1
|
Chen J, Tao R, Qiu Y, Yuan Q. CMFHMDA: a prediction framework for human disease-microbe associations based on cross-domain matrix factorization. Brief Bioinform 2024; 25:bbae481. [PMID: 39327064 PMCID: PMC11427075 DOI: 10.1093/bib/bbae481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Revised: 08/27/2024] [Accepted: 09/12/2024] [Indexed: 09/28/2024] Open
Abstract
Predicting associations between microbes and diseases opens up new avenues for developing diagnostic, preventive, and therapeutic strategies. Given that laboratory-based biological tests to verify these associations are often time-consuming and expensive, there is a critical need for innovative computational frameworks to predict new microbe-disease associations. In this work, we introduce a novel prediction algorithm called Predicting Human Disease-Microbe Associations using Cross-Domain Matrix Factorization (CMFHMDA). Initially, we calculate the composite similarity of diseases and the Gaussian interaction profile similarity of microbes. We then apply the Weighted K Nearest Known Neighbors (WKNKN) algorithm to refine the microbe-disease association matrix. Our CMFHMDA model is subsequently developed by integrating the network data of both microbes and diseases to predict potential associations. The key innovations of this method include using the WKNKN algorithm to preprocess missing values in the association matrix and incorporating cross-domain information from microbes and diseases into the CMFHMDA model. To validate CMFHMDA, we employed three different cross-validation techniques to evaluate the model's accuracy. The results indicate that the CMFHMDA model achieved Area Under the Receiver Operating Characteristic Curve scores of 0.9172, 0.8551, and 0.9351$\pm $0.0052 in global Leave-One-Out Cross-Validation (LOOCV), local LOOCV, and five-fold CV, respectively. Furthermore, many predicted associations have been confirmed by published experimental studies, establishing CMFHMDA as an effective tool for predicting potential disease-associated microbes.
Collapse
Affiliation(s)
- Jing Chen
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, 215009 Suzhou, China
| | - Ran Tao
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, 215009 Suzhou, China
| | - Yi Qiu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, 215009 Suzhou, China
| | - Qun Yuan
- Suzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, 215153 Suzhou, China
| |
Collapse
|
2
|
Shi K, Huang K, Li L, Liu Q, Zhang Y, Zheng H. Predicting microbe-disease association based on graph autoencoder and inductive matrix completion with multi-similarities fusion. Front Microbiol 2024; 15:1438942. [PMID: 39355422 PMCID: PMC11443509 DOI: 10.3389/fmicb.2024.1438942] [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: 05/27/2024] [Accepted: 08/02/2024] [Indexed: 10/03/2024] Open
Abstract
Background Clinical studies have demonstrated that microbes play a crucial role in human health and disease. The identification of microbe-disease interactions can provide insights into the pathogenesis and promote the diagnosis, treatment, and prevention of disease. Although a large number of computational methods are designed to screen novel microbe-disease associations, the accurate and efficient methods are still lacking due to data inconsistence, underutilization of prior information, and model performance. Methods In this study, we proposed an improved deep learning-based framework, named GIMMDA, to identify latent microbe-disease associations, which is based on graph autoencoder and inductive matrix completion. By co-training the information from microbe and disease space, the new representations of microbes and diseases are used to reconstruct microbe-disease association in the end-to-end framework. In particular, a similarity fusion strategy is conducted to improve prediction performance. Results The experimental results show that the performance of GIMMDA is competitive with that of existing state-of-the-art methods on 3 datasets (i.e., HMDAD, Disbiome, and multiMDA). In particular, it performs best with the area under the receiver operating characteristic curve (AUC) of 0.9735, 0.9156, 0.9396 on abovementioned 3 datasets, respectively. And the result also confirms that different similarity fusions can improve the prediction performance. Furthermore, case studies on two diseases, i.e., asthma and obesity, validate the effectiveness and reliability of our proposed model. Conclusion The proposed GIMMDA model show a strong capability in predicting microbe-disease associations. We expect that GPUDMDA will help identify potential microbe-related diseases in the future.
Collapse
Affiliation(s)
- Kai Shi
- College of Computer Science and Engineering, Guilin University of Technology, Guilin, China
- Guangxi Key Laboratory of Embedded Technology and Intelligent Systems, Guilin University of Technology, Guilin, China
| | - Kai Huang
- College of Computer Science and Engineering, Guilin University of Technology, Guilin, China
| | - Lin Li
- College of Computer Science and Engineering, Guilin University of Technology, Guilin, China
| | - Qiaohui Liu
- College of Computer Science and Engineering, Guilin University of Technology, Guilin, China
| | - Yi Zhang
- College of Computer Science and Engineering, Guilin University of Technology, Guilin, China
| | - Huilin Zheng
- College of Computer Science and Engineering, Guilin University of Technology, Guilin, China
| |
Collapse
|
3
|
Zhang C, Zhang Z, Zhang F, Zeng B, Liu X, Wang L. A computational model for potential microbe-disease association detection based on improved graph convolutional networks and multi-channel autoencoders. Front Microbiol 2024; 15:1435408. [PMID: 39144226 PMCID: PMC11322764 DOI: 10.3389/fmicb.2024.1435408] [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: 05/20/2024] [Accepted: 07/05/2024] [Indexed: 08/16/2024] Open
Abstract
Introduction Accumulating evidence shows that human health and disease are closely related to the microbes in the human body. Methods In this manuscript, a new computational model based on graph attention networks and sparse autoencoders, called GCANCAE, was proposed for inferring possible microbe-disease associations. In GCANCAE, we first constructed a heterogeneous network by combining known microbe-disease relationships, disease similarity, and microbial similarity. Then, we adopted the improved GCN and the CSAE to extract neighbor relations in the adjacency matrix and novel feature representations in heterogeneous networks. After that, in order to estimate the likelihood of a potential microbe associated with a disease, we integrated these two types of representations to create unique eigenmatrices for diseases and microbes, respectively, and obtained predicted scores for potential microbe-disease associations by calculating the inner product of these two types of eigenmatrices. Results and discussion Based on the baseline databases such as the HMDAD and the Disbiome, intensive experiments were conducted to evaluate the prediction ability of GCANCAE, and the experimental results demonstrated that GCANCAE achieved better performance than state-of-the-art competitive methods under the frameworks of both 2-fold and 5-fold CV. Furthermore, case studies of three categories of common diseases, such as asthma, irritable bowel syndrome (IBS), and type 2 diabetes (T2D), confirmed the efficiency of GCANCAE.
