Bioentity2vec: Attribute- and behavior-driven representation for predicting multi-type relationships between bioentities.

ABSTRACT:

Background

The explosive growth of genomic, chemical, and pathological data provides new opportunities and challenges for humans to thoroughly understand life activities in cells. However, there exist few computational models that aggregate various bioentities to comprehensively reveal the physical and functional landscape of biological systems.

Results

We constructed a molecular association network, which contains 18 edges (relationships) between 8 nodes (bioentities). Based on this, we propose Bioentity2vec, a new method for representing bioentities, which integrates information about the attributes and behaviors of a bioentity. Applying the random forest classifier, we achieved promising performance on 18 relationships, with an area under the curve of 0.9608 and an area u

SUBMITTER: Guo ZH 

PROVIDER: S-EPMC7293023 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature