The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection

Tang, Zaixiang; Shen, Yueping; Zhang, Xinyan; Yi, Nengjun

doi:10.1534/genetics.116.192195

Cited by 51 publications

(47 citation statements)

References 44 publications

(52 reference statements)

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“…In contrast to the minimal-optimal problem, the all-relevant problem is usually motivated by the need to identify features that are "significant" to the target variable [17], either in order to further investigate their dependencies, e.g. to find exploratory directions in gene micro-array research [23,24], or in order to enable a more interactive model design process, e.g. to design classifiers that take into account expert knowledge and the costs of acquiring each feature.…”

Section: All-relevantmentioning

confidence: 99%

Interpretation of linear classifiers by means of feature relevance bounds

Göpfert¹,

Pfannschmidt²,

Göpfert³

et al. 2018

Neurocomputing

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Research on feature relevance and feature selection problems goes back several decades, but the importance of these areas continues to grow as more and more data becomes available, and machine learning methods are used to gain insight and interpret, rather than solely to solve classification or regression problems. Despite the fact that feature relevance is often discussed, it is frequently poorly defined, and the feature selection problems studied are subtly different. Furthermore, the problem of finding all features relevant for a classification problem has only recently started to gain traction, despite its importance for interpretability and integrating expert knowledge. In this paper, we attempt to unify commonly used concepts and to give an overview of the main questions and results. We formalize two interpretations of the all-relevant problem and propose a polynomial method to approximate one of them for the important hypothesis class of linear classifiers, which also enables a distinction between strongly and weakly relevant features.

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Section: All-relevantmentioning

confidence: 99%

Interpretation of linear classifiers by means of feature relevance bounds

Göpfert¹,

Pfannschmidt²,

Göpfert³

et al. 2018

Neurocomputing

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show abstract

“…Besides, to shrink the coefficients of unimportant linear and nonlinear effects to zero exactly, we adopt the spike-and-slab priors in our model. The spike-and-slab priors have recently been shown as effective when being incorporated in Bayesian hierarchical framework for penalization methods, including the spike-and-slab LASSO, 20,21 Bayesian fused LASSO, 22 and Bayesian sparse group LASSO. 23 It leads to sparsity in the sense of exact 0 posterior estimates which are not available in Bayesian LASSO type of Bayesian shrinkage methods including that in the work of Li et al 18 Motivated by the pressing need to conduct efficient Bayesian G×E interaction studies accounting for the nonlinear interaction effects, the proposed semiparametric model significantly advances from existing Bayesian variable selection methods for G×E interactions in the following aspects.…”

Section: Introductionmentioning

confidence: 99%

Semiparametric Bayesian variable selection for gene‐environment interactions

Ren

Zhou

et al. 2019

Statistics in Medicine

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Many complex diseases are known to be affected by the interactions between genetic variants and environmental exposures beyond the main genetic and environmental effects. Study of gene-environment (G×E) interactions is important for elucidating the disease etiology. Existing Bayesian methods for G×E interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. Many studies have shown the advantages of penalization methods in detecting G×E interactions in "large p, small n" settings. However, Bayesian variable selection, which can provide fresh insight into G×E study, has not been widely examined. We propose a novel and powerful semiparametric Bayesian variable selection model that can investigate linear and nonlinear G×E interactions simultaneously. Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main-effects-only case within the Bayesian framework.Spike-and-slab priors are incorporated on both individual and group levels to identify the sparse main and interaction effects. The proposed method conducts Bayesian variable selection more efficiently than existing methods. Simulation shows that the proposed model outperforms competing alternatives in terms of both identification and prediction. The proposed Bayesian method leads to the identification of main and interaction effects with important implications in a high-throughput profiling study with high-dimensional SNP data.

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“…These methods include elastic nets [30] that add an additional ridge regression-like penalty to improve performance when the number of genetic variants is significantly larger than the sample size. Another approach is a spike-and-slab Lasso (ssLasso) method [23] that addresses the limitations of Lasso by imposing weak or no shrinkage on related features while imposing strong shrinkage on unrelated features. Bayesian-based methods are proposed for phenotype prediction as well, which includes a hybrid of LMMs and sparse regression models such as Bayesian Lasso [17] and Bayesian sparse linear mixed model (BSLMM) [29].…”

Section: Introductionmentioning

confidence: 99%

A Sparse Convolutional Predictor with Denoising Autoencoders for Phenotype Prediction

Chen

Shi

2019

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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Phenotype prediction has been widely conducted in many areas to help understand disease risks and susceptibility, and improve the breeding cycles of plants and animals. Most methods of phenotype prediction are based on regularized statistical approaches which only consider linear relationships among genetic features. Deep learning based methods have been recently reported to nicely address regression problems in high dimensional data in genomic studies. To explore deep learning for phenotype prediction, we propose a deep learning regression model, called Sparse Convolutional Predictor with Denoising Autoencoders (SCP_DAE), to predict quantitative traits. We constructed SCP_DAE by utilizing a convolutional layer that can extract correlation or linkage patterns in the genotype data and applying a sparse weight matrix resulted from the L 1 regularization to handle high dimensional genotype data. To learn efficient and compressed hidden representations of genotype data, we pre-trained the convolutional layer and the first fully connected layer in SCP_DAE using denoising autoencoders. These pre-trained layers were then fine-tuned to improve its performance of the SCP_DAE model for phenotype prediction. We comprehensively evaluated our proposed method on a yeast dataset which contains well assayed genotype profiles and quantitative traits. Our results showed that the proposed SCP_DAE method significantly outperforms regularized statistical approaches and similar deep learning models without pre-trained weights. CCS CONCEPTS • Applied computing → Computational genomics.

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The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection

Cited by 51 publications

References 44 publications

Interpretation of linear classifiers by means of feature relevance bounds

Interpretation of linear classifiers by means of feature relevance bounds

Semiparametric Bayesian variable selection for gene‐environment interactions

A Sparse Convolutional Predictor with Denoising Autoencoders for Phenotype Prediction

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