Collapse
Affiliation(s)
| | - Zhen Zhang
- Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, China
| | | | | | - Xin Liu
- Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, China
| | - Lei Wang
- Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, China
| |
Collapse
|
4
|
Sulaimany S, Farahmandi K, Mafakheri A. Computational prediction of new therapeutic effects of probiotics. Sci Rep 2024; 14:11932. [PMID: 38789535 PMCID: PMC11126595 DOI: 10.1038/s41598-024-62796-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 05/21/2024] [Indexed: 05/26/2024] Open
Abstract
Probiotics are living microorganisms that provide health benefits to their hosts, potentially aiding in the treatment or prevention of various diseases, including diarrhea, irritable bowel syndrome, ulcerative colitis, and Crohn's disease. Motivated by successful applications of link prediction in medical and biological networks, we applied link prediction to the probiotic-disease network to identify unreported relations. Using data from the Probio database and International Classification of Diseases-10th Revision (ICD-10) resources, we constructed a bipartite graph focused on the relationship between probiotics and diseases. We applied customized link prediction algorithms for this bipartite network, including common neighbors, Jaccard coefficient, and Adamic/Adar ranking formulas. We evaluated the results using Area under the Curve (AUC) and precision metrics. Our analysis revealed that common neighbors outperformed the other methods, with an AUC of 0.96 and precision of 0.6, indicating that basic formulas can predict at least six out of ten probable relations correctly. To support our findings, we conducted an exact search of the top 20 predictions and found six confirming papers on Google Scholar and Science Direct. Evidence suggests that Lactobacillus jensenii may provide prophylactic and therapeutic benefits for gastrointestinal diseases and that Lactobacillus acidophilus may have potential activity against urologic and female genital illnesses. Further investigation of other predictions through additional preclinical and clinical studies is recommended. Future research may focus on deploying more powerful link prediction algorithms to achieve better and more accurate results.
Collapse
Affiliation(s)
- Sadegh Sulaimany
- Social and Biological Network Analysis Laboratory (SBNA), Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran.
| | - Kajal Farahmandi
- Department of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
| | - Aso Mafakheri
- Social and Biological Network Analysis Laboratory (SBNA), Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran
| |
Collapse
|
5
|
Lu S, Liang Y, Li L, Miao R, Liao S, Zou Y, Yang C, Ouyang D. Predicting potential microbe-disease associations based on auto-encoder and graph convolution network. BMC Bioinformatics 2023; 24:476. [PMID: 38097930 PMCID: PMC10722760 DOI: 10.1186/s12859-023-05611-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 12/11/2023] [Indexed: 12/17/2023] Open
Abstract
The increasing body of research has consistently demonstrated the intricate correlation between the human microbiome and human well-being. Microbes can impact the efficacy and toxicity of drugs through various pathways, as well as influence the occurrence and metastasis of tumors. In clinical practice, it is crucial to elucidate the association between microbes and diseases. Although traditional biological experiments accurately identify this association, they are time-consuming, expensive, and susceptible to experimental conditions. Consequently, conducting extensive biological experiments to screen potential microbe-disease associations becomes challenging. The computational methods can solve the above problems well, but the previous computational methods still have the problems of low utilization of node features and the prediction accuracy needs to be improved. To address this issue, we propose the DAEGCNDF model predicting potential associations between microbes and diseases. Our model calculates four similar features for each microbe and disease. These features are fused to obtain a comprehensive feature matrix representing microbes and diseases. Our model first uses the graph convolutional network module to extract low-rank features with graph information of microbes and diseases, and then uses a deep sparse Auto-Encoder to extract high-rank features of microbe-disease pairs, after which the low-rank and high-rank features are spliced to improve the utilization of node features. Finally, Deep Forest was used for microbe-disease potential relationship prediction. The experimental results show that combining low-rank and high-rank features helps to improve the model performance and Deep Forest has better classification performance than the baseline model.
Collapse
Affiliation(s)
- Shanghui Lu
- Faculty of Innovation Enginee, Macau University of Science and Technology, Avenida Wai Long, Taipa, 999078, Macao, Macao Special Administrative Region of China, China
- School of Mathematics and Physics, Hechi University, No. 42, Longjiang, Hechi, 546300, Guangxi, China
| | - Yong Liang
- Faculty of Innovation Enginee, Macau University of Science and Technology, Avenida Wai Long, Taipa, 999078, Macao, Macao Special Administrative Region of China, China.
- Peng Cheng Laboratory, Shenzhen, 518055, Guangdong, China.
