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.

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。