Predicting Single-cell Drug Sensitivity by Adaptive Weighted Feature for
Adversarial Multi-source Domain Adaptation
- URL: http://arxiv.org/abs/2403.05260v1
- Date: Fri, 8 Mar 2024 12:31:03 GMT
- Title: Predicting Single-cell Drug Sensitivity by Adaptive Weighted Feature for
Adversarial Multi-source Domain Adaptation
- Authors: Wei Duan, Hui Liu
- Abstract summary: scAdaDrug is a multi-source adaptive weighting model to predict single-cell drug sensitivity.
Our model achieved state-of-the-art performance in predicting drug sensitivity on sinle-cell datasets, as well as on cell line and patient datasets.
- Score: 9.043161950476055
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The development of single-cell sequencing technology had promoted the
generation of a large amount of single-cell transcriptional profiles, providing
valuable opportunities to explore drug-resistant cell subpopulations in a
tumor. However, the drug sensitivity data in single-cell level is still scarce
to date, pressing an urgent and highly challenging task for computational
prediction of the drug sensitivity to individual cells. This paper proposed
scAdaDrug, a multi-source adaptive weighting model to predict single-cell drug
sensitivity. We used an autoencoder to extract domain-invariant features
related to drug sensitivity from multiple source domains by exploiting
adversarial domain adaptation. Especially, we introduced an adaptive weight
generator to produce importance-aware and mutual independent weights, which
could adaptively modulate the embedding of each sample in dimension-level for
both source and target domains. Extensive experimental results showed that our
model achieved state-of-the-art performance in predicting drug sensitivity on
sinle-cell datasets, as well as on cell line and patient datasets.
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