摘要：
The metabolome serves as a crucial intermediary between the microbiome and its host, playing a key role in revealing their biological functions. Previous research has established connections between various microbiomes and metabolomes through correlation and association analyses. Although traditional statistical analysis methods have been used to quantify microbe-metabolite correlations, they do not fully elucidate the biological connections between these associated pairs. In recent years, some models based on networks have been proposed to predict microbe-metabolite interactions. However, relying solely on microbial abundance to reconstruct metabolomic profiles may overlook the complex and interactive synergistic relationships within the microbiome and metabolome. In this study, a novel Combined Embedding Model based on Heterogeneous Network (CEM_HN) is proposed for inferring microbe-metabolite interactions. First, we build a heterogeneous network, which consists of microbe-metabolite pairs, microbial internal interaction network, and metabolite internal interaction network. Then, we utilize paired embeddings obtained from an autoencoder to extract fine-grained pairwise information in microbe-metabolite pairs. This autoencoder helps capture the hidden biological associations between nodes. Finally, by fusing the node embeddings with paired embeddings, a combined embedding is obtained to infer microbe-metabolite interactions. The experimental results demonstrate that the proposed method has strong performance and biomedical interpretability in predicting microbe-metabolite interactions.
期刊：
IEEE/ACM Transactions on Computational Biology and Bioinformatics,2024年21(1):120-128 ISSN：1545-5963
通讯作者：
Shen, XJ
作者机构：
[Shen, Xianjun; Xiao, Zhen; Zhao, Weizhong; Shen, XJ; Jiang, Xingpeng; Sun, Han; Li, Dandan] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
通讯机构：
[Shen, XJ ] C;Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
关键词：
Drugs;Diseases;Proteins;Heterogeneous networks;Kernel;Semantics;Matrix decomposition;Drug repositioning;drug-disease association prediction;heterogeneous networks;graph attention model;multi-kernel deep learning
摘要：
Computational drug repositioning can identify potential associations between drugs and diseases. This technology has been shown to be effective in accelerating drug development and reducing experimental costs. Although there has been plenty of research for this task, existing methods are deficient in utilizing complex relationships among biological entities, which may not be conducive to subsequent simulation of drug treatment processes. In this article, we propose a heterogeneous graph embedding method called HMLKGAT to infer novel potential drugs for diseases. More specifically, we first construct a heterogeneous information network by combining drug-disease, drug-protein and disease-protein biological networks. Then, a multi-layer graph attention model is utilized to capture the complex associations in the network to derive representations for drugs and diseases. Finally, to maintain the relationship of nodes in different feature spaces, we propose a multi-kernel learning method to transform and combine the representations. Experimental results demonstrate that HMLKGAT outperforms six state-of-the-art methods in drug-related disease prediction, and case studies of five classical drugs further demonstrate the effectiveness of HMLKGAT.
作者机构：
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, PR China;School of Computer, Central China Normal University, Wuhan, Hubei, PR China;National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei, PR China;[Xiaowei Xu] Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR, USA;[Shengwei Ye; Weizhong Zhao; Xianjun Shen; Xingpeng Jiang; Tingting He] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, PR China<&wdkj&>School of Computer, Central China Normal University, Wuhan, Hubei, PR China<&wdkj&>National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei, PR China
会议名称：
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
05 December 2023
会议地点：
Istanbul, Turkiye
会议论文集名称：
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
关键词：
drug repositioning;drug-disease associations prediction;counterfactual links;graph convolutional network;heterogeneous information network
摘要：
Drug repositioning is the process of identifying potential associations between approved drugs and diseases (DDAs) to unveil novel therapeutic applications. Unlike traditional drug discovery approaches, a key advantage of drug repositioning lies in its capacity to leverage the existing knowledge and safety profiles of established medications, leading to significant reductions in both the time and costs associated with drug development. While various methods have been proposed to address this challenge using diverse strategies, the conventional approach for training DDAs prediction models typically relies on random sampling of unknown drug-disease pairs to construct negative samples. However, this method may inadvertently introduce unwanted noise or errors by erroneously categorizing some genuine DDAs as negative samples, thereby leaving room for improvement in current methodologies. In this paper, we introduce a novel negative sample selection algorithm for DDAs prediction that explicitly incorporates causal knowledge inherent in DDAs. To accomplish this, we first construct a heterogeneous information network (HIN) that encompasses various biological entities associated with DDAs and their interconnections. Subsequently, we utilize the outcomes of community detection within the HIN as a form of counterfactual inference, resulting in the development of a negative sample selection algorithm based on a thoughtfully designed counterfactual question. By combining the known DDAs (i.e., positive samples) with the newly generated negative samples, we train a prediction model that incorporates a graph learning module to acquire representations of drugs and diseases. Comprehensive experiments confirm the effectiveness of our proposed model for DDAs prediction.
