# Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer Detection

arXiv: https://arxiv.org/abs/2408.09635 · HTML: https://mlsanity.com/paper/2408.09635/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records](https://mlsanity.com/paper/1905.03218/) (2019)

## Built on by (most cited first, 2 of 2)

- [User-Adaptive Meta-Learning for Cold-Start Medication Recommendation with Uncertainty Filtering](https://mlsanity.com/paper/2601.22820/) (2026)
- [SurvAttack: Black-Box Attack On Survival Models through Ontology-Informed EHR Perturbation](https://mlsanity.com/paper/2412.18706/) (2024)

## Abstract (arXiv)

Gene expression profiles obtained through DNA microarray have proven successful in providing critical information for cancer detection classifiers. However, the limited number of samples in these datasets poses a challenge to employ complex methodologies such as deep neural networks for sophisticated analysis. To address this "small data" dilemma, Meta-Learning has been introduced as a solution to enhance the optimization of machine learning models by utilizing similar datasets, thereby facilitating a quicker adaptation to target datasets without the requirement of sufficient samples. In this study, we present a meta-learning-based approach for predicting lung cancer from gene expression profiles. We apply this framework to well-established deep learning methodologies and employ four distinct datasets for the meta-learning tasks, where one as the target dataset and the rest as source datasets. Our approach is evaluated against both traditional and deep learning methodologies, and the results show the superior performance of meta-learning on augmented source data compared to the baselines trained on single datasets. Moreover, we conduct the comparative analysis between meta-learning and transfer learning methodologies to highlight the efficiency of the proposed approach in addressing the challenges associated with limited sample sizes. Finally, we incorporate the explainability study to illustrate the distinctiveness of decisions made by meta-learning.