Efficient Gene Selection for Cancer Prognostic Biomarkers Using Swarm Optimization and Survival Analysis

Aguirre-Gamboa, Raúl; Martinez-Ledesma, Emmanuel; Gómez-Rueda, Hugo; Palacios, Rebeca; Fuentes-Hernandez, Isabel; Sánchez-Canales, Emilio; Chacolla-Huaringa, Rafael; Cardona‐Huerta, Servando; Villela, Luís; Scott, Sean-Patrick; Tamez-Peña, José G.; Treviño, Víctor

doi:10.2174/1574893611999160610125628

Cited by 3 publications

(1 citation statement)

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“…Model selection via the Bootstrap Step-Wise Model selection (BSWiMS), and Best Subset Selection (BeSS) are among two of the machine learning options readily available to researchers [35], [36]. Besides these approaches, feature selection (FS) is a common method used to build Cox models [37], [38]. The wide variety of methods available to researchers can make biomarker discovery a complex effort, especially when there is no clear choice of methodology for building/exploring survival models.…”

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confidence: 99%

Exploring Survival Models Associated with MCI to AD Conversion: A Machine Learning Approach

Orozco-Sanchez

Treviño

Martinez-Ledesma

et al. 2019

Preprint

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Several studies have documented that structural MRI findings are associated with the presence of earlystage Alzheimer Disease (AD). However, the association of each MRI feature with the rate of conversion from mild cognitive impairment (MCI) to AD in a multivariate setting has not been studied fully. The objective of this work is the comprehensive exploration of four different machine learning (ML) strategies to build MRI-based multivariate Cox regression models. These models evaluated the association of MRI features with the time of MCI to clinical AD conversion. We used 442 MCI subjects from the Alzheimer's disease Neuroimaging Initiative (ADNI) study. Each subject was described by 346 MRI features and time to AD conversion. Cox regression models then estimated the rate of conversion. Models were built using four ML methodologies in a cross-validation (CV) setting. All the ML methods returned successful Cox models with different CV performances. The best model exhibited a concordance index of 0.84 (95% CI: 0.82-0.86). The final analysis described the hazard ratios (HR) of the top ten MRI features associated with MCI to AD conversion. Our results suggest ML exploration is a viable strategy for building and analyzing survival models that predict subjects at risk of AD conversion.

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Section: Introductionmentioning

confidence: 99%

Exploring Survival Models Associated with MCI to AD Conversion: A Machine Learning Approach

Orozco-Sanchez

Treviño

Martinez-Ledesma

et al. 2019

Preprint

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Iterative Decorrelation Analysis, Unit of Measure Preserving Transformations and Latent Biomarker Discovery

Tamez-Peña

2023

Preprint

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Background Numerous biomarker discovery studies and exploratory clinical studies extract a large set of measurable variables, which often have varying degrees of correlation among them. This data collinearity can impact statistical model interpretation and hinder the discovery of potential associations between measured variables and the observed outcome. Exploratory Factor Analysis (EFA), Principal Component Analysis (PCA), and Machine-Learning (ML) can be used to discover latent variables associated with disease progression or outcome by computing transformation matrices, but the interpretation of unsupervised/supervised latent variables in high-dimensional datasets can be challenging. Results This study describe and reports the performance of the iterative decorrelation analysis algorithm (IDeA). The algorithm iteratively analyzes the correlation matrix of the data, updating the transformation coefficients until it reaches the desired correlation goal. The output of IDeA is a basis-transformation matrix that preserves the data dimensionality and unit of measure of the original observed variables. The main advantages of the IDeA basis transformations are sparsity and interpretability. The transformation does not alter uncorrelated features, thus statistical modeling and biomarker discovery in the new transformed basis can be a combination of novel latent variables and a sizable subset of unaltered variables. The algorithm was tested on five multidimensional/hyperdimensional and multimodal sets, demonstrating the effect of decorrelation parameters, interpretability, and latent biomarker discovery. Conclusions The iterative decorrelation approach provides a simple to use tool for researchers to explore the association between correlated features in hyperdimensional/multimodal settings and to decorrelate significant associations via latent variables that preserve the unit of measurement. An efficient computer implementation of IDeA is available in the FRESA.CAD R package (https://cran.r-project.org/web/packages/FRESA.CAD/index.html).

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Evaluating Common Strategies for the Efficiency of Feature Selection in the Context of Microarray Analysis

Pintilie¹,

Sykes²

2017

JDAIP

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The recent explosion of high-throughput technology has been accompanied by a corresponding rapid increase in the number of new statistical methods for developing prognostic and predictive signatures. Three commonly used feature selection techniques for time-to-event data: single gene testing (SGT), Elastic net and the Maximizing R Square Algorithm (MARSA) are evaluated on simulated datasets that vary in the sample size, the number of features and the correlation between features. The results of each method are summarized by reporting the sensitivity and the Area Under the Receiver Operating Characteristic Curve (AUC). The performance of each of these algorithms depends heavily on the sample size while the number of features entered in the analysis has a much more modest impact. The coefficients estimated utilizing SGT are biased towards the null when the genes are uncorrelated and away from the null when the genes are correlated. The Elastic Net algorithms perform better than MARSA and almost as well as the SGT when the features are correlated and about the same as MARSA when the features are uncorrelated.

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Efficient Gene Selection for Cancer Prognostic Biomarkers Using Swarm Optimization and Survival Analysis

Cited by 3 publications

References 0 publications

Exploring Survival Models Associated with MCI to AD Conversion: A Machine Learning Approach

Exploring Survival Models Associated with MCI to AD Conversion: A Machine Learning Approach

Iterative Decorrelation Analysis, Unit of Measure Preserving Transformations and Latent Biomarker Discovery

Evaluating Common Strategies for the Efficiency of Feature Selection in the Context of Microarray Analysis

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