作者机构：
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China;School of Computer, Central China Normal University, Wuhan, China;National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan, China;[Xiaowei Xu] Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR, USA;[Wenjie Yao] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China<&wdkj&>School of Computer, Central China Normal University, Wuhan, China
会议名称：
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
05 December 2023
会议地点：
Istanbul, Turkiye
会议论文集名称：
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
关键词：
counterfactual inference;data augmentation;drug-side effect associations prediction;heterogeneous information network
摘要：
Detecting drug side effects is crucial in development of drugs. As publicly available biomedical data expands, researchers have devised numerous computational methods for predicting drug-side effect associations (DSAs). Among these, network-based approaches have gained significant attention in the biomedical field. However, the challenge of data scarcity poses a significant hurdle for existing DSAs prediction models. While various data augmentation methods have been created to solve the proble, most rely on random alterations to the original networks, neglecting the causality of DSAs’ existence, thus impacting the predictive performance negatively. In this paper, we introduce a counterfactual inference-based data augmentation method to enhance performance. First,a heterogeneous information network (HIN) is construct by integrating multiple biomedical data sources. We employ community detection on the HIN to preform a counterfactual inference-based method, deriving augmented links and an augmented HIN. Subsequently, we apply a meta-path-based graph neural network to obtain high-quality representations of drugs and side effects, enabling the prediction of DSAs. Our comprehensive experiments confirm the effectiveness of this counterfactual inference-based data augmentation for DSAs prediction.
作者机构：
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, WuHan, Hubei, PR China;School of Computer, Central China Normal University, WuHan, Hubei, PR China;National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, WuHan, Hubei, PR China;[Ruizhe Zhang; Dandan Li; Ying Xiao; Weizhong Zhao; Xingpeng Jiang; Xianjun Shen] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, WuHan, Hubei, PR China<&wdkj&>School of Computer, Central China Normal University, WuHan, Hubei, PR China<&wdkj&>National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, WuHan, Hubei, PR China
会议名称：
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
05 December 2023
会议地点：
Istanbul, Turkiye
会议论文集名称：
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
摘要：
Microbe-drug interactions, which refer to the effects of drugs on microorganisms, play a crucial role in the realm of studying antibiotic-resistant bacteria and the development of antimicrobial agents. With the rapid progress in biomedical field, numerous experimental results containing validated microbe-drug interactions have been available in scientific articles. However, since failing to employ domain knowledge, traditional natural language processing methods encounter challenges in accurately identifying microbe and drug entities. Moreover, the unstructured characteristics and semantic complexity of biomedical literature pose difficulties for conventional text mining approaches to accurately grasp the syntactic features. In this paper, we present a novel microbial-drug relation extraction model called D-GCN, in which dual graph convolutional networks are used. Specifically, the drug database Drugbank is leveraged as external domain knowledge, while the graph convolutional network-based model SemGCN is utilized to learn meaningful features from biomedical texts. In addition, the attention graph convolutional network A-GCN is introduced to capture crucial syntactic features contained in texts. The experimental results show that the proposed model achieves better performance over the selected baseline models, which means D-GCN can not only accurately recognize microbial and drug entity representations, but also effectively identify the microbe-drug interactions.
