GoVec: Gene Ontology Representation Learning Using Weighted Heterogeneous Graph and Meta-Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

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Megnn: Meta-path extracted graph neural network for heterogeneous graph representation learning

Knowledge-Based Systems ◽

10.1016/j.knosys.2021.107611 ◽

2021 ◽

pp. 107611

Author(s):

Yaomin Chang ◽

Chuan Chen ◽

Weibo Hu ◽

Zibin Zheng ◽

Xiaocong Zhou ◽

...

Keyword(s):

Neural Network ◽

Representation Learning ◽

Graph Representation ◽

Meta Path

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An Attention-Based Graph Neural Network for Heterogeneous Structural Learning

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.5833 ◽

2020 ◽

Vol 34 (04) ◽

pp. 4132-4139

Author(s):

Huiting Hong ◽

Hantao Guo ◽

Yucheng Lin ◽

Xiaoqing Yang ◽

Zang Li ◽

...

Keyword(s):

Neural Network ◽

Structural Information ◽

Representation Learning ◽

Graph Representation ◽

Heterogeneous Information ◽

Domain Experts ◽

Proposed Model ◽

Meta Path ◽

Low Dimensional ◽

Public Datasets

In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations. Most of the existing methods conducted on HIN revise homogeneous graph embedding models via meta-paths to learn low-dimensional vector space of HIN. In this paper, we propose a novel Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly encode structural information of HIN without meta-path and achieve more informative representations. With this method, domain experts will not be needed to design meta-path schemes and the heterogeneous information can be processed automatically by our proposed model. Specifically, we implicitly represent heterogeneous information using the following two methods: 1) we model the transformation between heterogeneous vertices through a projection in low-dimensional entity spaces; 2) afterwards, we apply the graph neural network to aggregate multi-relational information of projected neighborhood by means of attention mechanism. We also present three extensions of HetSANN, i.e., voices-sharing product attention for the pairwise relationships in HIN, cycle-consistency loss to retain the transformation between heterogeneous entity spaces, and multi-task learning with full use of information. The experiments conducted on three public datasets demonstrate that our proposed models achieve significant and consistent improvements compared to state-of-the-art solutions.

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Heterogeneous Network Representation Learning Method Based on Meta-path

2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) ◽

10.1109/icccbda.2019.8725667 ◽

2019 ◽

Author(s):

Ying Yin ◽

Lixin Ji ◽

Ruiyang Huang ◽

Xiaotao Cheng

Keyword(s):

Heterogeneous Network ◽

Representation Learning ◽

Learning Method ◽

Network Representation ◽

Meta Path

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Meta-Path Based Gene Ontology Profiles for Predicting Drug-Disease Associations

IEEE Access ◽

10.1109/access.2021.3065280 ◽

2021 ◽

pp. 1-1

Author(s):

Thitipong Kawichai ◽

Apichat Suratanee ◽

Kitiporn Plaimas

Keyword(s):

Gene Ontology ◽

Disease Associations ◽

Meta Path

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Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases

10.1101/2020.06.15.153692 ◽

2020 ◽

Author(s):

Junheng Hao ◽

Chelsea J.-T Ju ◽

Muhao Chen ◽

Yizhou Sun ◽

Carlo Zaniolo ◽

...

Keyword(s):

