1
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Zhu H, Hao H, Yu L. Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance. BMC Biol 2023; 21:294. [PMID: 38115088 PMCID: PMC10731776 DOI: 10.1186/s12915-023-01796-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Enormous clinical and biomedical researches have demonstrated that microbes are crucial to human health. Identifying associations between microbes and diseases can not only reveal potential disease mechanisms, but also facilitate early diagnosis and promote precision medicine. Due to the data perturbation and unsatisfactory latent representation, there is a significant room for improvement. RESULTS In this work, we proposed a novel framework, Multi-scale Variational Graph AutoEncoder embedding Wasserstein distance (MVGAEW) to predict disease-related microbes, which had the ability to resist data perturbation and effectively generate latent representations for both microbes and diseases from the perspective of distribution. First, we calculated multiple similarities and integrated them through similarity network confusion. Subsequently, we obtained node latent representations by improved variational graph autoencoder. Ultimately, XGBoost classifier was employed to predict potential disease-related microbes. We also introduced multi-order node embedding reconstruction to enhance the representation capacity. We also performed ablation studies to evaluate the contribution of each section of our model. Moreover, we conducted experiments on common drugs and case studies, including Alzheimer's disease, Crohn's disease, and colorectal neoplasms, to validate the effectiveness of our framework. CONCLUSIONS Significantly, our model exceeded other currently state-of-the-art methods, exhibiting a great improvement on the HMDAD database.
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Affiliation(s)
- Huan Zhu
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Hongxia Hao
- School of Computer Science and Technology, Xidian University, Xi'an, China.
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, China.
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2
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Peng L, Huang L, Tian G, Wu Y, Li G, Cao J, Wang P, Li Z, Duan L. Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network. Front Microbiol 2023; 14:1244527. [PMID: 37789848 PMCID: PMC10543759 DOI: 10.3389/fmicb.2023.1244527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 08/16/2023] [Indexed: 10/05/2023] Open
Abstract
Background Microbes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe-disease association (MDA) prediction are expensive, time-consuming, and labor-intensive. Methods We developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe-disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs. Results GPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation. Conclusion The proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
- College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, China
| | - Liangliang Huang
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Geng Tian
- Geneis (Beijing) Co. Ltd., Beijing, China
| | - Yan Wu
- Geneis (Beijing) Co. Ltd., Beijing, China
| | - Guang Li
- Faculty of Pediatrics, The Chinese PLA General Hospital, Beijing, China
- Department of Pediatric Surgery, The Seventh Medical Center of PLA General Hospital, Beijing, China
- National Engineering Laboratory for Birth Defects Prevention and Control of Key Technology, Beijing, China
- Beijing Key Laboratory of Pediatric Organ Failure, Beijing, China
| | - Jianying Cao
- Faculty of Pediatrics, The Chinese PLA General Hospital, Beijing, China
- Department of Pediatric Surgery, The Seventh Medical Center of PLA General Hospital, Beijing, China
- National Engineering Laboratory for Birth Defects Prevention and Control of Key Technology, Beijing, China
- Beijing Key Laboratory of Pediatric Organ Failure, Beijing, China
| | - Peng Wang
- School of Computer Science, Hunan Institute of Technology, Hengyang, China
| | - Zejun Li
- School of Computer Science, Hunan Institute of Technology, Hengyang, China
| | - Lian Duan
- Faculty of Pediatrics, The Chinese PLA General Hospital, Beijing, China
- Department of Pediatric Surgery, The Seventh Medical Center of PLA General Hospital, Beijing, China
- National Engineering Laboratory for Birth Defects Prevention and Control of Key Technology, Beijing, China
- Beijing Key Laboratory of Pediatric Organ Failure, Beijing, China
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3
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Karkera N, Acharya S, Palaniappan SK. Leveraging pre-trained language models for mining microbiome-disease relationships. BMC Bioinformatics 2023; 24:290. [PMID: 37468830 DOI: 10.1186/s12859-023-05411-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 07/13/2023] [Indexed: 07/21/2023] Open
Abstract
BACKGROUND The growing recognition of the microbiome's impact on human health and well-being has prompted extensive research into discovering the links between microbiome dysbiosis and disease (healthy) states. However, this valuable information is scattered in unstructured form within biomedical literature. The structured extraction and qualification of microbe-disease interactions are important. In parallel, recent advancements in deep-learning-based natural language processing algorithms have revolutionized language-related tasks such as ours. This study aims to leverage state-of-the-art deep-learning language models to extract microbe-disease relationships from biomedical literature. RESULTS In this study, we first evaluate multiple pre-trained large language models within a zero-shot or few-shot learning context. In this setting, the models performed poorly out of the box, emphasizing the need for domain-specific fine-tuning of these language models. Subsequently, we fine-tune multiple language models (specifically, GPT-3, BioGPT, BioMedLM, BERT, BioMegatron, PubMedBERT, BioClinicalBERT, and BioLinkBERT) using labeled training data and evaluate their performance. Our experimental results demonstrate the state-of-the-art performance of these fine-tuned models ( specifically GPT-3, BioMedLM, and BioLinkBERT), achieving an average F1 score, precision, and recall of over [Formula: see text] compared to the previous best of 0.74. CONCLUSION Overall, this study establishes that pre-trained language models excel as transfer learners when fine-tuned with domain and problem-specific data, enabling them to achieve state-of-the-art results even with limited training data for extracting microbiome-disease interactions from scientific publications.
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Affiliation(s)
| | - Sathwik Acharya
- The Systems Biology Institute, Tokyo, Japan
- PES University, Bengaluru, India
| | - Sucheendra K Palaniappan
- The Systems Biology Institute, Tokyo, Japan.
- Iom Bioworks Pvt Ltd., Bengaluru, India.
- SBX Corporation, Tokyo, Japan.
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4
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Wang F, Yang H, Wu Y, Peng L, Li X. SAELGMDA: Identifying human microbe-disease associations based on sparse autoencoder and LightGBM. Front Microbiol 2023; 14:1207209. [PMID: 37415823 PMCID: PMC10320730 DOI: 10.3389/fmicb.2023.1207209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 05/18/2023] [Indexed: 07/08/2023] Open
Abstract
Introduction Identification of complex associations between diseases and microbes is important to understand the pathogenesis of diseases and design therapeutic strategies. Biomedical experiment-based Microbe-Disease Association (MDA) detection methods are expensive, time-consuming, and laborious. Methods Here, we developed a computational method called SAELGMDA for potential MDA prediction. First, microbe similarity and disease similarity are computed by integrating their functional similarity and Gaussian interaction profile kernel similarity. Second, one microbe-disease pair is presented as a feature vector by combining the microbe and disease similarity matrices. Next, the obtained feature vectors are mapped to a low-dimensional space based on a Sparse AutoEncoder. Finally, unknown microbe-disease pairs are classified based on Light Gradient boosting machine. Results The proposed SAELGMDA method was compared with four state-of-the-art MDA methods (MNNMDA, GATMDA, NTSHMDA, and LRLSHMDA) under five-fold cross validations on diseases, microbes, and microbe-disease pairs on the HMDAD and Disbiome databases. The results show that SAELGMDA computed the best accuracy, Matthews correlation coefficient, AUC, and AUPR under the majority of conditions, outperforming the other four MDA prediction models. In particular, SAELGMDA obtained the best AUCs of 0.8358 and 0.9301 under cross validation on diseases, 0.9838 and 0.9293 under cross validation on microbes, and 0.9857 and 0.9358 under cross validation on microbe-disease pairs on the HMDAD and Disbiome databases. Colorectal cancer, inflammatory bowel disease, and lung cancer are diseases that severely threat human health. We used the proposed SAELGMDA method to find possible microbes for the three diseases. The results demonstrate that there are potential associations between Clostridium coccoides and colorectal cancer and one between Sphingomonadaceae and inflammatory bowel disease. In addition, Veillonella may associate with autism. The inferred MDAs need further validation. Conclusion We anticipate that the proposed SAELGMDA method contributes to the identification of new MDAs.
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Affiliation(s)
- Feixiang Wang
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Huandong Yang
- Department of Gastrointestinal Surgery, Yidu Central Hospital of Weifang, Weifang, China
| | - Yan Wu
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Xiaoling Li
- The Second Department of Oncology, Beidahuang Industry Group General Hospital, Harbin, China
- The Second Department of Oncology, Heilongjiang Second Cancer Hospital, Harbin, China
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5
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Li S, Chang M, Tong L, Wang Y, Wang M, Wang F. Screening potential lncRNA biomarkers for breast cancer and colorectal cancer combining random walk and logistic matrix factorization. Front Genet 2023; 13:1023615. [PMID: 36744179 PMCID: PMC9895102 DOI: 10.3389/fgene.2022.1023615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 10/10/2022] [Indexed: 01/21/2023] Open
Abstract
Breast cancer and colorectal cancer are two of the most common malignant tumors worldwide. They cause the leading causes of cancer mortality. Many researches have demonstrated that long noncoding RNAs (lncRNAs) have close linkages with the occurrence and development of the two cancers. Therefore, it is essential to design an effective way to identify potential lncRNA biomarkers for them. In this study, we developed a computational method (LDA-RWLMF) by integrating random walk with restart and Logistic Matrix Factorization to investigate the roles of lncRNA biomarkers in the prognosis and diagnosis of the two cancers. We first fuse disease semantic and Gaussian association profile similarities and lncRNA functional and Gaussian association profile similarities. Second, we design a negative selection algorithm to extract negative LncRNA-Disease Associations (LDA) based on random walk. Third, we develop a logistic matrix factorization model to predict possible LDAs. We compare our proposed LDA-RWLMF method with four classical LDA prediction methods, that is, LNCSIM1, LNCSIM2, ILNCSIM, and IDSSIM. The results from 5-fold cross validation on the MNDR dataset show that LDA-RWLMF computes the best AUC value of 0.9312, outperforming the above four LDA prediction methods. Finally, we rank all lncRNA biomarkers for the two cancers after determining the performance of LDA-RWLMF, respectively. We find that 48 and 50 lncRNAs have the highest association scores with breast cancer and colorectal cancer among all lncRNAs known to associate with them on the MNDR dataset, respectively. We predict that lncRNAs HULC and HAR1A could be separately potential biomarkers for breast cancer and colorectal cancer and need to biomedical experimental validation.
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6
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Yang M, Huang ZA, Gu W, Han K, Pan W, Yang X, Zhu Z. Prediction of biomarker-disease associations based on graph attention network and text representation. Brief Bioinform 2022; 23:6651308. [PMID: 35901464 DOI: 10.1093/bib/bbac298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/28/2022] [Accepted: 06/30/2022] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION The associations between biomarkers and human diseases play a key role in understanding complex pathology and developing targeted therapies. Wet lab experiments for biomarker discovery are costly, laborious and time-consuming. Computational prediction methods can be used to greatly expedite the identification of candidate biomarkers. RESULTS Here, we present a novel computational model named GTGenie for predicting the biomarker-disease associations based on graph and text features. In GTGenie, a graph attention network is utilized to characterize diverse similarities of biomarkers and diseases from heterogeneous information resources. Meanwhile, a pretrained BERT-based model is applied to learn the text-based representation of biomarker-disease relation from biomedical literature. The captured graph and text features are then integrated in a bimodal fusion network to model the hybrid entity representation. Finally, inductive matrix completion is adopted to infer the missing entries for reconstructing relation matrix, with which the unknown biomarker-disease associations are predicted. Experimental results on HMDD, HMDAD and LncRNADisease data sets showed that GTGenie can obtain competitive prediction performance with other state-of-the-art methods. AVAILABILITY The source code of GTGenie and the test data are available at: https://github.com/Wolverinerine/GTGenie.
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Affiliation(s)
- Minghao Yang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518000, China
| | - Zhi-An Huang
- Center for Computer Science and Information Technology, City University of Hong Kong Dongguan Research Institute, Dongguan, China
| | - Wenhao Gu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518000, China.,GeneGenieDx Corp, 160 E Tasman Dr, San Jose, CA 95134
| | - Kun Han
- GeneGenieDx Corp, 160 E Tasman Dr, San Jose, CA 95134
| | - Wenying Pan
- GeneGenieDx Corp, 160 E Tasman Dr, San Jose, CA 95134
| | - Xiao Yang
- GeneGenieDx Corp, 160 E Tasman Dr, San Jose, CA 95134
| | - Zexuan Zhu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518000, China
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7
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Peng L, Wang F, Wang Z, Tan J, Huang L, Tian X, Liu G, Zhou L. Cell-cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies. Brief Bioinform 2022; 23:6618236. [PMID: 35753695 DOI: 10.1093/bib/bbac234] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/15/2022] [Accepted: 05/19/2022] [Indexed: 12/12/2022] Open
Abstract
Carcinomas are complex ecosystems composed of cancer, stromal and immune cells. Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell-cell communication can be inferred through ligand-receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand-receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell-cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell-cell communication estimation tools for tumor-targeted therapy.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China.,College of Life Sciences and Chemistry, Hunan University of Technology, 412007, Hunan, China
| | - Feixiang Wang
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
| | - Zhao Wang
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
| | - Jingwei Tan
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
| | - Li Huang
- Academy of Arts and Design, Tsinghua University, 10084, Beijing, China.,The Future Laboratory, Tsinghua University, 10084, Beijing, China
| | - Xiongfei Tian
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
| | - Guangyi Liu
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
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8
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Zhu B, Xu Y, Zhao P, Yiu SM, Yu H, Shi JY. NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations. Front Microbiol 2022; 13:846915. [PMID: 35479616 PMCID: PMC9035839 DOI: 10.3389/fmicb.2022.846915] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 02/14/2022] [Indexed: 11/13/2022] Open
Abstract
Many drugs can be metabolized by human microbes; the drug metabolites would significantly alter pharmacological effects and result in low therapeutic efficacy for patients. Hence, it is crucial to identify potential drug–microbe associations (DMAs) before the drug administrations. Nevertheless, traditional DMA determination cannot be applied in a wide range due to the tremendous number of microbe species, high costs, and the fact that it is time-consuming. Thus, predicting possible DMAs in computer technology is an essential topic. Inspired by other issues addressed by deep learning, we designed a deep learning-based model named Nearest Neighbor Attention Network (NNAN). The proposed model consists of four components, namely, a similarity network constructor, a nearest-neighbor aggregator, a feature attention block, and a predictor. In brief, the similarity block contains a microbe similarity network and a drug similarity network. The nearest-neighbor aggregator generates the embedding representations of drug–microbe pairs by integrating drug neighbors and microbe neighbors of each drug–microbe pair in the network. The feature attention block evaluates the importance of each dimension of drug–microbe pair embedding by a set of ordinary multi-layer neural networks. The predictor is an ordinary fully-connected deep neural network that functions as a binary classifier to distinguish potential DMAs among unlabeled drug–microbe pairs. Several experiments on two benchmark databases are performed to evaluate the performance of NNAN. First, the comparison with state-of-the-art baseline approaches demonstrates the superiority of NNAN under cross-validation in terms of predicting performance. Moreover, the interpretability inspection reveals that a drug tends to associate with a microbe if it finds its top-l most similar neighbors that associate with the microbe.
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Affiliation(s)
- Bei Zhu
- School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Yi Xu
- School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Pengcheng Zhao
- School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Siu-Ming Yiu
- Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Hui Yu
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
- *Correspondence: Hui Yu,
| | - Jian-Yu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Jian-Yu Shi,
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9
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Wang L, Tan Y, Yang X, Kuang L, Ping P. Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models. Brief Bioinform 2022; 23:6553604. [PMID: 35325024 DOI: 10.1093/bib/bbac080] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 12/11/2022] Open
Abstract
In recent years, with the rapid development of techniques in bioinformatics and life science, a considerable quantity of biomedical data has been accumulated, based on which researchers have developed various computational approaches to discover potential associations between human microbes, drugs and diseases. This paper provides a comprehensive overview of recent advances in prediction of potential correlations between microbes, drugs and diseases from biological data to computational models. Firstly, we introduced the widely used datasets relevant to the identification of potential relationships between microbes, drugs and diseases in detail. And then, we divided a series of a lot of representative computing models into five major categories including network, matrix factorization, matrix completion, regularization and artificial neural network for in-depth discussion and comparison. Finally, we analysed possible challenges and opportunities in this research area, and at the same time we outlined some suggestions for further improvement of predictive performances as well.
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Affiliation(s)
- Lei Wang
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China.,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Yaqin Tan
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China.,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Xiaoyu Yang
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China.,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Linai Kuang
- Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Pengyao Ping
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China
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10
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Xu D, Xu H, Zhang Y, Gao R. Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes. Front Microbiol 2022; 13:834982. [PMID: 35369503 PMCID: PMC8965656 DOI: 10.3389/fmicb.2022.834982] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 01/19/2022] [Indexed: 12/14/2022] Open
Abstract
Extensive clinical and biomedical studies have shown that microbiome plays a prominent role in human health. Identifying potential microbe–disease associations (MDAs) can help reveal the pathological mechanism of human diseases and be useful for the prevention, diagnosis, and treatment of human diseases. Therefore, it is necessary to develop effective computational models and reduce the cost and time of biological experiments. Here, we developed a novel machine learning-based joint framework called CWNMF-GLapRLS for human MDA prediction using the proposed collaborative weighted non-negative matrix factorization (CWNMF) technique and graph Laplacian regularized least squares. Especially, to fuse more similarity information, we calculated the functional similarity of microbes. To deal with missing values and effectively overcome the data sparsity problem, we proposed a collaborative weighted NMF technique to reconstruct the original association matrix. In addition, we developed a graph Laplacian regularized least-squares method for prediction. The experimental results of fivefold and leave-one-out cross-validation demonstrated that our method achieved the best performance by comparing it with 5 state-of-the-art methods on the benchmark dataset. Case studies further showed that the proposed method is an effective tool to predict potential MDAs and can provide more help for biomedical researchers.
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Affiliation(s)
- Da Xu
- School of Mathematics and Statistics, Shandong University, Weihai, China
| | - Hanxiao Xu
- School of Mathematics and Statistics, Shandong University, Weihai, China
| | - Yusen Zhang
- School of Mathematics and Statistics, Shandong University, Weihai, China
- *Correspondence: Yusen Zhang,
| | - Rui Gao
- School of Control Science and Engineering, Shandong University, Jinan, China
- Rui Gao,
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11
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Li G, Wang D, Zhang Y, Liang C, Xiao Q, Luo J. Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA-Disease Associations Based on Multi-Source Data. Front Genet 2022; 13:829937. [PMID: 35198012 PMCID: PMC8859418 DOI: 10.3389/fgene.2022.829937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
Cumulative research studies have verified that multiple circRNAs are closely associated with the pathogenic mechanism and cellular level. Exploring human circRNA-disease relationships is significant to decipher pathogenic mechanisms and provide treatment plans. At present, several computational models are designed to infer potential relationships between diseases and circRNAs. However, the majority of existing approaches could not effectively utilize the multisource data and achieve poor performance in sparse networks. In this study, we develop an advanced method, GATGCN, using graph attention network (GAT) and graph convolutional network (GCN) to detect potential circRNA-disease relationships. First, several sources of biomedical information are fused via the centered kernel alignment model (CKA), which calculates the corresponding weight of different kernels. Second, we adopt the graph attention network to learn latent representation of diseases and circRNAs. Third, the graph convolutional network is deployed to effectively extract features of associations by aggregating feature vectors of neighbors. Meanwhile, GATGCN achieves the prominent AUC of 0.951 under leave-one-out cross-validation and AUC of 0.932 under 5-fold cross-validation. Furthermore, case studies on lung cancer, diabetes retinopathy, and prostate cancer verify the reliability of GATGCN for detecting latent circRNA-disease pairs.
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Affiliation(s)
- Guanghui Li
- School of Information Engineering, East China Jiaotong University, Nanchang, China
| | - Diancheng Wang
- School of Information Engineering, East China Jiaotong University, Nanchang, China
| | - Yuejin Zhang
- School of Information Engineering, East China Jiaotong University, Nanchang, China
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Qiu Xiao
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
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12
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Zhu X, He X, Kuang L, Chen Z, Lancine C. A Novel Collaborative Filtering Model-Based Method for Identifying Essential Proteins. Front Genet 2021; 12:763153. [PMID: 34745230 PMCID: PMC8566338 DOI: 10.3389/fgene.2021.763153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 09/13/2021] [Indexed: 11/19/2022] Open
Abstract
Considering that traditional biological experiments are expensive and time consuming, it is important to develop effective computational models to infer potential essential proteins. In this manuscript, a novel collaborative filtering model-based method called CFMM was proposed, in which, an updated protein–domain interaction (PDI) network was constructed first by applying collaborative filtering algorithm on the original PDI network, and then, through integrating topological features of PDI networks with biological features of proteins, a calculative method was designed to infer potential essential proteins based on an improved PageRank algorithm. The novelties of CFMM lie in construction of an updated PDI network, application of the commodity-customer-based collaborative filtering algorithm, and introduction of the calculation method based on an improved PageRank algorithm, which ensured that CFMM can be applied to predict essential proteins without relying entirely on known protein–domain associations. Simulation results showed that CFMM can achieve reliable prediction accuracies of 92.16, 83.14, 71.37, 63.87, 55.84, and 52.43% in the top 1, 5, 10, 15, 20, and 25% predicted candidate key proteins based on the DIP database, which are remarkably higher than 14 competitive state-of-the-art predictive models as a whole, and in addition, CFMM can achieve satisfactory predictive performances based on different databases with various evaluation measurements, which further indicated that CFMM may be a useful tool for the identification of essential proteins in the future.
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Affiliation(s)
- Xianyou Zhu
- College of Computer Science and Technology, Hengyang Normal University, Hengyang, China.,Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, China
| | - Xin He
- College of Computer, Xiangtan University, Xiangtan, China
| | - Linai Kuang
- College of Computer, Xiangtan University, Xiangtan, China
| | - Zhiping Chen
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| | - Camara Lancine
- The Social Sciences and Management University of Bamako, Bamako, Mali
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13
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Wu C, Xiao X, Yang C, Chen J, Yi J, Qiu Y. Mining microbe-disease interactions from literature via a transfer learning model. BMC Bioinformatics 2021; 22:432. [PMID: 34507528 PMCID: PMC8430297 DOI: 10.1186/s12859-021-04346-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 08/28/2021] [Indexed: 12/22/2022] Open
Abstract
Background Interactions of microbes and diseases are of great importance for biomedical research. However, large-scale of microbe–disease interactions are hidden in the biomedical literature. The structured databases for microbe–disease interactions are in limited amounts. In this paper, we aim to construct a large-scale database for microbe–disease interactions automatically. We attained this goal via applying text mining methods based on a deep learning model with a moderate curation cost. We also built a user-friendly web interface that allows researchers to navigate and query required information. Results Firstly, we manually constructed a golden-standard corpus and a sliver-standard corpus (SSC) for microbe–disease interactions for curation. Moreover, we proposed a text mining framework for microbe–disease interaction extraction based on a pretrained model BERE. We applied named entity recognition tools to detect microbe and disease mentions from the free biomedical texts. After that, we fine-tuned the pretrained model BERE to recognize relations between targeted entities, which was originally built for drug–target interactions or drug–drug interactions. The introduction of SSC for model fine-tuning greatly improved detection performance for microbe–disease interactions, with an average reduction in error of approximately 10%. The MDIDB website offers data browsing, custom searching for specific diseases or microbes, and batch downloading. Conclusions Evaluation results demonstrate that our method outperform the baseline model (rule-based PKDE4J) with an average \documentclass[12pt]{minimal}
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\begin{document}$$F_1$$\end{document}F1-score of 73.81%. For further validation, we randomly sampled nearly 1000 predicted interactions by our model, and manually checked the correctness of each interaction, which gives a 73% accuracy. The MDIDB webiste is freely avaliable throuth http://dbmdi.com/index/
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Affiliation(s)
- Chengkun Wu
- State Key Laboratory of High-Performance Computing, National University of Defense Technology, Changsha, 410073, China. .,College of Computer, National University of Defense Technology, Changsha, 410073, China.
