon, masked painter and proportional overlapped score (POS). These methods were applied on 5 data sets, i.e. Colon, Lymph node breast cancer, Leukemia, Serrated colorectal carcinomas, and Breast Cancer. Comparison was done by selecting top 20 genes by applying the gene selection methods and applying random forest (RF) and support vector machine (SVM) classifiers to assess their predictive performance on the datasets with selected genes. Classification accuracy, Brier score, and sensitivity have been used as performance measures. Results: The proposed method gave better results than the other methods using both random forest and SVM classifiers on all the datasets among all the feature selection methods. Conclusion: The proposed method showed improved performance in terms of classification accuracy, Brier score and sensitivity, and hence, could be used as a novel method for gene selection to classify tissue samples into their correct classes. Key Words: Gene selection, classification, random forest, cancer, microarray gene expression.

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Minimizing Redundancy Among Genes Selected Based on the Overlapping Analysis

Cited by 2 publications

References 14 publications

Slope-Hunter: A robust method for index-event bias correction in genome-wide association studies of subsequent traits

Slope-Hunter: A robust method for index-event bias correction in genome-wide association studies of subsequent traits

Regulatory genes identification within functional genomics experiments for tissue classification into binary classes via machine learning techniques

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