期刊：
BRIEFINGS IN BIOINFORMATICS,2023年24(2) ISSN：1467-5463
通讯作者：
Weizhong Zhao
作者机构：
[Shen, Xianjun; Zhao, Weizhong; Yuan, Xueling; He, Tingting; Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Shi, Chuan] Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
通讯机构：
[Weizhong Zhao] H;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University , Wuhan, Hubei 430079, P R China<&wdkj&>School of Computer Science, Beijing University of Posts and Telecommunications , Beijing, 100876, P R China<&wdkj&>National Language Resources Monitoring & Research Center for Network Media, Central China Normal University , Wuhan, Hubei 430079, P R China
关键词：
drug–drug interaction;heterogeneous information network;meta-path-based information fusion
摘要：
<jats:title>Abstract</jats:title><jats:p>Drug–drug interactions (DDIs) are compound effects when patients take two or more drugs at the same time, which may weaken the efficacy of drugs or cause unexpected side effects. Thus, accurately predicting DDIs is of great significance for the drug development and the drug safety surveillance. Although many methods have been proposed for the task, the biological knowledge related to DDIs is not fully utilized and the complex semantics among drug-related biological entities are not effectively captured in existing methods, leading to suboptimal performance. Moreover, the lack of interpretability for the predicted results also limits the wide application of existing methods for DDIs prediction. In this study, we propose a novel framework for predicting DDIs with interpretability. Specifically, we construct a heterogeneous information network (HIN) by explicitly utilizing the biological knowledge related to the procedure of inducing DDIs. To capture the complex semantics in HIN, a meta-path-based information fusion mechanism is proposed to learn high-quality representations of drugs. In addition, an attention mechanism is designed to combine semantic information obtained from meta-paths with different lengths to obtain final representations of drugs for DDIs prediction. Comprehensive experiments are conducted on 2410 approved drugs, and the results of predictive performance comparison show that our proposed framework outperforms selected representative baselines on the task of DDIs prediction. The results of ablation study and cold-start scenario indicate that the meta-path-based information fusion mechanism red is beneficial for capturing the complex semantics among drug-related biological entities. Moreover, the results of case study demonstrate that the designed attention mechanism is able to provide partial interpretability for the predicted DDIs. Therefore, the proposed method will be a feasible solution to the task of predicting DDIs.</jats:p>
摘要：
Drug repurposing, which typically applies the procedure of drug-disease associations (DDAs) prediction, is a feasible solution to drug discovery. Compared with traditional methods, drug repurposing can reduce the cost and time for drug development and advance the success rate of drug discovery. Although many methods for drug repurposing have been proposed and the obtained results are relatively acceptable, there is still some room for improving the predictive performance, since those methods fail to consider fully the issue of sparseness in known drug-disease associations. In this paper, we propose a novel multi-task learning framework based on graph representation learning to identify DDAs for drug repurposing. In our proposed framework, a heterogeneous information network is first constructed by combining multiple biological datasets. Then, a module consisting of multiple layers of graph convolutional networks is utilized to learn low-dimensional representations of nodes in the constructed heterogeneous information network. Finally, two types of auxiliary tasks are designed to help to train the target task of DDAs prediction in the multi-task learning framework. Comprehensive experiments are conducted on real data and the results demonstrate the effectiveness of the proposed method for drug repurposing.
