# A Multi-Domain Multi-Task Approach for Feature Selection from Bulk RNA Datasets

arXiv: https://arxiv.org/abs/2405.02534 · HTML: https://mlsanity.com/paper/2405.02534/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 4 May 2024.

## Builds on (most cited first, 9 of 9)

- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://mlsanity.com/paper/1711.02257/) (2017)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Concrete Autoencoders for Differentiable Feature Selection and Reconstruction](https://mlsanity.com/paper/1901.09346/) (2019)
- [LassoNet: A Neural Network with Feature Sparsity](https://mlsanity.com/paper/1907.12207/) (2019)
- [Sparse-Input Neural Networks for High-dimensional Nonparametric Regression and Classification](https://mlsanity.com/paper/1711.07592/) (2017)
- [A Unified Perspective on Multi-Domain and Multi-Task Learning](https://mlsanity.com/paper/1412.7489/) (2014)
- [FsNet: Feature Selection Network on High-dimensional Biological Data](https://mlsanity.com/paper/2001.08322/) (2020)

## 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.