| | - Le Li
- Faculty of Innovation Enginee, Macau University of Science and Technology, Avenida Wai Long, Taipa, 999078, Macao, Macao Special Administrative Region of China, China
| | - Rui Miao
- Basic Teaching Department, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519041, Guangdong, China
| | - Shuilin Liao
- Faculty of Innovation Enginee, Macau University of Science and Technology, Avenida Wai Long, Taipa, 999078, Macao, Macao Special Administrative Region of China, China
| | - Yongfu Zou
- School of Mathematics and Physics, Hechi University, No. 42, Longjiang, Hechi, 546300, Guangxi, China
| | - Chengjun Yang
- School of Artificial Intelligence and Manufacturing, Hechi University, No. 42, Longjiang, Hechi, 546300, Guangxi, China
| | - Dong Ouyang
- School of Biomedical Engineering, Guangdong Medical University, No. 1, Xincheng, Zhanjiang, 523808, Guangdong, China
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Wang L, Wang Y, Xuan C, Zhang B, Wu H, Gao J. Predicting potential microbe-disease associations based on multi-source features and deep learning. Brief Bioinform 2023; 24:bbad255. [PMID: 37406190 DOI: 10.1093/bib/bbad255] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/30/2023] [Accepted: 06/20/2023] [Indexed: 07/07/2023] Open
Abstract
Studies have confirmed that the occurrence of many complex diseases in the human body is closely related to the microbial community, and microbes can affect tumorigenesis and metastasis by regulating the tumor microenvironment. However, there are still large gaps in the clinical observation of the microbiota in disease. Although biological experiments are accurate in identifying disease-associated microbes, they are also time-consuming and expensive. The computational models for effective identification of diseases related microbes can shorten this process, and reduce capital and time costs. Based on this, in the paper, a model named DSAE_RF is presented to predict latent microbe-disease associations by combining multi-source features and deep learning. DSAE_RF calculates four similarities between microbes and diseases, which are then used as feature vectors for the disease-microbe pairs. Later, reliable negative samples are screened by k-means clustering, and a deep sparse autoencoder neural network is further used to extract effective features of the disease-microbe pairs. In this foundation, a random forest classifier is presented to predict the associations between microbes and diseases. To assess the performance of the model in this paper, 10-fold cross-validation is implemented on the same dataset. As a result, the AUC and AUPR of the model are 0.9448 and 0.9431, respectively. Furthermore, we also conduct a variety of experiments, including comparison of negative sample selection methods, comparison with different models and classifiers, Kolmogorov-Smirnov test and t-test, ablation experiments, robustness analysis, and case studies on Covid-19 and colorectal cancer. The results fully demonstrate the reliability and availability of our model.
Collapse
Affiliation(s)
- Liugen Wang
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Yan Wang
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Chenxu Xuan
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Bai Zhang
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Hanwen Wu
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Jie Gao
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China
| |
Collapse
|
8
|
Xiang H, Guo R, Liu L, Guo T, Huang Q. MSIF-LNP: microbial and human health association prediction based on matrix factorization noise reduction for similarity fusion and bidirectional linear neighborhood label propagation. Front Microbiol 2023; 14:1216811. [PMID: 37389340 PMCID: PMC10303805 DOI: 10.3389/fmicb.2023.1216811] [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: 05/04/2023] [Accepted: 05/25/2023] [Indexed: 07/01/2023] Open
Abstract
Studies have shown that microbes are closely related to human health. Clarifying the relationship between microbes and diseases that cause health problems can provide new solutions for the treatment, diagnosis, and prevention of diseases, and provide strong protection for human health. Currently, more and more similarity fusion methods are available to predict potential microbe-disease associations. However, existing methods have noise problems in the process of similarity fusion. To address this issue, we propose a method called MSIF-LNP that can efficiently and accurately identify potential connections between microbes and diseases, and thus clarify the relationship between microbes and human health. This method is based on matrix factorization denoising similarity fusion (MSIF) and bidirectional linear neighborhood propagation (LNP) techniques. First, we use non-linear iterative fusion to obtain a similarity network for microbes and diseases by fusing the initial microbe and disease similarities, and then reduce noise by using matrix factorization. Next, we use the initial microbe-disease association pairs as label information to perform linear neighborhood label propagation on the denoised similarity network of microbes and diseases. This enables us to obtain a score matrix for predicting microbe-disease relationships. We evaluate the predictive performance of MSIF-LNP and seven other advanced methods through 10-fold cross-validation, and the experimental results show that MSIF-LNP outperformed the other seven methods in terms of AUC. In addition, the analysis of Cystic fibrosis and Obesity cases further demonstrate the predictive ability of this method in practical applications.
Collapse
Affiliation(s)
- Hui Xiang
- College of Physical Education, Southwest Forestry University, Kunming, Yunnan, China
| | - Rong Guo
- College of Physical Education, Southwest Forestry University, Kunming, Yunnan, China
| | - Li Liu
- College of Physical Education, Suzhou University, Suzhou, Anhui, China
| | - Tengjie Guo
- College of Physical Education, Yunnan Normal University, Kunming, Yunnan, China
| | - Quan Huang
- College of Physical Education, Southwest Forestry University, Kunming, Yunnan, China
| |
Collapse
|
9
|
Shokri Garjan H, Omidi Y, Poursheikhali Asghari M, Ferdousi R. In-silico computational approaches to study microbiota impacts on diseases and pharmacotherapy. Gut Pathog 2023; 15:10. [PMID: 36882861 PMCID: PMC9990230 DOI: 10.1186/s13099-023-00535-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 02/21/2023] [Indexed: 03/09/2023] Open
Abstract
Microorganisms have been linked to a variety of critical human disease, thanks to advances in sequencing technology and microbiology. The growing recognition of human microbe-disease relationships provides crucial insights into the underlying disease process from the perspective of pathogens, which is extremely useful for pathogenesis research, early diagnosis, and precision medicine and therapy. Microbe-based analysis in terms of diseases and related drug discovery can predict new connections/mechanisms and provide new concepts. These phenomena have been studied via various in-silico computational approaches. This review aims to elaborate on the computational works conducted on the microbe-disease and microbe-drug topics, discuss the computational model approaches used for predicting associations and provide comprehensive information on the related databases. Finally, we discussed potential prospects and obstacles in this field of study, while also outlining some recommendations for further enhancing predictive capabilities.
