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R-GAT:癌症文档分类利用基于图形的剩余网络,用于数据有限的场景

Elias Hossain¹, Tasfia Nuzhat², Shamsul Masum³

¹Department of Computer Science and Engineering, Mississippi State University, Starkville, MS, 39762, USA. mh3511@msstate.edu.

Scientific reports

|February 17, 2026

概括

一个新的残留图注意网络 (R-GAT) 有效地对癌症摘要进行分类. 这种轻量级模型的性能与使用较少计算资源的复杂变压器相当,有助于癌症信息学研究.

科学领域:

生物医学自然语言处理 (NLP)
癌症信息学癌症信息学
机器学习用于医疗保健

背景情况:

对与癌症相关的生物医学摘要进行准确的分类对于癌症信息学和医疗保健研究至关重要.
变压器模型的有限的标记数据和高的计算成本阻碍了该领域的进展.

研究的目的:

开发一个计算高效和有效的模型来分类与癌症相关的生物医学摘要.
在数据要求和计算资源方面解决现有方法的局限性.

主要方法:

提出了一个剩余图注意力网络 (R-GAT),集成多头注意力和剩余连接.
捕获生物医学文本中的语义和关系依赖关系.
在1875个PubMed摘要的精选数据集上评估模型,涵盖甲状腺,结肠,肺部和通用癌症主题.

主要成果:

R-GAT获得了0.96±0.01的宏F1得分,表现出稳定且具有竞争力的表现.
性能与以变压器为基础的模型 (BioBERT,BioClinicalBERT) 和经典基线 (逻辑回归) 相当.
与变压器模型相比,R-GAT需要的计算资源要少得多.

结论:

像R-GAT这样的轻量级基于图形的架构是生物医学NLP中计算密集型变压器的可靠和资源高效的替代方案.
注意力机制和剩余连接对于模型的稳定性至关重要,尤其是在有限的数据的情况下.
策划数据集的发布旨在促进可复制性和进一步的癌症信息学研究.

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