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A learning based framework for diverse biomolecule relationship prediction in molecular association network
Communications Biology ( IF 5.2 ) Pub Date : 2020-03-13 , DOI: 10.1038/s42003-020-0858-8
Zhen-Hao Guo , Zhu-Hong You , De-Shuang Huang , Hai-Cheng Yi , Zhan-Heng Chen , Yan-Bin Wang

Abundant life activities are maintained by various biomolecule relationships in human cells. However, many previous computational models only focus on isolated objects, without considering that cell is a complete entity with ample functions. Inspired by holism, we constructed a Molecular Associations Network (MAN) including 9 kinds of relationships among 5 types of biomolecules, and a prediction model called MAN-GF. More specifically, biomolecules can be represented as vectors by the algorithm called biomarker2vec which combines 2 kinds of information involved the attribute learned by k-mer, etc and the behavior learned by Graph Factorization (GF). Then, Random Forest classifier is applied for training, validation and test. MAN-GF obtained a substantial performance with AUC of 0.9647 and AUPR of 0.9521 under 5-fold Cross-validation. The results imply that MAN-GF with an overall perspective can act as ancillary for practice. Besides, it holds great hope to provide a new insight to elucidate the regulatory mechanisms.



中文翻译:

基于学习的分子缔合网络中多种生物分子关系预测框架

人类细胞中各种生物分子的关系维持着丰富的生命活动。但是,许多以前的计算模型只关注隔离的对象,而没有考虑单元是具有足够功能的完整实体。受整体主义的启发,我们构建了一个分子协会网络(MAN),该网络包括5种类型的生物分子之间的9种关系,以及一个名为MAN-GF的预测模型。更具体地,可以通过称为biomarker2vec的算法将生物分子表示为向量,该算法结合了由k-mer等学习的属性和通过图因式分解(GF)学习的行为的两种信息。然后,将随机森林分类器应用于训练,验证和测试。在5倍交叉验证下,MAN-GF获得了显着的性能,AUC为0.9647,AUPR为0.9521。结果表明,MAN-GF具有全面的视角,可以作为实践的辅助。此外,它有很大的希望为阐明监管机制提供新的见解。

更新日期:2020-03-16
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