Collapse
Affiliation(s)
- Hassan Shokri Garjan
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Yadollah Omidi
- Department of Pharmaceutical Sciences, Nova Southeastern University, College of Pharmacy, Fort Lauderdale, FL, USA
| | | | - Reza Ferdousi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
| |
Collapse
|
10
|
Shi K, Li L, Wang Z, Chen H, Chen Z, Fang S. Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy. Front Neurosci 2023; 16:1124315. [PMID: 36741060 PMCID: PMC9892757 DOI: 10.3389/fnins.2022.1124315] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 12/31/2022] [Indexed: 01/20/2023] Open
Abstract
The interactions between the microbiota and the human host can affect the physiological functions of organs (such as the brain, liver, gut, etc.). Accumulating investigations indicate that the imbalance of microbial community is closely related to the occurrence and development of diseases. Thus, the identification of potential links between microbes and diseases can provide insight into the pathogenesis of diseases. In this study, we propose a deep learning framework (MDAGCAN) based on graph convolutional attention network to identify potential microbe-disease associations. In MDAGCAN, we first construct a heterogeneous network consisting of the known microbe-disease associations and multi-similarity fusion networks of microbes and diseases. Then, the node embeddings considering the neighbor information of the heterogeneous network are learned by applying graph convolutional layers and graph attention layers. Finally, a bilinear decoder using node embedding representations reconstructs the unknown microbe-disease association. Experiments show that our method achieves reliable performance with average AUCs of 0.9778 and 0.9454 ± 0.0038 in the frameworks of Leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. Furthermore, we apply MDAGCAN to predict latent microbes for two high-risk human diseases, i.e., liver cirrhosis and epilepsy, and results illustrate that 16 and 17 out of the top 20 predicted microbes are verified by published literatures, respectively. In conclusion, our method displays effective and reliable prediction performance and can be expected to predict unknown microbe-disease associations facilitating disease diagnosis and prevention.
Collapse
Affiliation(s)
- Kai Shi
- College of Information Science and Engineering, Guilin University of Technology, Guilin, China
- Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, China
| | - Lin Li
- College of Information Science and Engineering, Guilin University of Technology, Guilin, China
| | - Zhengfeng Wang
- College of Information Science and Engineering, Guilin University of Technology, Guilin, China
| | - Huazhou Chen
- College of Science, Guilin University of Technology, Guilin, China
| | - Zilin Chen
- Department of Developmental and Behavioural Pediatric Department & Department of Child Primary Care, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuanfeng Fang
- Department of Children Health Care, Children’s Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| |
Collapse
|
11
|
Liu H, Bing P, Zhang M, Tian G, Ma J, Li H, Bao M, He K, He J, He B, Yang J. MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm. Comput Struct Biotechnol J 2023; 21:1414-1423. [PMID: 36824227 PMCID: PMC9941872 DOI: 10.1016/j.csbj.2022.12.053] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 12/29/2022] [Accepted: 12/30/2022] [Indexed: 01/03/2023] Open
Abstract
Identifying the potential associations between microbes and diseases is the first step for revealing the pathological mechanisms of microbe-associated diseases. However, traditional culture-based microbial experiments are expensive and time-consuming. Thus, it is critical to prioritize disease-associated microbes by computational methods for further experimental validation. In this study, we proposed a novel method called MNNMDA, to predict microbe-disease associations (MDAs) by applying a Matrix Nuclear Norm method into known microbe and disease data. Specifically, we first calculated Gaussian interaction profile kernel similarity and functional similarity for diseases and microbes. Then we constructed a heterogeneous information network by combining the integrated disease similarity network, the integrated microbe similarity network and the known microbe-disease bipartite network. Finally, we formulated the microbe-disease association prediction problem as a low-rank matrix completion problem, which was solved by minimizing the nuclear norm of a matrix with a few regularization terms. We tested the performances of MNNMDA in three datasets including HMDAD, Disbiome, and Combined Data with small, medium and large sizes respectively. We also compared MNNMDA with 5 state-of-the-art methods including KATZHMDA, LRLSHMDA, NTSHMDA, GATMDA, and KGNMDA, respectively. MNNMDA achieved area under the ROC curves (AUROC) of 0.9536 and 0.9364 respectively on HDMAD and Disbiome, better than the AUCs of compared methods under the 5-fold cross-validation for all microbe-disease associations. It also obtained a relatively good performance with AUROC 0.8858 in the combined data. In addition, MNNMDA was also better than other methods in area under precision and recall curve (AUPR) under the 5-fold cross-validation for all associations, and in both AUROC and AUPR under the 5-fold cross-validation for diseases and the 5-fold cross-validation for microbes. Finally, the case studies on colon cancer and inflammatory bowel disease (IBD) also validated the effectiveness of MNNMDA. In conclusion, MNNMDA is an effective method in predicting microbe-disease associations. Availability The codes and data for this paper are freely available at Github https://github.com/Haiyan-Liu666/MNNMDA.
Collapse
Affiliation(s)
- Haiyan Liu
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,College of Information Engineering, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China
| | - Pingping Bing
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China
| | - Meijun Zhang
- Geneis Beijing Co., Ltd., Beijing 100102, PR China
| | - Geng Tian
- Geneis Beijing Co., Ltd., Beijing 100102, PR China
| | - Jun Ma
- College of Information Engineering, Changsha Medical University, Changsha 410219, PR China
| | - Haigang Li
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China
| | - Meihua Bao
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China
| | - Kunhui He
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China
| | - Jianjun He
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China,Corresponding authors at: Academician Workstation, Changsha Medical University, Changsha 410219, PR China.
| | - Binsheng He
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China,Corresponding authors at: Academician Workstation, Changsha Medical University, Changsha 410219, PR China.
| | - Jialiang Yang
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,Geneis Beijing Co., Ltd., Beijing 100102, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China,Corresponding authors at: Academician Workstation, Changsha Medical University, Changsha 410219, PR China.
| |
Collapse
|
12
|
Li W, Wang S, Xu J, Xiang J. Inferring Latent MicroRNA-Disease Associations on a Gene-Mediated Tripartite Heterogeneous Multiplexing Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3190-3201. [PMID: 35041612 DOI: 10.1109/tcbb.2022.3143770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
MicroRNA (miRNA) is a class of non-coding single-stranded RNA molecules encoded by endogenous genes with a length of about 22 nucleotides. MiRNAs have been successfully identified as differentially expressed in various cancers. There is evidence that disorders of miRNAs are associated with a variety of complex diseases. Therefore, inferring potential miRNA-disease associations (MDAs) is very important for understanding the aetiology and pathogenesis of many diseases and is useful to disease diagnosis, prognosis and treatment. First, We creatively fused multiple similarity subnetworks from multi-sources for miRNAs, genes and diseases by multiplexing technology, respectively. Then, three multiplexed biological subnetworks are connected through the extended binary association to form a tripartite complete heterogeneous multiplexed network (Tri-HM). Finally, because the constructed Tri-HM network can retain subnetworks' original topology and biological functions and expands the binary association and dependence between the three biological entities, rich neighbourhood information is obtained iteratively from neighbours by a non-equilibrium random walk. Through cross-validation, our tri-HM-RWR model obtained an AUC value of 0.8657, and an AUPR value of 0.2139 in the global 5-fold cross-validation, which shows that our model can more fully speculate disease-related miRNAs.
