深度基因突变:在瘤学的自动基因突变分类:一个深度学习的比较研究研究
Emad A Elsamahy1, Asmaa E Ahmed1, Tahseen Shoala2
1College of Computing and Information Technology, Arab Academy for Science, Technology, and Maritime Transport, Cairo, Egypt.
Heliyon
|June 24, 2024
概括
生物BERT是一种深度学习模型,可以准确地从生物医学文本中分类基因突变,改善早期癌症检测. 这种自动化方法超越了以前的方法,在临床解释中提供了更高的精度.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 病理学家对遗传突变进行手动分类是耗时的,可能会影响早期癌症检测.
- 下一代测序技术可实现自动化突变分析,提高临床解释精度.
研究的目的:
- 评估深度学习模型,以使用生物医学文本对基因突变进行自动分类.
- 为了完成这个任务,将BioBERT的性能与BERT,LSTM和BiLSTM等其他模型进行比较.
主要方法:
- 使用了四种深度学习分类模型:BioBERT,BERT,LSTM和BiLSTM.
- 在生物医学文本数据集上训练模型,其中包含遗传突变,来源于纪念斯隆·凯特林癌症中心.
- 解决了包括巨大的文本长度,数据偏差和数据重复在内的挑战.
主要成果:
- 生物BERT表现出卓越的性能,F1得分为0.87和马修斯相关系数 (MCC) 为0.850.
- 这对BERT模型来说是一个显著的改进,BERT模型获得了F1得分0.70.
- 这些模型在具有挑战性的Kaggle数据集上使用标准指标进行了评估.
结论:
- 生物BERT是一种高度有效的模型,用于自动化从生物医学文本中对遗传突变进行分类.
- 开发的计算方法提高了用于早期癌症检测的突变分析的准确性和效率.
- 进一步的研究可以在BioBERT的成功基础上改进自动化临床解释工具.
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