ArticleJournal of the American Statistical Association2018
Bayesian Neural Networks for Selection of Drug Sensitive Genes.
Article in Journal of the American Statistical Association, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed.
- A weight-sharing Bayesian neural network for consistent feature selection with applications in cancer gene expression data.BMC bioinformatics · 2026Article
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- Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior.Journal of data science : JDS · 2024Article
- Deep network embedding with dimension selection.Neural networks : the official journal of the International Neural Network Society · 2024Article
- Scaling Up Bayesian Neural Networks with Neural Networks.Transactions on machine learning research · 2024Article
- Causal-StoNet: Causal Inference for High-Dimensional Complex Data.... International Conference on Learning Representations · 2024Article
- A Double Regression Method for Graphical Modeling of High-dimensional Nonlinear and Non-Gaussian Data.Statistics and its interface · 2024Article
- Debiased inference for heterogeneous subpopulations in a high-dimensional logistic regression model.Scientific reports · 2023Article
- DIET: Conditional independence testing with marginal dependence measures of residual information.Proceedings of machine learning research · 2023Article
- Screening and identification of potential key biomarkers for glucocorticoid-induced osteonecrosis of the femoral head.Journal of orthopaedic surgery and research · 2023Article
- A nonlinear sparse neural ordinary differential equation model for multiple functional processes.The Canadian journal of statistics = Revue canadienne de statistique · 2022Article
- Consistent Sparse Deep Learning: Theory and Computation.Journal of the American Statistical Association · 2022Article
- Learning Sparse Deep Neural Networks with a Spike-and-Slab Prior.Statistics & probability letters · 2022Article
- Multi-scale inference of genetic trait architecture using biologically annotated neural networks.PLoS genetics · 2021Article
- Genome-wide association study-based deep learning for survival prediction.Statistics in medicine · 2020Article
- Deep direct likelihood knockoffs.Advances in neural information processing systems · 2020Article
- Article
- An Adaptive Empirical Bayesian Method for Sparse Deep Learning.Advances in neural information processing systems · 2019Article
- Bayesian additive regression trees and the General BART model.Statistics in medicine · 2019Article
- A simple convolutional neural network for prediction of enhancer-promoter interactions with DNA sequence data.Bioinformatics (Oxford, England) · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Recent advances in high-throughput biotechnologies have provided an unprecedented opportunity for biomarker discovery, which, from a statistical point of view, can be cast as a variable selection problem. This problem is challenging due to the high-dimensional and non-linear nature of omics data and, in general, it suffers three difficulties: (i) an unknown functional form of the nonlinear system, (ii) variable selection consistency, and (iii) high-demanding computation. To circumvent the first difficulty, we employ a feed-forward neural network to approximate the unknown nonlinear function motivated by its universal approximation ability. To circumvent the second difficulty, we conduct structure selection for the neural network, which induces variable selection, by choosing appropriate prior distributions that lead to the consistency of variable selection. To circumvent the third difficulty, we implement the population stochastic approximation Monte Carlo algorithm, a parallel adaptive Markov Chain Monte Carlo (MCMC) algorithm, on the OpenMP platform which provides a linear speedup for the simulation with the number of cores of the computer. The numerical results indicate that the proposed method can work very well for identification of relevant variables for high-dimensional nonlinear systems. The proposed method is successfully applied to identification of the genes that are associated with anticancerdrug sensitivities based on the data collected in the cancer cell line encyclopedia (CCLE) study.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.