Pathway analysis using random forests with bivariate node-split for survival outcomes

Pang, Herbert; Datta, Debayan; Zhao, Hongyu

doi:10.1093/bioinformatics/btp640

Cited by 37 publications

(32 citation statements)

References 65 publications

(70 reference statements)

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“…Cutler and Breiman's Random Forest (RF) algorithm [16] is a relatively new ensemble-based method that has quickly gained popularity in a wide range of disciplines, including bioinformatics, [17][18][19][20] and QSAR. [21][22][23][24] The approach's popularity is likely to be due to its consistency in producing models with high prediction accuracy, and that it is considered to be almost immune to overfitting of the training data.…”

Section: Random Forestsmentioning

confidence: 99%

Automated QSAR with a Hierarchy of Global and Local Models

Wood¹,

Buttar²,

Cumming³

et al. 2011

Molecular Informatics

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We present an automated QSAR procedure that is used in AstraZeneca's AutoQSAR system. The approach involves automatically selecting the most predictive models from pools of both global and local models. The effectiveness of this QSAR modelling strategy is demonstrated with a retrospective study that uses a diverse selection of 9 early stage AstraZeneca drug discovery projects and 3 physicochemical endpoints: LogD; solubility and human plasma protein binding. We show that the strategy makes a statistically significant improvement to the accuracy of predictions when compared to an updating global strategy, and that the systematic biases inherent in the global model predictions are almost completely removed. This improvement is attributed to the model selection aspect of the strategy.

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Section: Random Forestsmentioning

confidence: 99%

Automated QSAR with a Hierarchy of Global and Local Models

Wood¹,

Buttar²,

Cumming³

et al. 2011

Molecular Informatics

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“…It can also provide a robust and synergistic multivariate descriptor of disease complexity via explicit incorporation of the interaction (synergistic) effects of the individual predictors, thereby allowing investigation of a possible classification or prediction model as well as optimizing the predictor subset in input-output interconnections and personalized dose-response relations [10, 44, 45]. Despite known loss of statistical power following dichotomization in the univariate case and in the linear multivariate regression models, it has been shown by many studies that dichotomizing continuous data can greatly improve the power of multiple testing procedures (even in false discovery rate controlling methods) [43].…”

Section: Discussionmentioning

confidence: 99%

Big data and computational biology strategy for personalized prognosis

Tang

Kuznetsov

2016

Oncotarget

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The era of big data and precision medicine has led to accumulation of massive datasets of gene expression data and clinical information of patients. For a new patient, we propose that identification of a highly similar reference patient from an existing patient database via similarity matching of both clinical and expression data could be useful for predicting the prognostic risk or therapeutic efficacy.Here, we propose a novel methodology to predict disease/treatment outcome via analysis of the similarity between any pair of patients who are each characterized by a certain set of pre-defined biological variables (biomarkers or clinical features) represented initially as a prognostic binary variable vector (PBVV) and subsequently transformed to a prognostic signature vector (PSV). Our analyses revealed that Euclidean distance rather correlation distance measure was effective in defining an unbiased similarity measure calculated between two PSVs.We implemented our methods to high-grade serous ovarian cancer (HGSC) based on a 36-mRNA predictor that was previously shown to stratify patients into 3 distinct prognostic subgroups. We studied and revealed that patient's age, when converted into binary variable, was positively correlated with the overall risk of succumbing to the disease. When applied to an independent testing dataset, the inclusion of age into the molecular predictor provided more robust personalized prognosis of overall survival correlated with the therapeutic response of HGSC and provided benefit for treatment targeting of the tumors in HGSC patients.Finally, our method can be generalized and implemented in many other diseases to accurately predict personalized patients’ outcomes.

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“…Survival outcome has been studied by several researchers ([16], [41], [33]). In addition, our approach is suitable for modeling continuous outcome, but there are data with binary phenotype of interest.…”

Section: Discussionmentioning

confidence: 99%

Random Effects Model for Multiple Pathway Analysis with Applications to Type II Diabetes Microarray Data

2014

Self Cite

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Close to three percent of the world’s population suffer from diabetes. Despite the range of treatment options available for diabetes patients, not all patients benefit from them. Investigating how different pathways correlate with phenotype of interest may help unravel novel drug targets and discover a possible cure. Many pathway-based methods have been developed to incorporate biological knowledge into the study of microarray data. Most of these methods can only analyze individual pathways but cannot deal with two or more pathways in a model based framework. This represents a serious limitation because, like genes, individual pathways do not work in isolation, and joint modeling may enable researchers to uncover patterns not seen in individual pathway-based analysis. In this paper, we propose a random effects model to analyze two or more pathways. We also derive score test statistics for significance of pathway effects. We apply our method to a microarray study of Type II diabetes. Our method may eludicate how pathways crosstalk with each other and facilitate the investigation of pathway crosstalks. Further hypothesis on the biological mechanisms underlying the disease and traits of interest may be generated and tested based on this method.

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Pathway analysis using random forests with bivariate node-split for survival outcomes

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 37 publications

References 65 publications

Automated QSAR with a Hierarchy of Global and Local Models

Automated QSAR with a Hierarchy of Global and Local Models

Big data and computational biology strategy for personalized prognosis

Random Effects Model for Multiple Pathway Analysis with Applications to Type II Diabetes Microarray Data

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