作者：
Shengwei Ye;Weizhong Zhao;Xianjun Shen;Xingpeng Jiang;Tingting He
作者机构：
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, PR China;School of Computer, Central China Normal University, Wuhan, Hubei, PR China;National Language Resources Monitoring & Research Center for Network Media Central China Normal University, Wuhan, Hubei, PR China;[Shengwei Ye; Weizhong Zhao; Xianjun Shen; Xingpeng Jiang; Tingting He] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, PR China<&wdkj&>School of Computer, Central China Normal University, Wuhan, Hubei, PR China<&wdkj&>National Language Resources Monitoring & Research Center for Network Media Central China Normal University, Wuhan, Hubei, PR China
会议名称：
2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
06 December 2022
会议地点：
Las Vegas, NV, USA
会议论文集名称：
2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
关键词：
drug repositioning;drug-disease associations prediction;multi-task learning;graph convolutional network;heterogeneous information network
摘要：
Compared with traditional methods, drug repositioning is a viable solution to drug discovery. Drug repositioning usually applies the procedure of drug-disease associations (DDAs) prediction, which can reduce the cost and time of drug development and improve the success rate of drug discovery. In this paper, we develop a new multi-task learning framework based on heterogeneous graph convolutional network (MTHGCN) to recognize potential DDAs. In MTHGCN, a heterogeneous information network is constructed by combining multiple biological datasets. And then, a module based on graph convolutional networks is utilized to learn low-dimensional representations of drugs and diseases. Finally, we design two types of auxiliary tasks to help to train the target DDAs prediction task based on the multi-task learning mechanism. We conduct comprehensive experiments on MTHGCN. The results demonstrate the effectiveness of MTHGCN for drug repositioning.
作者：
Wenjie Yao;Weizhong Zhao;Xingpeng Jiang;Xianjun Shen;Tingting He
作者机构：
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, China;School of Computer, Central China Normal University,Wuhan, Hubei, Hubei, China;National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan, Hubei, China;[Wenjie Yao] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, China<&wdkj&>School of Computer, Central China Normal University,Wuhan, Hubei, Hubei, China;[Weizhong Zhao; Xingpeng Jiang; Xianjun Shen; Tingting He] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, China<&wdkj&>School of Computer, Central China Normal University,Wuhan, Hubei, Hubei, China<&wdkj&>National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan, Hubei, China
会议名称：
2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
06 December 2022
会议地点：
Las Vegas, NV, USA
会议论文集名称：
2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
关键词：
drug-side effect association prediction;meta-path;heterogeneous information network
摘要：
Drug side effect is an important entity in the biomedical field, and identifying the association of the drug-side effects is a very important issue in pharmacological studies and drug risk-benefit. Traditional side effect discovery methods are mainly based on pharmacological experiments. These methods can detect the side effects of some drugs, but the identification process is time-consuming, expensive, and fails to identify some rare side effects. In recent years, with the expansion of massive biomedical data, computational-based methods are widely developed and applied for the task of drug-side effect association(DSA) prediction. However, existing methods cannot fully utilize public biomedical databases, and the complex semantic associations between drugs and side effects are not effectively captured, which leads to suboptimal model prediction performance. In this study, we develop a novel meta-path-based graph neural network model for drug-side effect association prediction. In the proposed model, we first construct a heterogeneous information network(HIN) by fusing multiple biological datasets. And then, a novel meta-path-based feature learning module is designed to learn high-quality representations of drugs and side effects. Finally, with the learned features, the prediction module utilizes a fully connected neural network to make prediction. In addition, comprehensive experiments is conducted, the results demonstrate the effectiveness of our model, indicating that the method will be a viable approach for DSA prediction tasks.
作者机构：
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, PR China;National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, PR China;School of Mathematics and Statistics, Central China Normal University, Wuhan, China;School of Computer, Central China Normal University, Wuhan, PR China;[Ruilong Xiang; Yue Wang; Xianjun Shen] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, PR China<&wdkj&>School of Computer, Central China Normal University, Wuhan, PR China<&wdkj&>National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, PR China
会议名称：
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
09 December 2021
会议地点：
Houston, TX, USA
会议论文集名称：
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
关键词：
Simplex;Higher-order interactions among microbes;Hyperedge weights;Microbial higher-order modules
摘要：
Microbial interactions are of great importance for maintaining ecological balance and regulating human health. Most of the previous studies focus on the paired relationships and pay less attention to the higher-order interaction relationships in the microbial communities. The hypergraph was applied to establish higher-order interaction networks among microbes in microbial communities and the result of hypergraph clustering depends on hyperedge weights. So, we adopt simplex and take advantage of its volume for reconstructing each hyperedge weight to improve hypergraph clustering. We proposed a novel hypergraph clustering algorithm based on simplex (HCBS) here to detect the higher-order interaction modules in the network in a manner of clustering. The HCBS algorithm achieves the hyperedge weight from a unique higher-order relationship by calculating the joint contribution of all nodes in each hyperedge. The maximum modularity was utilized to optimize the clustering number of the hypergraph in the paper. The experimental results illustrate that the HCBS algorithm emphasis the differences of hyperedge weights and it is very effective in detecting microbial higher-order modules.
