Gemini: memory-efficient integration of hundreds of gene networks with high-order pooling

Abstract

Motivation The exponential growth of genomic sequencing data has created ever-expanding repositories of gene networks. Unsupervised network integration methods are critical to learn informative representations for each gene, which are later used as features for downstream applications. However, these network integration methods must be scalable to account for the increasing number of networks and robust to an uneven distribution of network types within hundreds of gene networks. Results To address these needs, we present Gemini, a novel network integration method that uses memory-efficient high-order pooling to represent and weight each network according to its uniqueness. Gemini then mitigates the uneven network distribution through mixing up existing networks to create many new networks. We find that Gemini leads to more than a 10% improvement in F1 score, 15% improvement in micro-AUPRC, and 63% improvement in macro-AUPRC for human protein function prediction by integrating hundreds of networks from BioGRID, and that Gemini’s performance significantly improves when more networks are added to the input network collection, while Mashup and BIONIC embeddings’ performance deteriorates. Gemini thereby enables memory-efficient and informative network integration for large gene networks and can be used to massively integrate and analyze networks in other domains. Availability and implementation Gemini can be accessed at: https://github.com/MinxZ/Gemini.

CONE: COntext-specific Network Embedding via Contextualized Graph Attention

“…We also include embeddings generated by a two-layer GAT (v2) network [28, 29] trained in a standard graph autoencoder style [38] as a more direct baseline against CONE. Moreover, BIONIC [16] and Gemini [19] are two recent approaches that learn an integrated embedding across a collection of networks. We use them to test if embedding multiple context-specific subgraphs together gives an advantage over embedding a single context-naive network.…”

“…Numerous functional genomics projects generate data comprising diverse types, qualities, and scopes of genes or molecules [12–14]. To obtain a high-quality and comprehensive network embedding, several methods are developed to infer a joint network representation by integrating multiple networks [15–19]. However, a drawback of network integration methods is that the integration process can eliminate context-specific information in each input network, resulting in a context-naive network.…”