# Coupling Deep Imputation with Multitask Learning for Downstream Tasks on Genomics Data

arXiv: https://arxiv.org/abs/2204.13705 · HTML: https://mlsanity.com/paper/2204.13705/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [AffinityNet: semi-supervised few-shot learning for disease type prediction](https://mlsanity.com/paper/1805.08905/) (2018)
- [OmiEmbed: a unified multi-task deep learning framework for multi-omics data](https://mlsanity.com/paper/2102.02669/) (2021)

## Built on by (most cited first, 1 of 1)

- [Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques](https://mlsanity.com/paper/2210.08258/) (2022)

## Abstract (arXiv)

Genomics data such as RNA gene expression, methylation and micro RNA expression are valuable sources of information for various clinical predictive tasks. For example, predicting survival outcomes, cancer histology type and other patients' related information is possible using not only clinical data but molecular data as well. Moreover, using these data sources together, for example in multitask learning, can boost the performance. However, in practice, there are many missing data points which leads to significantly lower patient numbers when analysing full cases, which in our setting refers to all modalities being present. In this paper we investigate how imputing data with missing values using deep learning coupled with multitask learning can help to reach state-of-the-art performance results using combined genomics modalities, RNA, micro RNA and methylation. We propose a generalised deep imputation method to impute values where a patient has all modalities present except one. Interestingly enough, deep imputation alone outperforms multitask learning alone for the classification and regression tasks across most combinations of modalities. In contrast, when using all modalities for survival prediction we observe that multitask learning alone outperforms deep imputation alone with statistical significance (adjusted p-value 0.03). Thus, both approaches are complementary when optimising performance for downstream predictive tasks.