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摘要翻译：
己糖是一种单糖，在许多细胞途径中起着关键作用，并在发育和疾病机制的调节中发挥着重要作用。目前的蛋白质-糖计算模型至少部分地基于先前的生化发现和知识。他们将这些发现的不同部分纳入了预测性黑箱模型。我们通过比较归纳逻辑程序设计(ILP)诱导规则与实际生化结果来研究生化发现的经验支持。我们从蛋白质数据库中挖掘出具有代表性的六糖结合位点、非六糖结合位点和表面凹槽的数据集。我们建立了一个六糖结合位点的ILP模型，并用几个基线机器学习分类器对我们的结果进行了评估。我们的方法获得了与其他黑箱分类器相似的精度，同时提供了对判别过程的洞察力。此外，它证实了湿实验室的发现，并揭示了以前未报道的Trp-Glu氨基酸依赖性。
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英文标题：
《An Inductive Logic Programming Approach to Validate Hexose Binding
Biochemical Knowledge》
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作者：
Houssam Nassif, Hassan Al-Ali, Sawsan Khuri, Walid Keirouz, and David
Page
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最新提交年份：
2018
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分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Computer Science 计算机科学
二级分类：Logic in Computer Science 计算机科学中的逻辑
分类描述：Covers all aspects of logic in computer science, including finite model theory, logics of programs, modal logic, and program verification. Programming language semantics should have Programming Languages as the primary subject area. Roughly includes material in ACM Subject Classes D.2.4, F.3.1, F.4.0, F.4.1, and F.4.2; some material in F.4.3 (formal languages) may also be appropriate here, although Computational Complexity is typically the more appropriate subject area.
涵盖计算机科学中逻辑的所有方面，包括有限模型理论，程序逻辑，模态逻辑和程序验证。程序设计语言语义学应该把程序设计语言作为主要的学科领域。大致包括ACM学科类D.2.4、F.3.1、F.4.0、F.4.1和F.4.2中的材料；F.4.3（形式语言）中的一些材料在这里也可能是合适的，尽管计算复杂性通常是更合适的主题领域。
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英文摘要：
Hexoses are simple sugars that play a key role in many cellular pathways, and in the regulation of development and disease mechanisms. Current protein-sugar computational models are based, at least partially, on prior biochemical findings and knowledge. They incorporate different parts of these findings in predictive black-box models. We investigate the empirical support for biochemical findings by comparing Inductive Logic Programming (ILP) induced rules to actual biochemical results. We mine the Protein Data Bank for a representative data set of hexose binding sites, non-hexose binding sites and surface grooves. We build an ILP model of hexose-binding sites and evaluate our results against several baseline machine learning classifiers. Our method achieves an accuracy similar to that of other black-box classifiers while providing insight into the discriminating process. In addition, it confirms wet-lab findings and reveals a previously unreported Trp-Glu amino acids dependency.
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PDF链接：
https://arxiv.org/pdf/1810.04707
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