Power of Data Mining Methods to Detect Genetic Associations and Interactions

Carriero, Nicholas; Bjornson, Robert; Hartge, Patricia; Rothman, Nathaniel; Chatterjee, Nilanjan

doi:10.1159/000330579

Cited by 23 publications

(21 citation statements)

References 52 publications

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“…This good performance is documented in several independent comparison studies implementing different simulation settings [26,54,73,17]. In these studies, however, standard VIMs (either Gini or permutation) are used to rank the SNPs.…”

Section: Predictors Involved In Interactionsmentioning

confidence: 87%

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Boulesteix

Janitza

Kruppa

et al. 2012

WIREs Data Min & Knowl

632

455

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The Random Forest (RF) algorithm by Leo Breiman has become a standard data analysis tool in bioinformatics. It has shown excellent performance in settings where the number of variables is much larger than the number of observations, can cope with complex interaction structures as well as highly correlated variables and returns measures of variable importance. This paper synthesizes ten years of RF development with emphasis on applications to bioinformatics and computational biology. Special attention is given to practical aspects such as the selection of parameters, available RF implementations, and important pitfalls and biases of RF and its variable importance measures (VIMs). The paper surveys recent developments of the methodology relevant to bioinformatics as well as some representative examples of RF applications in this context and possible directions for future research.

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Section: Predictors Involved In Interactionsmentioning

confidence: 87%

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Boulesteix

Janitza

Kruppa

et al. 2012

WIREs Data Min & Knowl

632

455

View full text Add to dashboard Cite

show abstract

“…Variable importance rankings were also derived within the R randomForest procedure using the Breiman–Cutler permutation strategy (Breiman and Cutler, 2001), which has good performance and comparative efficacy (Molinaro et al ., 2011). This procedure estimates, for each variable, the degree to which random permutation of its values decreases prediction accuracy of the machine, while retaining original values of all other variables.…”

Section: Methodsmentioning

confidence: 99%

Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy

Askland

Garnaat

Sibrava

et al. 2015

Int J Methods Psych Res

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The study objective was to apply machine learning methodologies to identify predictors of remission in a longitudinal sample of 296 adults with a primary diagnosis of obsessive compulsive disorder (OCD). Random Forests is an ensemble machine learning algorithm that has been successfully applied to large-scale data analysis across vast biomedical disciplines, though rarely in psychiatric research or for application to longitudinal data. When provided with 795 raw and composite scores primarily from baseline measures, Random Forest regression prediction explained 50.8% (5000-run average, 95% bootstrap confidence interval [CI]: 50.3–51.3%) of the variance in proportion of time spent remitted. Machine performance improved when only the most predictive 24 items were used in a reduced analysis. Consistently high-ranked predictors of longitudinal remission included Yale–Brown Obsessive Compulsive Scale (Y-BOCS) items, NEO items and subscale scores, Y-BOCS symptom checklist cleaning/washing compulsion score, and several self-report items from social adjustment scales. Random Forest classification was able to distinguish participants according to binary remission outcomes with an error rate of 24.6% (95% bootstrap CI: 22.9–26.2%). Our results suggest that clinically-useful prediction of remission may not require an extensive battery of measures. Rather, a small set of assessment items may efficiently distinguish high- and lower-risk patients and inform clinical decision-making.

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“…The power of the variable importance measure to discriminate between relevant and non-relevant variables was poor, too. The approach of Altmann et al (2010), which showed high power in other studies (Molinaro et al;2011;Hapfelmeier and Ulm;, also had very low statistical power in our studies. This discrepancy is likely related to the fact that the existing studies included only a few variables, while our studies are based on several thousands of variables.…”

Section: Discussionmentioning

confidence: 52%

A computationally fast variable importance test for random forests for high-dimensional data

Janitza

Celik

Boulesteix

2016

Adv Data Anal Classif

151

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Random forests are a commonly used tool for classification with high-dimensional data as well as for ranking candidate predictors based on the so-called variable importance measures. There are different importance measures for ranking predictor variables, the two most common measures are the Gini importance and the permutation importance. The latter has been found to be more reliable than the Gini importance. It is computed from the change in prediction accuracy when removing any association between the response and a predictor variable, with large changes indicating that the predictor variable is important. A drawback of those variable importance measures is that there is no natural cutoff that can be used to discriminate between important and non-important variables. Several approaches, for example approaches based on hypothesis testing, have been developed for addressing this problem. The existing testing approaches are permutation-based and require the repeated computation of forests. While for low-dimensional settings those permutation-based approaches might be computationally tractable, for high-dimensional settings typically including thousands of candidate predictors, computing time is enormous. A new computationally fast heuristic procedure of a variable importance test is proposed, that is appropriate for high-dimensional data where many variables do not carry any information. The testing approach is based on a modified version of the permutation variable importance measure, which is inspired by cross-validation procedures. The novel testing approach is tested and compared to the permutation-based testing approach of Altmann and colleagues using studies on complex high-dimensional binary classification settings. The new approach controlled the type I error and had at least comparable power at a substantially smaller computation time in our studies. The new variable importance test is implemented in the R package vita.

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Power of Data Mining Methods to Detect Genetic Associations and Interactions

Cited by 23 publications

References 52 publications

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy

A computationally fast variable importance test for random forests for high-dimensional data

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