Gene Ontology ◽

Protein Interactions ◽

Representation Learning ◽

Knowledge Bases ◽

Biological Knowledge ◽

Transfer Model ◽

Enzymatic Function ◽

Ppi Networks ◽

Wide Range ◽

Embedding Spaces

AbstractThe widespread of Coronavirus has led to a worldwide pandemic with a high mortality rate. Currently, the knowledge accumulated from different studies about this virus is very limited. Leveraging a wide-range of biological knowledge, such as gene on-tology and protein-protein interaction (PPI) networks from other closely related species presents a vital approach to infer the molecular impact of a new species. In this paper, we propose the transferred multi-relational embedding model Bio-JOIE to capture the knowledge of gene ontology and PPI networks, which demonstrates superb capability in modeling the SARS-CoV-2-human protein interactions. Bio-JOIE jointly trains two model components. The knowledge model encodes the relational facts from the protein and GO domains into separated embedding spaces, using a hierarchy-aware encoding technique employed for the GO terms. On top of that, the transfer model learns a non-linear transformation to transfer the knowledge of PPIs and gene ontology annotations across their embedding spaces. By leveraging only structured knowledge, Bio-JOIE significantly outperforms existing state-of-the-art methods in PPI type prediction on multiple species. Furthermore, we also demonstrate the potential of leveraging the learned representations on clustering proteins with enzymatic function into enzyme commission families. Finally, we show that Bio-JOIE can accurately identify PPIs between the SARS-CoV-2 proteins and human proteins, providing valuable insights for advancing research on this new disease.

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selfRL: Two-Level Self-Supervised Transformer Representation Learning for Link Prediction of Heterogeneous Biomedical Networks

10.1101/2020.10.20.347153 ◽

2020 ◽

Author(s):

Xiaoqi Wang ◽

Yaning Yang ◽

Xiangke Liao ◽

Lenli Li ◽

Fei Li ◽

...

Keyword(s):

Supervised Learning ◽

Link Prediction ◽

Level Structure ◽

Representation Learning ◽

Learning Task ◽

Learning Tasks ◽

Biomedical Problem ◽

Great Performance ◽

Meta Path ◽

Frame Work

AbstractPredicting potential links in heterogeneous biomedical networks (HBNs) can greatly benefit various important biomedical problem. However, the self-supervised representation learning for link prediction in HBNs has been slightly explored in previous researches. Therefore, this study proposes a two-level self-supervised representation learning, namely selfRL, for link prediction in heterogeneous biomedical networks. The meta path detection-based self-supervised learning task is proposed to learn representation vectors that can capture the global-level structure and semantic feature in HBNs. The vertex entity mask-based self-supervised learning mechanism is designed to enhance local association of vertices. Finally, the representations from two tasks are concatenated to generate high-quality representation vectors. The results of link prediction on six datasets show selfRL outperforms 25 state-of-the-art methods. In particular, selfRL reveals great performance with results close to 1 in terms of AUC and AUPR on the NeoDTI-net dataset. In addition, the PubMed publications demonstrate that nine out of ten drugs screened by selfRL can inhibit the cytokine storm in COVID-19 patients. In summary, selfRL provides a general frame-work that develops self-supervised learning tasks with unlabeled data to obtain promising representations for improving link prediction.

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TransPath: Representation Learning for Heterogeneous Information Networks via Translation Mechanism

10.20944/preprints201801.0147.v1 ◽

2018 ◽

Author(s):

Yang Fang ◽

Xiang Zhao ◽

Zhen Tan

Keyword(s):

Large Scale ◽

Representation Learning ◽

Information Networks ◽

Heterogeneous Information ◽

Structure Information ◽

Heterogeneous Information Networks ◽

Network Representation ◽

Meta Path ◽

Translation Mechanism ◽

Real World Datasets

In this paper, we propose a novel network representation learning model TransPath to encode heterogeneous information networks (HINs). Traditional network representation learning models aim to learn the embeddings of a homogeneous network. TransPath is able to capture the rich semantic and structure information of a HIN via meta-paths. We take advantage of the concept of translation mechanism in knowledge graph which regards a meta-path, instead of an edge, as a translating operation from the first node to the last node. Moreover, we propose a user-guided meta-path sampling strategy which takes users' preference as a guidance, which could explore the semantics of a path more precisely, and meanwhile improve model efficiency via the avoidance of other noisy and meaningless meta-paths. We evaluate our model on two large-scale real-world datasets DBLP and YELP, and two benchmark tasks similarity search and node classification. We observe that TransPath outperforms other state-of-the-art baselines consistently and significantly.

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