@Article{biocell.2023.025865,
AUTHOR = {YUHAN JI, YONG LIANG, ZIYI YANG, NING AI},
TITLE = {SW-Net: A novel few-shot learning approach for disease subtype prediction},
JOURNAL = {BIOCELL},
VOLUME = {47},
YEAR = {2023},
NUMBER = {3},
PAGES = {569--579},
URL = {http://www.techscience.com/biocell/v47n3/51089},
ISSN = {1667-5746},
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.},
DOI = {10.32604/biocell.2023.025865}
}