Integrative Construction and Analysis of Molecular Association Network in Human Cells by Fusing Node Attribute and Behavior Information

Detecting whether a pair of biomolecules associate is of great significance in the study of molecular biology. Hence, computational methods are urgently needed as guidance for practice. However, most of the previous prediction models influenced by reductionism focused on isolated research objects, which have their own inherent defects. Inspired by holism, a machine-learning-based framework called MAN-node2vec is proposed to predict multi-type relationships in the molecular associations network (MAN). Specifically, we constructed a large-scale MAN composed of 1,023 miRNAs, 1,649 proteins, 769 long non-coding RNAs (lncRNAs), 1,025 drugs, and 2,062 diseases. Then, each biomolecule in MAN can be represented as a vector by its attribute learned by k-mer, etc. and its behavior learned by node2vec. Finally, the random forest classifier is applied to carry out the relationship prediction task. The proposed model achieved a reliable performance with 0.9677 areas under the curve (AUCs) and 0.9562 areas under the precision curve (AUPRs) under 5-fold cross-validation. Also, additional experiments proved that the proposed global model shows more competitive performance than the traditional local method. All of these provided a systematic insight for understanding the synergistic interactions between various molecules and diseases. It is anticipated that this work can bring beneficial inspiration and advance to related systems biology and biomedical research.