| | - Xinyi Xiao
- College of Computer, National University of Defense Technology, Changsha, 410073, China
| | - Canqun Yang
- College of Computer, National University of Defense Technology, Changsha, 410073, China
| | - JinXiang Chen
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Jiacai Yi
- College of Computer, National University of Defense Technology, Changsha, 410073, China
| | - Yanlong Qiu
- College of Computer, National University of Defense Technology, Changsha, 410073, China
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14
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Zhao L, Li Y, Wang Y, Gao Q, Ge Z, Sun X, Li Y. Development and Validation of a Nomogram for the Prediction of Hospital Mortality of Patients With Encephalopathy Caused by Microbial Infection: A Retrospective Cohort Study. Front Microbiol 2021; 12:737066. [PMID: 34489922 PMCID: PMC8417384 DOI: 10.3389/fmicb.2021.737066] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 08/02/2021] [Indexed: 12/12/2022] Open
Abstract
Background Hospital mortality is high for patients with encephalopathy caused by microbial infection. Microbial infections often induce sepsis. The damage to the central nervous system (CNS) is defined as sepsis-associated encephalopathy (SAE). However, the relationship between pathogenic microorganisms and the prognosis of SAE patients is still unclear, especially gut microbiota, and there is no clinical tool to predict hospital mortality for SAE patients. The study aimed to explore the relationship between pathogenic microorganisms and the hospital mortality of SAE patients and develop a nomogram for the prediction of hospital mortality in SAE patients. Methods The study is a retrospective cohort study. The lasso regression model was used for data dimension reduction and feature selection. Model of hospital mortality of SAE patients was developed by multivariable Cox regression analysis. Calibration and discrimination were used to assess the performance of the nomogram. Decision curve analysis (DCA) to evaluate the clinical utility of the model. Results Unfortunately, the results of our study did not find intestinal infection and microorganisms of the gastrointestinal (such as: Escherichia coli) that are related to the prognosis of SAE. Lasso regression and multivariate Cox regression indicated that factors including respiratory failure, lactate, international normalized ratio (INR), albumin, SpO2, temperature, and renal replacement therapy were significantly correlated with hospital mortality. The AUC of 0.812 under the nomogram was more than that of the Simplified Acute Physiology Score (0.745), indicating excellent discrimination. DCA demonstrated that using the nomogram or including the prognostic signature score status was better than without the nomogram or using the SAPS II at predicting hospital mortality. Conclusion The prognosis of SAE patients has nothing to do with intestinal and microbial infections. We developed a nomogram that predicts hospital mortality in patients with SAE according to clinical data. The nomogram exhibited excellent discrimination and calibration capacity, favoring its clinical utility.
