Abstract
Molecular networks embraced diverse biological and functional associations between genes and gene products, which are conducive for identifying novel genes and pathways of a specific disease phenotype. Although great progress has been achieved in high-throughput interactome mapping, the associations among genes still incomplete which caused the sparsity of Gene Networks (GNs). Here, we proposed a network-based framework, termed NIHO, for optimizing and completing GNs by integrating six genome networks: STRING, ConsenusPathDB, HumanNet, GeneMANIA, GIANT and BioGRID. NIHO learns high-level features of genes from the heterogeneous networks by an end-to-end way that consisted of neural network and matrix completion. Then, the learned low-dimensional representations are used to calculate the geometric proximity of genes in the projected space. Finally, NIHO infers the interactions among genes by analyzing the proximity scores and adds those interactions that originally not existed into GNs. The experimental results showed that the capability of GNs to recover disease gene sets get significantly improved after processed by NIHO. In addition, we not only examined the proportion of the added interactions but also observed the performance of NIHO can be promoted when the number of heterogeneous networks is increased.
Highlights
Gene Networks (GNs) have become increasingly prominent in biology, which captures the knowledge of diverse interactions, including co-expression, co-citation, co-complex, pathway, physical, signaling, and those occurring between genes and gene products [1]–[6]
We proposed a framework, Neural Integration of Heterogeneous databases for the completion of bio-Molecular Network (NIHO), combining neural network with matrix completion to fuse the diverse information from six GNs: STRING, ConsenusPathDB, HumanNet, GeneMANIA, GIANT and BioGRID
DATASETS We adopted six GNs that were widely used by the public: STRING, ConsensusPathDB, GIANT, HumanNet, GeneMANIA, and BioGRID
Summary
Gene Networks (GNs) have become increasingly prominent in biology, which captures the knowledge of diverse interactions, including co-expression, co-citation, co-complex, pathway, physical, signaling, and those occurring between genes and gene products [1]–[6]. INDEX TERMS Neural integration, matrix completion, heterogeneous networks, gene interaction prediction, optimization, reconstruction. We proposed a framework, Neural Integration of Heterogeneous databases for the completion of bio-Molecular Network (NIHO), combining neural network with matrix completion to fuse the diverse information from six GNs: STRING, ConsenusPathDB, HumanNet, GeneMANIA, GIANT and BioGRID.
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