Collapse
|
13
|
Miao Y, Zhang X, Chen S, Zhou W, Xu D, Shi X, Li J, Tu J, Yuan X, Lv K, Tian G. Identifying cancer tissue-of-origin by a novel machine learning method based on expression quantitative trait loci. Front Oncol 2022; 12:946552. [PMID: 36016607 PMCID: PMC9396384 DOI: 10.3389/fonc.2022.946552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 06/24/2022] [Indexed: 11/13/2022] Open
Abstract
Cancer of unknown primary (CUP) refers to cancer with primary lesion unidentifiable by regular pathological and clinical diagnostic methods. This kind of cancer is extremely difficult to treat, and patients with CUP usually have a very short survival time. Recent studies have suggested that cancer treatment targeting primary lesion will significantly improve the survival of CUP patients. Thus, it is critical to develop accurate yet fast methods to infer the tissue-of-origin (TOO) of CUP. In the past years, there are a few computational methods to infer TOO based on single omics data like gene expression, methylation, somatic mutation, and so on. However, the metastasis of tumor involves the interaction of multiple levels of biological molecules. In this study, we developed a novel computational method to predict TOO of CUP patients by explicitly integrating expression quantitative trait loci (eQTL) into an XGBoost classification model. We trained our model with The Cancer Genome Atlas (TCGA) data involving over 7,000 samples across 20 types of solid tumors. In the 10-fold cross-validation, the prediction accuracy of the model with eQTL was over 0.96, better than that without eQTL. In addition, we also tested our model in an independent data downloaded from Gene Expression Omnibus (GEO) consisting of 87 samples across 4 cancer types. The model also achieved an f1-score of 0.7-1 depending on different cancer types. In summary, eQTL was an important information in inferring cancer TOO and the model might be applied in clinical routine test for CUP patients in the future.
Collapse
Affiliation(s)
- Yongchang Miao
- Gastroenterology Center, The Second People’s Hospital of Lianyungang, Lianyungang, China
- Lianyungang Clinical College of Xuzhou Medical University, Lianyungang, China
- The Second People’s Hospital of Lianyungang, Affiliated to Kangda College of Nanjing Medical University, Lianyungang, China
| | - Xueliang Zhang
- Fifth Division of Cancer, Jiamusi Cancer Hospital, Jiamusi, China
| | - Sijie Chen
- Department of Mathematics, Ocean University of China, Qingdao, China
| | - Wenjing Zhou
- Department of Oncology, Hiser Medical Center of Qingdao, Qingdao, China
| | - Dalai Xu
- Gastrointestinal Surgery, The Second People’s Hospital of Lianyungang, Lianyungang, China
| | - Xiaoli Shi
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jian Li
- Department of Mathematics, Ocean University of China, Qingdao, China
| | - Jinhui Tu
- Department of Mathematics, Ocean University of China, Qingdao, China
| | - Xuelian Yuan
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Kebo Lv
- Department of Mathematics, Ocean University of China, Qingdao, China
| | - Geng Tian
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
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.
Collapse
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,
| |
Collapse
|
16
|
Wang L, Li H, Wang Y, Tan Y, Chen Z, Pei T, Zou Q. MDADP: A webserver integrating database and prediction tools for microbe-disease associations. IEEE J Biomed Health Inform 2022; 26:3427-3434. [PMID: 35254998 DOI: 10.1109/jbhi.2022.3156166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
More and more evidence has demonstrated that microbiota play important roles in the life processes of the human body. In recent years, various computational methods have been proposed for identifying potentially disease-associated microbes to save costs in traditional biological experiments. However, prediction performances of these methods are generally limited by outdated and incomplete datasets. And moreover, until now, there are limited studies that can provide visual predictive tools for inferring possible microbe-disease associations (MDAs) as well. Hence, in this manuscript, a novel webserver called MDADP will be proposed to identify latent MDAs, in which, a new MDA database together with interactive prediction tools for MDAs studies will be designed simultaneously. Especially, in the newly constructed MDA database, 2019 known MDAs between 58 diseases and 703 microbes have been manually collected first. And then, through adopting the average ranking method and the co-confidence method respectively, eight representative computational models have been integrated together to identify potential disease-related microbes. As a result, MDADP can provide not only interactive features for users to access and capture MDAs entities, but also effective tools for users to identify candidate microbes for different diseases. To our knowledge, MDADP is the first online platform that incorporates a new MDA database with comprehensive MDA prediction tools. Therefore, we believe that it will be a valuable source of information for researches in microbiology and disease-related fields. MDADP can be accessed at http://mdadp.leelab2997.cn.
Collapse
|
17
|
Yang X, Kuang L, Chen Z, Wang L. Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe-Disease Associations. Front Genet 2021; 12:754425. [PMID: 34721543 PMCID: PMC8551558 DOI: 10.3389/fgene.2021.754425] [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: 08/06/2021] [Accepted: 09/21/2021] [Indexed: 11/13/2022] Open
Abstract
Accumulating studies have shown that microbes are closely related to human diseases. In this paper, a novel method called MSBMFHMDA was designed to predict potential microbe-disease associations by adopting multi-similarities bilinear matrix factorization. In MSBMFHMDA, a microbe multiple similarities matrix was constructed first based on the Gaussian interaction profile kernel similarity and cosine similarity for microbes. Then, we use the Gaussian interaction profile kernel similarity, cosine similarity, and symptom similarity for diseases to compose the disease multiple similarities matrix. Finally, we integrate these two similarity matrices and the microbe-disease association matrix into our model to predict potential associations. The results indicate that our method can achieve reliable AUCs of 0.9186 and 0.9043 ± 0.0048 in the framework of leave-one-out cross validation (LOOCV) and fivefold cross validation, respectively. What is more, experimental results indicated that there are 10, 10, and 8 out of the top 10 related microbes for asthma, inflammatory bowel disease, and type 2 diabetes mellitus, respectively, which were confirmed by experiments and literatures. Therefore, our model has favorable performance in predicting potential microbe-disease associations.
Collapse
Affiliation(s)
- Xiaoyu Yang
- Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China.,College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| | - Linai Kuang
- Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China
| | - Zhiping Chen
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| | - Lei Wang
- Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China.,College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| |
Collapse
|
18
|
Li H, Wang Y, Zhang Z, Tan Y, Chen Z, Wang X, Pei T, Wang L. Identifying Microbe-Disease Association Based on a Novel Back-Propagation Neural Network Model. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2502-2513. [PMID: 32305935 DOI: 10.1109/tcbb.2020.2986459] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Over the years, numerous evidences have demonstrated that microbes living in the human body are closely related to human life activities and human diseases. However, traditional biological experiments are time-consuming and expensive, so it has become a research topic in bioinformatics to predict potential microbe-disease associations by adopting computational methods. In this study, a novel calculative method called BPNNHMDA is proposed to identify potential microbe-disease associations. In BPNNHMDA, a novel neural network model is first designed to infer potential microbe-disease associations, its input signal is a matrix of known microbe-disease associations, and its output signal is matrix of potential microbe-disease associations probabilities. And moreover, in the novel neural network model, a new activation function is designed to activate the hidden layer and the output layer based on the hyperbolic tangent function, and its initial connection weights are optimized by adopting Gaussian Interaction Profile kernel (GIP) similarity for microbes, which can improve the training speed of BPNNHMDA efficiently. Finally, in order to verify the performance of our prediction model, different frameworks such as the Leave-One-Out Cross Validation (LOOCV) and k-Fold Cross Validation ( k-Fold CV) are implemented on BPNNHMDA respectively. Simulation results illustrate that BPNNHMDA can achieve reliable AUCs of 0.9242, 0.9127 ± 0.0009 and 0.8955 ± 0.0018 in LOOCV, 5-Fold CV and 2-Fold CV separately, which are superior to previous state-of-the-art methods. Furthermore, case studies of inflammatory bowel disease (IBD), asthma and obesity demonstrate that BPNNHMDA has excellent prediction ability in practical applications as well.
Collapse
|
19
|
Wang J, Wang C, Shen L, Zhou L, Peng L. Screening Potential Drugs for COVID-19 Based on Bound Nuclear Norm Regularization. Front Genet 2021; 12:749256. [PMID: 34691157 PMCID: PMC8529063 DOI: 10.3389/fgene.2021.749256] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 08/23/2021] [Indexed: 01/04/2023] Open
Abstract
The novel coronavirus pneumonia COVID-19 infected by SARS-CoV-2 has attracted worldwide attention. It is urgent to find effective therapeutic strategies for stopping COVID-19. In this study, a Bounded Nuclear Norm Regularization (BNNR) method is developed to predict anti-SARS-CoV-2 drug candidates. First, three virus-drug association datasets are compiled. Second, a heterogeneous virus-drug network is constructed. Third, complete genomic sequences and Gaussian association profiles are integrated to compute virus similarities; chemical structures and Gaussian association profiles are integrated to calculate drug similarities. Fourth, a BNNR model based on kernel similarity (VDA-GBNNR) is proposed to predict possible anti-SARS-CoV-2 drugs. VDA-GBNNR is compared with four existing advanced methods under fivefold cross-validation. The results show that VDA-GBNNR computes better AUCs of 0.8965, 0.8562, and 0.8803 on the three datasets, respectively. There are 6 anti-SARS-CoV-2 drugs overlapping in any two datasets, that is, remdesivir, favipiravir, ribavirin, mycophenolic acid, niclosamide, and mizoribine. Molecular dockings are conducted for the 6 small molecules and the junction of SARS-CoV-2 spike protein and human angiotensin-converting enzyme 2. In particular, niclosamide and mizoribine show higher binding energy of −8.06 and −7.06 kcal/mol with the junction, respectively. G496 and K353 may be potential key residues between anti-SARS-CoV-2 drugs and the interface junction. We hope that the predicted results can contribute to the treatment of COVID-19.
Collapse
Affiliation(s)
- Juanjuan Wang
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Chang Wang
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Ling Shen
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China.,College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, China
| |
Collapse
|
20
|
Yang H, Tong F, Qi C, Wang P, Li J, Cheng L. Prioritizing Disease-Related Microbes Based on the Topological Properties of a Comprehensive Network. Front Microbiol 2021; 12:685549. [PMID: 34326821 PMCID: PMC8315281 DOI: 10.3389/fmicb.2021.685549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 05/10/2021] [Indexed: 01/09/2023] Open
Abstract
Many microbes are parasitic within the human body, engaging in various physiological processes and playing an important role in human diseases. The discovery of new microbe-disease associations aids our understanding of disease pathogenesis. Computational methods can be applied in such investigations, thereby avoiding the time-consuming and laborious nature of experimental methods. In this study, we constructed a comprehensive microbe-disease network by integrating known microbe-disease associations from three large-scale databases (Peryton, Disbiome, and gutMDisorder), and extended the random walk with restart to the network for prioritizing unknown microbe-disease associations. The area under the curve values of the leave-one-out cross-validation and the fivefold cross-validation exceeded 0.9370 and 0.9366, respectively, indicating the high performance of this method. Despite being widely studied diseases, in case studies of inflammatory bowel disease, asthma, and obesity, some prioritized disease-related microbes were validated by recent literature. This suggested that our method is effective at prioritizing novel disease-related microbes and may offer further insight into disease pathogenesis.
Collapse
Affiliation(s)
- Haixiu Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Fan Tong
- Academy of Military Medical Science, Beijing, China
| | - Changlu Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ping Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jiangyu Li
- Academy of Military Medical Science, Beijing, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Harbin Medical University, Harbin, China
| |
Collapse
|
21
|
Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model. Sci Rep 2021; 11:4490. [PMID: 33627732 PMCID: PMC7904816 DOI: 10.1038/s41598-021-83966-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 02/08/2021] [Indexed: 02/07/2023] Open
Abstract
With recent advances in biotechnology and sequencing technology, the microbial community has been intensively studied and discovered to be associated with many chronic as well as acute diseases. Even though a tremendous number of studies describing the association between microbes and diseases have been published, text mining methods that focus on such associations have been rarely studied. We propose a framework that combines machine learning and natural language processing methods to analyze the association between microbes and diseases. A hierarchical long short-term memory network was used to detect sentences that describe the association. For the sentences determined, two different parse tree-based search methods were combined to find the relation-describing word. The ensemble model of constituency parsing for structural pattern matching and dependency-based relation extraction improved the prediction accuracy. By combining deep learning and parse tree-based extractions, our proposed framework could extract the microbe-disease association with higher accuracy. The evaluation results showed that our system achieved an F-score of 0.8764 and 0.8524 in binary decisions and extracting relation words, respectively. As a case study, we performed a large-scale analysis of the association between microbes and diseases. Additionally, a set of common microbes shared by multiple diseases were also identified in this study. This study could provide valuable information for the major microbes that were studied for a specific disease. The code and data are available at https://github.com/DMnBI/mdi_predictor .
Collapse
|
22
|
Xie G, Huang B, Sun Y, Wu C, Han Y. RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation. Mol Genet Genomics 2021; 296:473-483. [PMID: 33590345 DOI: 10.1007/s00438-021-01764-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Accepted: 01/28/2021] [Indexed: 12/13/2022]
Abstract
An increasing number of studies and experiments have demonstrated that long noncoding RNAs (lncRNAs) have a massive impact on various biological processes. Predicting potential associations between lncRNAs and diseases not only can improve our understanding of the molecular mechanisms of human diseases but also can facilitate the identification of biomarkers for disease diagnosis, treatment, and prevention. However, identifying such associations through experiments is costly and demanding, thereby prompting researchers to develop computational methods to complement these experiments. In this paper, we constructed a novel model called RWSF-BLP (a novel lncRNA-disease association prediction model using Random Walk-based multi-Similarity Fusion and Bidirectional Label Propagation), which applies an efficient random walk-based multi-similarity fusion (RWSF) method to fuse different similarity matrices and utilizes bidirectional label propagation to predict potential lncRNA-disease associations. Leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold-CV) were implemented in the evaluation RWSF-BLP performance. Results showed that, RWSF-BLP has reliable AUCs of 0.9086 and 0.9115 ± 0.0044 under the framework of LOOCV and 5-fold-CV and outperformed other four canonical methods. Case studies on lung cancer and leukemia demonstrated that potential lncRNA-disease associations can be predicted through our method. Therefore, our method can accurately infer potential lncRNA-disease associations and may be a good choice in future biomedical research.
Collapse
Affiliation(s)
- Guobo Xie
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Bin Huang
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Yuping Sun
- School of Computer Science, Guangdong University of Technology, Guangzhou, China.
| | - Changhai Wu
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Yuqiong Han
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| |
Collapse
|
23
|
Xu D, Xu H, Zhang Y, Wang M, Chen W, Gao R. MDAKRLS: Predicting human microbe-disease association based on Kronecker regularized least squares and similarities. J Transl Med 2021; 19:66. [PMID: 33579301 PMCID: PMC7881563 DOI: 10.1186/s12967-021-02732-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 02/01/2021] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Microbes are closely related to human health and diseases. Identification of disease-related microbes is of great significance for revealing the pathological mechanism of human diseases and understanding the interaction mechanisms between microbes and humans, which is also useful for the prevention, diagnosis and treatment of human diseases. Considering the known disease-related microbes are still insufficient, it is necessary to develop effective computational methods and reduce the time and cost of biological experiments. METHODS In this work, we developed a novel computational method called MDAKRLS to discover potential microbe-disease associations (MDAs) based on the Kronecker regularized least squares. Specifically, we introduced the Hamming interaction profile similarity to measure the similarities of microbes and diseases besides Gaussian interaction profile kernel similarity. In addition, we introduced the Kronecker product to construct two kinds of Kronecker similarities between microbe-disease pairs. Then, we designed the Kronecker regularized least squares with different Kronecker similarities to obtain prediction scores, respectively, and calculated the final prediction scores by integrating the contributions of different similarities. RESULTS The AUCs value of global leave-one-out cross-validation and 5-fold cross-validation achieved by MDAKRLS were 0.9327 and 0.9023 ± 0.0015, which were significantly higher than five state-of-the-art methods used for comparison. Comparison results demonstrate that MDAKRLS has faster computing speed under two kinds of frameworks. In addition, case studies of inflammatory bowel disease (IBD) and asthma further showed 19 (IBD), 19 (asthma) of the top 20 prediction disease-related microbes could be verified by previously published biological or medical literature. CONCLUSIONS All the evaluation results adequately demonstrated that MDAKRLS has an effective and reliable prediction performance. It may be a useful tool to seek disease-related new microbes and help biomedical researchers to carry out follow-up studies.
Collapse
Affiliation(s)
- Da Xu
- School of Mathematics and Statistics, Shandong University, Weihai, 264209, China
| | - Hanxiao Xu
- School of Mathematics and Statistics, Shandong University, Weihai, 264209, China
| | - Yusen Zhang
- School of Mathematics and Statistics, Shandong University, Weihai, 264209, China.
| | - Mingyi Wang
- Department of Central Lab, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong, China.
| | - Wei Chen
- School of Mathematics and Statistics, Shandong University, Weihai, 264209, China
| | - Rui Gao
- School of Control Science and Engineering, Shandong University, Jinan, 250061, China
| |
Collapse
|
24
|
Abstract
Diagnostic processes typically rely on traditional and laborious methods, that are prone to human error, resulting in frequent misdiagnosis of diseases. Computational approaches are being increasingly used for more precise diagnosis of the clinical pathology, diagnosis of genetic and microbial diseases, and analysis of clinical chemistry data. These approaches are progressively used for improving the reliability of testing, resulting in reduced diagnostic errors. Artificial intelligence (AI)-based computational approaches mostly rely on training sets obtained from patient data stored in clinical databases. However, the use of AI is associated with several ethical issues, including patient privacy and data ownership. The capacity of AI-based mathematical models to interpret complex clinical data frequently leads to data bias and reporting of erroneous results based on patient data. In order to improve the reliability of computational approaches in clinical diagnostics, strategies to reduce data bias and analyzing real-life patient data need to be further refined.
Collapse
Affiliation(s)
- Mohammed A Alaidarous
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah, Kingdom of Saudi Arabia. E-mail.
| |
Collapse
|
25
|
Zhao Y, Wang CC, Chen X. Microbes and complex diseases: from experimental results to computational models. Brief Bioinform 2020; 22:5882184. [PMID: 32766753 DOI: 10.1093/bib/bbaa158] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 06/19/2020] [Accepted: 06/22/2020] [Indexed: 12/13/2022] Open
Abstract
Studies have shown that the number of microbes in humans is almost 10 times that of cells. These microbes have been proven to play an important role in a variety of physiological processes, such as enhancing immunity, improving the digestion of gastrointestinal tract and strengthening metabolic function. In addition, in recent years, more and more research results have indicated that there are close relationships between the emergence of the human noncommunicable diseases and microbes, which provides a novel insight for us to further understand the pathogenesis of the diseases. An in-depth study about the relationships between diseases and microbes will not only contribute to exploring new strategies for the diagnosis and treatment of diseases but also significantly heighten the efficiency of new drugs development. However, applying the methods of biological experimentation to reveal the microbe-disease associations is costly and inefficient. In recent years, more and more researchers have constructed multiple computational models to predict microbes that are potentially associated with diseases. Here, we start with a brief introduction of microbes and databases as well as web servers related to them. Then, we mainly introduce four kinds of computational models, including score function-based models, network algorithm-based models, machine learning-based models and experimental analysis-based models. Finally, we summarize the advantages as well as disadvantages of them and set the direction for the future work of revealing microbe-disease associations based on computational models. We firmly believe that computational models are expected to be important tools in large-scale predictions of disease-related microbes.
Collapse
Affiliation(s)
- Yan Zhao
- School of Information and Control Engineering, China University of Mining
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining
| |
Collapse
|
26
|
Wen Z, Yan C, Duan G, Li S, Wu FX, Wang J. A survey on predicting microbe-disease associations: biological data and computational methods. Brief Bioinform 2020; 22:5881365. [PMID: 34020541 DOI: 10.1093/bib/bbaa157] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 06/18/2020] [Accepted: 06/22/2020] [Indexed: 02/06/2023] Open
Abstract
Various microbes have proved to be closely related to the pathogenesis of human diseases. While many computational methods for predicting human microbe-disease associations (MDAs) have been developed, few systematic reviews on these methods have been reported. In this study, we provide a comprehensive overview of the existing methods. Firstly, we introduce the data used in existing MDA prediction methods. Secondly, we classify those methods into different categories by their nature and describe their algorithms and strategies in detail. Next, experimental evaluations are conducted on representative methods using different similarity data and calculation methods to compare their prediction performances. Based on the principles of computational methods and experimental results, we discuss the advantages and disadvantages of those methods and propose suggestions for the improvement of prediction performances. Considering the problems of the MDA prediction at present stage, we discuss future work from three perspectives including data, methods and formulations at the end.
Collapse
Affiliation(s)
- Zhongqi Wen
- Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering at Central South University, Hunan, China
| | - Cheng Yan
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Guihua Duan
- School of Computer Science and Engineering, Central South University
| | - Suning Li
- Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Fang-Xiang Wu
- College of Engineering and the Department of Computer Sciences, University of Saskatchewan, Saskatoon, Canada
| | - Jianxin Wang
- Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering at Central South University, Hunan, China
| |
Collapse
|
27
|
Fan Y, Chen M, Zhu Q, Wang W. Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection. Front Bioeng Biotechnol 2020; 8:831. [PMID: 32850711 PMCID: PMC7418576 DOI: 10.3389/fbioe.2020.00831] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 06/29/2020] [Indexed: 12/18/2022] Open
Abstract
Plenty of microbes in our human body play a vital role in the process of cell physiology. In recent years, there is accumulating evidence indicating that microbes are closely related to many complex human diseases. In-depth investigation of disease-associated microbes can contribute to understanding the pathogenesis of diseases and thus provide novel strategies for the treatment, diagnosis, and prevention of diseases. To date, many computational models have been proposed for predicting microbe-disease associations using available similarity networks. However, these similarity networks are not effectively fused. In this study, we proposed a novel computational model based on multi-data integration and network consistency projection for Human Microbe-Disease Associations Prediction (HMDA-Pred), which fuses multiple similarity networks by a linear network fusion method. HMDA-Pred yielded AUC values of 0.9589 and 0.9361 ± 0.0037 in the experiments of leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. Furthermore, in case studies, 10, 8, and 10 out of the top 10 predicted microbes of asthma, colon cancer, and inflammatory bowel disease were confirmed by the literatures, respectively.
Collapse
Affiliation(s)
- Yongxian Fan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
| | | | | | | |
Collapse
|