Biological network link prediction method based on meta-path and bidirectional encoder

Abstract

The invention belongs to the field of computer science, and discloses a biological network link prediction method based on a meta-path and a bidirectional encoder. The method comprises the following steps: constructing a multi-source heterogeneous drug information network, and simultaneously designing a plurality of semantic paths for sequence sampling to form a large-scale semantic information base; organically fusing a depth Transformer encoder and a mask language model (mask language model) to design a depth two-way encoding representation model, and effectively extracting a low-latitude representation vector of each node; and finally, carrying out biological link prediction on a disease protein association relationship, a protein-drug interaction relationship, a drug side effect association relationship and the like by utilizing an induction matrix complementation technology, so as to complete a drug research and development technical system from disease to target to drug to side effect.

Description

technical field [0001] The invention belongs to the field of computer science and relates to the application of artificial intelligence technology, in particular to a biological network link prediction method based on meta-paths and bidirectional encoders. Background technique [0002] For a set of biomedical entities and their known interactions, aiming to predict other potential interactions (links) between entities is one of the most important tasks in the field of biomedicine. Therefore, more and more researchers use computer techniques to predict potential interactions in various biomedical networks. [0003] Traditional approaches in the biomedical field have devoted considerable effort to exploiting biologically relevant features, such as chemical substructure, gene ontology, and topological similarity. Meanwhile, supervised learning methods and semi-supervised graph inference models are used to predict potential interactions. These methods are mainly based on the s...

AI Technical Summary

Problems solved by technology

However, prediction methods based on biological features usually face two problems: (1) The cost of biological feature extraction is very high, and even some biological features are difficult to obtain, although those biological entities without features can be deleted through preprocessing, but This usually results in small data sets, missing important information, and thus not practical in practical applications; (2) biological features may not be precise enough to represent biomedical entities, and may not be able to build stable and accurate models

However, existing methods only focus on the structural features between network nodes, while ignoring the semantic information between network entities; or they can only capture shorter structures and meta-paths, and cannot deeply mine the structural and semantic relationships between network nodes.

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