Moghaddam Arya Hadizadeh, Kerdabadi Mohsen Nayebi, Zhong Cuncong, Yao Zijun
University of Kansas, Lawrence, KS, USA.
AMIA Annu Symp Proc. 2025 May 22;2024:828-837. eCollection 2024.
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.
通过DNA微阵列获得的基因表达谱已被证明能成功为癌症检测分类器提供关键信息。然而，这些数据集中样本数量有限，这对采用深度神经网络等复杂方法进行精细分析构成了挑战。为了解决这一“小数据”困境，元学习被引入作为一种解决方案，通过利用相似数据集来增强机器学习模型的优化，从而在无需足够样本的情况下更快地适应目标数据集。在本研究中，我们提出了一种基于元学习从基因表达谱预测肺癌的方法。我们将此框架应用于成熟的深度学习方法，并使用四个不同的数据集进行元学习任务，其中一个作为目标数据集，其余作为源数据集。我们的方法与传统方法和深度学习方法进行了评估比较，结果表明，与在单个数据集上训练的基线相比，元学习在增强源数据上具有卓越性能。此外,我们对元学习和迁移学习方法进行了对比分析，以突出所提方法在应对与有限样本量相关挑战方面的效率性。最后，我们纳入了可解释性研究，以说明元学习所做决策的独特性。
