A Multi-Domain Multi-Task Approach for Feature Selection from Bulk RNA Datasets
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Abstract (arXiv)
In this paper a multi-domain multi-task algorithm for feature selection in bulk RNAseq data is proposed. Two datasets are investigated arising from mouse host immune response to Salmonella infection. Data is collected from several strains of collaborative cross mice. Samples from the spleen and liver serve as the two domains. Several machine learning experiments are conducted and the small subset of discriminative across domains features have been extracted in each case. The algorithm proves viable and underlines the benefits of across domain feature selection by extracting new subset of discriminative features which couldn't be extracted only by one-domain approach.
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References · 9
- 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,135
- 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 816
- 2018Multi-Task Learning as Multi-Objective Optimizationcited by 809
- 2017An Overview of Multi-Task Learning in Deep Neural Networkscited by 791
- 2019Concrete Autoencoders for Differentiable Feature Selection and Reconstructioncited by 90
- 2019LassoNet: A Neural Network with Feature Sparsitycited by 81
- 2017Sparse-Input Neural Networks for High-dimensional Nonparametric Regression and Classificationcited by 39
- 2014A Unified Perspective on Multi-Domain and Multi-Task Learningcited by 33
- 2020FsNet: Feature Selection Network on High-dimensional Biological Datacited by 23
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