期刊：
Communications in Computer and Information Science,2020年1205:257-268 ISSN：1865-0929
通讯作者：
Shen, X.
作者机构：
[Yang Y.; Shen X.] School of Computer, Central China Normal University, Wuhan, Hubei, China;[Wang Y.] Collaborative & Innovation Center, Central China Normal University, Wuhan, Hubei, China
通讯机构：
[Shen, X.] S;School of Computer, China
会议名称：
11th International Symposium on Intelligence Computation and Applications, ISICA 2019
会议时间：
16 November 2019 through 17 November 2019
会议论文集名称：
Artificial Intelligence Algorithms and Applications
作者：
Xiangru Tang;Xianjun Shen;Yujie Wang;Yujuan Yang
期刊：
Lecture Notes in Computer Science,2020年12522:300-315 ISSN：0302-9743
作者机构：
School of Computer, Central China Normal University, Wuhan, China;National Language Resources Monitoring and Research Center for Network Media, Beijing, China;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Wuhan, China
会议名称：
19th China National Conference on Computational Linguistics, CCL 2020
会议时间：
30 October 2020 through 1 November 2020
会议论文集名称：
Chinese Computational Linguistics
关键词：
Computational linguistics;Social networking (online);Capsule system;Chinese corpus;Fine grained;Language resources;Offensive languages;Processing speed;Real situation;State of the art;Classification (of information)
期刊：
Communications in Computer and Information Science,2020年 1205: 269-280 ISSN：1865-0929
通讯作者：
Shen, X.
作者机构：
[Shen X.; Yang Y.] School of Computer, Central China Normal University, Wuhan, Hubei, China;[Wang Y.] Collaborative & Innovation Center, Central China Normal University, Wuhan, Hubei, China
通讯机构：
[Shen, X.] S;School of Computer, China
会议名称：
11th International Symposium on Intelligence Computation and Applications, ISICA 2019
会议时间：
16 November 2019 through 17 November 2019
会议论文集名称：
Artificial Intelligence Algorithms and Applications
关键词：
BERT;CNN;Sensitive information;Short text classification
期刊：
Frontiers in Genetics,2020年10:501186 ISSN：1664-8021
通讯作者：
Shen, Xianjun
作者机构：
[Yang, Jincai; Shen, Xianjun; Yu, Limin; Zhong, Duo] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;[Shen, Xianjun; Yu, Limin; Zhong, Duo] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.
通讯机构：
[Shen, Xianjun] C;Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.
关键词：
Laplace normalization;LncRNA;miRNA-disease association prediction;three-layer heterogeneous network;unbalanced random walk
摘要：
Microbial ecosystems are complex, by analyzing co-occurrence modules of microbial communities, we can better understand the conditions of microbial interactions in each environment, and help understand the interaction patterns that maintain the stability of microbial communities. Imbalances in human microbiome are closely related to human disease. Previous modular clustering analysis was based only on the relationship between paired microorganisms. In this paper, we propose calculating the logical relationship between microbial triplet in human body by information entropy and construct a hypergraph based on the triplet network. Based on the hypergraph clustering, we proposed a novel hypergraph clustering algorithm based on intra-class scatter matrix (HCIS) to reconstruct hyperedge similarity, and selected the optimal cluster number by maximizing modularity to analyze higher-order module of microorganisms. The clustering results verify the effectiveness and feasibility of HCIS algorithm for higher-order microbial module analysis.
