KR4SL:用于解释性预测合成致命性的知识图推理
Ke Zhang1,2, Min Wu3, Yong Liu4
1School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China.
Bioinformatics (Oxford, England)
|June 30, 2023
概括
KR4SL使用知识图表和深度学习预测合成死亡率的合作伙伴,改善癌症药物发现. 这种可解释的模型增强了对合成致命性机制的理解.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 合成致死性 (SL) 是一种关键的抗癌策略,但湿实验室查是昂贵的,容易产生非目标效应.
- 知识图 (KGs) 可以改善基于机器学习的SL预测,但子图结构仍然未得到充分探索.
- 当前机器学习方法缺乏可解释性,这阻碍了它们在SL识别中的应用.
研究的目的:
- 开发一种新的计算模型,KR4SL,用于预测合成致命性 (SL) 合作伙伴.
- 利用知识图 (KG) 的结构和文本语义来增强SL预测.
- 通过识别关键子图结构来提高SL预测模型的解释性.
主要方法:
- KR4SL在KG中构建和学习关系二图,融合实体文本语义.
- 一个反复的神经网络增强了顺序路径语义,而一个细心的聚合器识别了解释性的子图.
- 该模型通过捕获KG的结构和语义信息来预测SL合作伙伴.
主要成果:
- 在预测合成致命性合作伙伴方面,KR4SL显著优于现有的基线方法.
- 该模型通过识别的子图结构提供可解释的解释,揭示了潜在的机制.
- 实验结果表明KR4SL的优越预测能力和可解释性.
结论:
- KR4SL提供了一种强大且可解释的深度学习方法,用于识别合成杀伤性目标.
- 该模型发现机制的能力提高了其在癌症药物标发现中的实用性.
- 这项工作强调了深度学习和KG探索在推进基于SL的癌症治疗中的实际价值.
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