Integrating data and knowledge to identify functional modules of genes: a multilayer approach

Molecular complex detection in protein interaction networks through reinforcement learning

BACKGROUND

Proteins often assemble into higher-order complexes to perform their biological functions. Such protein-protein interactions (PPI) are often experimentally measured for pairs of proteins and summarized in a weighted PPI network, to which community detection algorithms can be applied to define the various higher-order protein complexes. Current methods include unsupervised and supervised approaches, often assuming that protein complexes manifest only as dense subgraphs. Utilizing supervised approaches, the focus is not on how to find them in a network, but only on learning which subgraphs correspond to complexes, currently solved using heuristics. However, learning to walk trajectories on a network to identify protein complexes leads naturally to a reinforcement learning (RL) approach, a strategy not extensively explored for community detection. Here, we develop and evaluate a reinforcement learning pipeline for community detection on weighted protein-protein interaction networks to detect new protein complexes. The algorithm is trained to calculate the value of different subgraphs encountered while walking on the network to reconstruct known complexes. A distributed prediction algorithm then scales the RL pipeline to search for novel protein complexes on large PPI networks.

RESULTS

The reinforcement learning pipeline is applied to a human PPI network consisting of 8k proteins and 60k PPI, which results in 1,157 protein complexes. The method demonstrated competitive accuracy with improved speed compared to previous algorithms. We highlight protein complexes such as C4orf19, C18orf21, and KIAA1522 which are currently minimally characterized. Additionally, the results suggest TMC04 be a putative additional subunit of the KICSTOR complex and confirm the involvement of C15orf41 in a higher-order complex with HIRA, CDAN1, ASF1A, and by 3D structural modeling.

CONCLUSIONS

Reinforcement learning offers several distinct advantages for community detection, including scalability and knowledge of the walk trajectories defining those communities. Applied to currently available human protein interaction networks, this method had comparable accuracy with other algorithms and notable savings in computational time, and in turn, led to clear predictions of protein function and interactions for several uncharacterized human proteins.

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

Over-expression of long non-coding RNA insulin-like growth factor 2-antisense suppressed hepatocellular carcinoma cell proliferation and metastasis by regulating the microRNA-520h/cyclin-dependent kinase inhibitor 1A signaling pathway

Primary liver cancer is the sixth most common cancer and the third leading cause of malignancy-related death worldwide in 2020, with 75–85% of hepatocellular carcinoma (HCC). Evidences have verified that long noncoding RNAs (lncRNAs) play key roles in HCC onset and development. However, the function and mechanism of lncRNA insulin-like growth factor 2-antisense (IGF2-AS) in HCC remain unclear. Herein, IGF2-AS expression profile in HCC patients was first investigated based on The Cancer Genome Atlas (TCGA) database and local HCC patients, followed by prognostic value evaluation using Kaplan–Meier method; then, the bioinformatics analysis, dual-luciferase reporter assay, Spearman correlation assay, function gain, and loss with rescue experiments were applied to investigate the biological function and the involved molecular mechanisms of IGF2-AS in HCC oncogenesis and development. Our results showed that IGF2-AS expression was significantly down-regulated in HCC cells and tissues; lower IGF2-AS expression was significantly associated with poor prognosis of HCC patients; IGF2-AS over-expression inhibited the viability, colony formation, invasion, and migration, while promoted apoptosis in vitro, and inhibited HCC xenograft growth in vivo; IGF2-AS sponged microRNA-520h (miR-520h) to up-regulate IGF2-AS expression, and miR-520h over-expression or cyclin-dependent kinase inhibitor 1A (CDKN1A) silencing reversed IGF2-AS reduced aggressive behaviors of HCC cells. In conclusion, IGF2-AS is a tumor-suppressor in HCC, and lower IGF2-AS expression is associated with poor prognosis of HCC patients; IGF2-AS inhibits HCC oncogenesis and development by IGF2-AS/miR-520h/CDKN1A pathway. Therefore, IGF2-AS may serve as a new biomarker for HCC management.

An adaptive node embedding framework for multiplex networks

Network Embedding (NE) has emerged as a powerful tool in many applications. Many real-world networks have multiple types of relations between the same entities, which are appropriate to be modeled as multiplex networks. However, at random walk-based embedding study for multiplex networks, very little attention has been paid to the problems of sampling bias and imbalanced relation types. In this paper, we propose an Adaptive Node Embedding Framework (ANEF) based on cross-layer sampling strategies of nodes for multiplex networks. ANEF is the first framework to focus on the bias issue of sampling strategies. Through metropolis hastings random walk (MHRW) and forest fire sampling (FFS), ANEF is less likely to be trapped in local structure with high degree nodes. We utilize a fixed-length queue to record previously visited layers, which can balance the edge distribution over different layers in sampled node sequence processes. In addition, to adaptively sample the cross-layer context of nodes, we also propose a node metric called Neighbors Partition Coefficient (NPC). Experiments on real-world networks in diverse fields show that our framework outperforms the state-of-the-art methods in application tasks such as cross-domain link prediction and mutual community detection.

NPF:network propagation for protein function prediction

Background

The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, treating disease and developing new medicines. Various methods have been developed to facilitate the prediction of these functions by combining protein interaction networks (PINs) with multi-omics data. However, it is still challenging to make full use of multiple biological to improve the performance of functions annotation.

Results

We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. According to the comprehensive evaluation of NPF, it delivered a better performance than other competing methods in terms of leave-one-out cross-validation and ten-fold cross validation.

Conclusions

We demonstrated that network propagation, together with multi-omics data, can both discover more partners with similar function, and is unconstricted by the “small-world” feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional information of similarity from protein correlations.