CancerSiamese: one-shot learning for primary and metastatic tumor classification

Mostavi, M.; Chiu, Y.-C.; Chen, Y.; Huang, Y.

doi:10.1101/2020.09.07.286583

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bioRxiv · 10.1101/2020.09.07.286583

CancerSiamese: one-shot learning for primary and metastatic tumor classification

Mostavi, M.·Chiu, Y.-C.·Chen, Y.·Huang, Y.

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Abstract

We consider cancer classification based on one single gene expression profile. We proposed CancerSiamese, a new one-shot learning model, to predict the cancer type of a query primary or metastatic tumor sample based on a support set that contains only one known sample for each cancer type. CancerSiamese receives pairs of gene expression profiles and learns a representation of similar or dissimilar cancer types through two parallel Convolutional Neural Networks joined by a similarity function. We trained CancerSiamese for both primary and metastatic cancer type predictions using samples from TCGA and MET500. Test results for different N-way predictions yielded an average accuracy improvement of 8% and 4% over the benchmark 1-Nearest Neighbor (1-NN) classifier for primary and metastatic tumors, respectively. Moreover, we applied the guided gradient saliency map and feature selection to CancerSiamese to identify and analyze the marker-gene candidates for primary and metastatic cancers. Our work demonstrated, for the first time, the feasibility of applying one-shot learning for expression-based cancer type prediction when gene expression data of cancer types are limited and could inspire new and ingenious applications of one-shot and few-shot learning solutions for improving cancer diagnosis, treatment planning, and our understanding of cancer.

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Mostavi, M., Chiu, Y.-C., Chen, Y., Huang, Y.. 2020-09-09. CancerSiamese: one-shot learning for primary and metastatic tumor classification. https://doi.org/10.1101/2020.09.07.286583

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