TY  - EJOU
AU  - JI, YUHAN 
AU  - LIANG, YONG 
AU  - YANG, ZIYI 
AU  - AI, NING 

TI  - SW-Net: A novel few-shot learning approach for disease subtype prediction
T2  - BIOCELL

PY  - 2023
VL  - 47
IS  - 3
SN  - 1667-5746

AB  - 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.
KW  - Few-shot learning; Disease sub-type classification; Feature selection; Deep learning; Meta-learning

DO  - 10.32604/biocell.2023.025865