G aussian Distributed Prototypical Network for Few-shot Genomic Variant Detection (2023)

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.

## formulation our goal in this work is to formulate cancer genomic variant detection as a fsl problem, which has not been done in prior work. to achieve this, we first present the fsl framework and specify symbols and terminology in this section 1. then we illustrate the proposed method in the following section 2. ## results tmvar emu bronco corpus type title and abstract title and abstract full-text number of documents 500 109 108 cancer variants 871 172 275 mutation types sub, del, ins, dup, indel, snp, fs sub, del, ins, snp, fs sub, del, ins, indel, snp, fs ## discussion as shown in figure 4 , we utilize two cases to demonstrate the effectiveness of the proposed model. specifically, we compare the proposed model with the fsl and conventional ner models, respectively, to showcase our model's advance. the upper figure is a comparison between the proposed model and a conventional crf-based ner model. we can notice that our model correctly assigns the "e746-a750" a label "b-del", while bilstm-crf model wrongly predicts it as "o." as the label "b-del" rarely appears in the training set, bilstm-crf model struggles with capturing its relevant semantics and assigning "b-del" to the correct token spans. our model can predict the "b-del" for the token "e746-a750" credited to the prototype representation of the label "b-del" that has been learned in the support set. the following case compares the proposed model and a prototype-based fsl model. our model successfully predicts "c.370-371insa" as "b-del" while prototype network predicts its as "i-del". this is due to the fact that prototype network only learns the transition scores via prototype representations of specific categories. although the prototype representation provides the feature of label "del" to some extent, the model still miscognizes it as "i-del" because of the data randomness. our model overcomes this issue according to suppress the data fluctuation via gussinan distribution, therefore predicting this case correctly. ## conclusion in this paper, we address the problem of recognizing the unseen entity categories in the genomic cancer literature. we exploit the few shot learning paradigm in this task and propose a transited pro-totype ner framework to generate the transition scores for crf models. meanwhile, since the training samples are limited in the support set, which results in data fluctuation, we adopt gaussian distribution as our transition function to smoothen the randomness caused by a few samples. finally, experimental results on the three cancer genomic datasets prove the effectiveness of our proposed method. bold marks the highest figure, underline marks the second-highest figure.