Open Access

ARTICLE

SW-Net: A novel few-shot learning approach for disease subtype prediction

YUHAN JI¹, YONG LIANG^1,*, ZIYI YANG², NING AI¹

1 Faculty of Innovation Engineering, School of Computer Science and Engineering, Macau University of Science and Technology, Macau, 999078, China
2 Tencent Quantum Lab, Shenzhen, 518000, China

* Corresponding Author: YONG LIANG. Email:

(This article belongs to the Special Issue: Application of Deep Learning in Cancer)

BIOCELL 2023, 47(3), 569-579. https://doi.org/10.32604/biocell.2023.025865

Received 06 August 2022; Accepted 24 October 2022; Issue published 03 January 2023

Abstract

Few-shot learning is becoming more and more popular in many fields, especially in the computer vision field. This inspires us to introduce few-shot learning to the genomic field, which faces a typical few-shot problem because some tasks only have a limited number of samples with high-dimensions. The goal of this study was to investigate the few-shot disease sub-type prediction problem and identify patient subgroups through training on small data. Accurate disease sub-type classification allows clinicians to efficiently deliver investigations and interventions in clinical practice. We propose the SW-Net, which simulates the clinical process of extracting the shared knowledge from a range of interrelated tasks and generalizes it to unseen data. Our model is built upon a simple baseline, and we modified it for genomic data. Support-based initialization for the classifier and transductive fine-tuning techniques were applied in our model to improve prediction accuracy, and an Entropy regularization term on the query set was appended to reduce over-fitting. Moreover, to address the high dimension and high noise issue, we future extended a feature selection module to adaptively select important features and a sample weighting module to prioritize high-confidence samples. Experiments on simulated data and The Cancer Genome Atlas meta-dataset show that our new baseline model gets higher prediction accuracy compared to other competing algorithms.

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Keywords

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