@inproceedings{cao-etal-2023-gaussian,
title = "{G}aussian Distributed Prototypical Network for Few-shot Genomic Variant Detection",
author = "Cao, Jiarun and
Peek, Niels and
Renehan, Andrew and
Ananiadou, Sophia",
editor = "Demner-fushman, Dina and
Ananiadou, Sophia and
Cohen, Kevin",
booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bionlp-1.2",
doi = "10.18653/v1/2023.bionlp-1.2",
pages = "26--36",
abstract = "Automatically identifying genetic mutations in the cancer literature using text mining technology has been an important way to study the vast amount of cancer medical literature. However, novel knowledge regarding the genetic variants proliferates rapidly, though current supervised learning models struggle with discovering these unknown entity types. Few-shot learning allows a model to perform effectively with great generalization on new entity types, which has not been explored in recognizing cancer mutation detection. This paper addresses cancer mutation detection tasks with few-shot learning paradigms. We propose GDPN framework, which models the label dependency from the training examples in the support set and approximates the transition scores via Gaussian distribution. The experiments on three benchmark cancer mutation datasets show the effectiveness of our proposed model.",
}
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<abstract>Automatically identifying genetic mutations in the cancer literature using text mining technology has been an important way to study the vast amount of cancer medical literature. However, novel knowledge regarding the genetic variants proliferates rapidly, though current supervised learning models struggle with discovering these unknown entity types. Few-shot learning allows a model to perform effectively with great generalization on new entity types, which has not been explored in recognizing cancer mutation detection. This paper addresses cancer mutation detection tasks with few-shot learning paradigms. We propose GDPN framework, which models the label dependency from the training examples in the support set and approximates the transition scores via Gaussian distribution. The experiments on three benchmark cancer mutation datasets show the effectiveness of our proposed model.</abstract>
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%0 Conference Proceedings
%T Gaussian Distributed Prototypical Network for Few-shot Genomic Variant Detection
%A Cao, Jiarun
%A Peek, Niels
%A Renehan, Andrew
%A Ananiadou, Sophia
%Y Demner-fushman, Dina
%Y Ananiadou, Sophia
%Y Cohen, Kevin
%S The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F cao-etal-2023-gaussian
%X Automatically identifying genetic mutations in the cancer literature using text mining technology has been an important way to study the vast amount of cancer medical literature. However, novel knowledge regarding the genetic variants proliferates rapidly, though current supervised learning models struggle with discovering these unknown entity types. Few-shot learning allows a model to perform effectively with great generalization on new entity types, which has not been explored in recognizing cancer mutation detection. This paper addresses cancer mutation detection tasks with few-shot learning paradigms. We propose GDPN framework, which models the label dependency from the training examples in the support set and approximates the transition scores via Gaussian distribution. The experiments on three benchmark cancer mutation datasets show the effectiveness of our proposed model.
%R 10.18653/v1/2023.bionlp-1.2
%U https://aclanthology.org/2023.bionlp-1.2
%U https://doi.org/10.18653/v1/2023.bionlp-1.2
%P 26-36
Markdown (Informal)
[Gaussian Distributed Prototypical Network for Few-shot Genomic Variant Detection](https://aclanthology.org/2023.bionlp-1.2) (Cao et al., BioNLP 2023)
ACL