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Affiliation(s)
- Lina Zhao
- Emergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.,Department of Critical Care Medicine, Chifeng Municipal Hospital, Chifeng Clinical Medical College of Inner Mongolia Medical University, Chifeng, China
| | - Yun Li
- Department of Anesthesiology, Chifeng Municipal Hospital, Chifeng Clinical Medical College of Inner Mongolia Medical University, Chifeng, China
| | - Yunying Wang
- Department of Critical Care Medicine, Chifeng Municipal Hospital, Chifeng Clinical Medical College of Inner Mongolia Medical University, Chifeng, China
| | - Qian Gao
- Department of Neurology, Yidu Central Hospital Affiliated to Weifang Medical University, Weifang, China
| | - Zengzheng Ge
- Emergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Xibo Sun
- Department of Neurology, Yidu Central Hospital Affiliated to Weifang Medical University, Weifang, China
| | - Yi Li
- Emergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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15
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Sun X, Cheng L, Liu J, Xie C, Yang J, Li F. Predicting lncRNA-Protein Interaction With Weighted Graph-Regularized Matrix Factorization. Front Genet 2021; 12:690096. [PMID: 34335693 PMCID: PMC8322775 DOI: 10.3389/fgene.2021.690096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 05/21/2021] [Indexed: 11/13/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) are widely concerned because of their close associations with many key biological activities. Though precise functions of most lncRNAs are unknown, research works show that lncRNAs usually exert biological function by interacting with the corresponding proteins. The experimental validation of interactions between lncRNAs and proteins is costly and time-consuming. In this study, we developed a weighted graph-regularized matrix factorization (LPI-WGRMF) method to find unobserved lncRNA-protein interactions (LPIs) based on lncRNA similarity matrix, protein similarity matrix, and known LPIs. We compared our proposed LPI-WGRMF method with five classical LPI prediction methods, that is, LPBNI, LPI-IBNRA, LPIHN, RWR, and collaborative filtering (CF). The results demonstrate that the LPI-WGRMF method can produce high-accuracy performance, obtaining an AUC score of 0.9012 and AUPR of 0.7324. The case study showed that SFPQ, SNHG3, and PRPF31 may associate with Q9NUL5, Q9NUL5, and Q9UKV8 with the highest linking probabilities and need to further experimental validation.
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Affiliation(s)
- Xibo Sun
- Yidu Central Hospital of Weifang, Weifang, China
| | | | - Jinyang Liu
- Geneis Beijing Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Cuinan Xie
- Geneis Beijing Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jiasheng Yang
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Fu Li
- Department of Thoracic Surgery, The Second Affiliated Hospital of Hainan Medical University, Haikou, China
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16
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Chen Y, Sun H, Sun M, Shi C, Sun H, Shi X, Ji B, Cui J. Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe-Disease Association Prediction. Front Microbiol 2021; 12:650056. [PMID: 33796094 PMCID: PMC8007907 DOI: 10.3389/fmicb.2021.650056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 02/09/2021] [Indexed: 12/02/2022] Open
Abstract
Microbes are closely associated with the formation and development of diseases. The identification of the potential associations between microbes and diseases can boost the understanding of various complex diseases. Wet experiments applied to microbe-disease association (MDA) identification are costly and time-consuming. In this manuscript, we developed a novel computational model, NLLMDA, to find unobserved MDAs, especially for colon cancer and colorectal carcinoma. NLLMDA integrated negative MDA selection, linear neighborhood similarity, label propagation, information integration, and known biological data. The Gaussian association profile (GAP) similarity of microbes and GAPs similarity and symptom similarity of diseases were firstly computed. Secondly, linear neighborhood method was then applied to the above computed similarity matrices to obtain more stable performance. Thirdly, negative MDA samples were selected, and the label propagation algorithm was used to score for microbe-disease pairs. The final association probabilities can be computed based on the information integration method. NLLMDA was compared with the other five classical MDA methods and obtained the highest area under the curve (AUC) value of 0.9031 and 0.9335 on cross-validations of diseases and microbe-disease pairs. The results suggest that NLLMDA was an effective prediction method. More importantly, we found that Acidobacteriaceae may have a close link with colon cancer and Tannerella may densely associate with colorectal carcinoma.
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Affiliation(s)
- Yu Chen
- The Cancer Hospital of Jia Mu Si, Jiamusi, China
| | - Hongjian Sun
- Oncological Surgery, The Central Hospital of Jia Mu Si, Jiamusi, China
| | - Mengzhe Sun
- Oncological Surgery, The Central Hospital of Jia Mu Si, Jiamusi, China
| | - Changguo Shi
- Department of Thoracic Surgery, The Cancer Hospital of Jia Mu Si, Jiamusi, China
| | - Hongmei Sun
- Medical Oncology, The Cancer Hospital of Jia Mu Si, Jiamusi, China
| | - Xiaoli Shi
- Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Binbin Ji
- Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jinpeng Cui
- Department of Laboratory Medicine, Yantaishan Hospital of Yantai City, Yantai, China
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