Abstract
The identification of profiled cancer-related genes plays an essential role in cancer diagnosis and treatment. Based on literature research, the classification of genetic mutations continues to be done manually nowadays. Manual classification of genetic mutations is pathologist-dependent, subjective, and time-consuming. To improve the accuracy of clinical interpretation, scientists have proposed computational-based approaches for automatic analysis of mutations with the advent of next-generation sequencing technologies. Nevertheless, some challenges, such as multiple classifications, the complexity of texts, redundant descriptions, and inconsistent interpretation, have limited the development of algorithms. To overcome these difficulties, we have adapted a deep learning method named Bidirectional Encoder Representations from Transformers (BERT) to classify genetic mutations based on text evidence from an annotated database. During the training, three challenging features such as the extreme length of texts, biased data presentation, and high repeatability were addressed. Finally, the BERT+abstract demonstrates satisfactory results with 0.80 logarithmic loss, 0.6837 recall, and 0.705 F-measure. It is feasible for BERT to classify the genomic mutation text within literature-based datasets. Consequently, BERT is a practical tool for facilitating and significantly speeding up cancer research towards tumor progression, diagnosis, and the design of more precise and effective treatments.
Highlights
Nowadays, genomic, transcriptomic, and epigenomic studies have been benefited from the development of inexpensive next-generation sequencing technologies, which play essential roles in exploring tumor biology [1,2,3]
Parameters of Bidirectional Encoder Representations from Transformers (BERT)-base methods are loaded into the downstream BERT classification model so that our model parameters can be fine-tuned based on these pretrained models, which significantly reduces the convergence time of the model and increases the accuracy of the model
This paper evaluates the performances of the model using several evaluation indicators: Logloss, recall (REC), precision (PRE), F1 score, receiver operating characteristic (ROC) curve, and confusion matrix
Summary
Genomic, transcriptomic, and epigenomic studies have been benefited from the development of inexpensive next-generation sequencing technologies, which play essential roles in exploring tumor biology [1,2,3]. The advanced ML methods, such as Light Gradient Boosting Machine (LightGBM), has been proposed to enable gene multiclassification based on complex literature [25]. These methods are limited by complex calculations when applied to large-scale datasets, for genomic-related literature datasets that contain millions, or billions, of annotated training examples [26, 27]. The performances of ML are dependent on feature extraction that requires professional knowledge and longterm processing [28,29,30,31] To overcome these difficulties, deep learning (DL) has emerged to handle large-scale and complex datasets since its performance increases with the enlargement of datasets [32,33,34]. We improve the BERT method to classify complex clinical texts, and obtain 0.8074 logarithmic loss, 0.6837 recall, and 0.705 F-measure scores
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