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Luo H, Yin W, Wang J, Zhang G, Liang W, Luo J, Yan C. Drug-drug interactions prediction based on deep learning and knowledge graph: A review. iScience 2024; 27:109148. [PMID: 38405609 PMCID: PMC10884936 DOI: 10.1016/j.isci.2024.109148] [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] [Indexed: 02/27/2024] Open
Abstract
Drug-drug interactions (DDIs) can produce unpredictable pharmacological effects and lead to adverse events that have the potential to cause irreversible damage to the organism. Traditional methods to detect DDIs through biological or pharmacological analysis are time-consuming and expensive, therefore, there is an urgent need to develop computational methods to effectively predict drug-drug interactions. Currently, deep learning and knowledge graph techniques which can effectively extract features of entities have been widely utilized to develop DDI prediction methods. In this research, we aim to systematically review DDI prediction researches applying deep learning and graph knowledge. The available biomedical data and public databases related to drugs are firstly summarized in this review. Then, we discuss the existing drug-drug interactions prediction methods which have utilized deep learning and knowledge graph techniques and group them into three main classes: deep learning-based methods, knowledge graph-based methods, and methods that combine deep learning with knowledge graph. We comprehensively analyze the commonly used drug related data and various DDI prediction methods, and compare these prediction methods on benchmark datasets. Finally, we briefly discuss the challenges related to drug-drug interactions prediction, including asymmetric DDIs prediction and high-order DDI prediction.
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Affiliation(s)
- Huimin Luo
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China
| | - Weijie Yin
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Jianlin Wang
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Academy for Advanced Interdisciplinary Studies, Zhengzhou, China
| | - Ge Zhang
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China
| | - Wenjuan Liang
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Junwei Luo
- College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China
| | - Chaokun Yan
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Academy for Advanced Interdisciplinary Studies, Zhengzhou, China
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2
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Liu H, Peng W, Dai W, Lin J, Fu X, Liu L, Liu L, Yu N. Improving anti-cancer drug response prediction using multi-task learning on graph convolutional networks. Methods 2024; 222:41-50. [PMID: 38157919 DOI: 10.1016/j.ymeth.2023.11.018] [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: 07/21/2023] [Revised: 09/19/2023] [Accepted: 11/19/2023] [Indexed: 01/03/2024] Open
Abstract
Predicting the therapeutic effect of anti-cancer drugs on tumors based on the characteristics of tumors and patients is one of the important contents of precision oncology. Existing computational methods regard the drug response prediction problem as a classification or regression task. However, few of them consider leveraging the relationship between the two tasks. In this work, we propose a Multi-task Interaction Graph Convolutional Network (MTIGCN) for anti-cancer drug response prediction. MTIGCN first utilizes an graph convolutional network-based model to produce embeddings for both cell lines and drugs. After that, the model employs multi-task learning to predict anti-cancer drug response, which involves training the model on three different tasks simultaneously: the main task of the drug sensitive or resistant classification task and the two auxiliary tasks of regression prediction and similarity network reconstruction. By sharing parameters and optimizing the losses of different tasks simultaneously, MTIGCN enhances the feature representation and reduces overfitting. The results of the experiments on two in vitro datasets demonstrated that MTIGCN outperformed seven state-of-the-art baseline methods. Moreover, the well-trained model on the in vitro dataset GDSC exhibited good performance when applied to predict drug responses in in vivo datasets PDX and TCGA. The case study confirmed the model's ability to discover unknown drug responses in cell lines.
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Affiliation(s)
- Hancheng Liu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China
| | - Wei Peng
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China.
| | - Wei Dai
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China.
| | - Jiangzhen Lin
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China
| | - Xiaodong Fu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China
| | - Li Liu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China.
| | - Lijun Liu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China
| | - Ning Yu
- State University of New York, The College at Brockport, Department of Computing Sciences, 350 New Campus Drive, Brockport NY 14422.
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3
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Kumar R, Yadav G, Kuddus M, Ashraf GM, Singh R. Unlocking the microbial studies through computational approaches: how far have we reached? ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:48929-48947. [PMID: 36920617 PMCID: PMC10016191 DOI: 10.1007/s11356-023-26220-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 02/24/2023] [Indexed: 04/16/2023]
Abstract
The metagenomics approach accelerated the study of genetic information from uncultured microbes and complex microbial communities. In silico research also facilitated an understanding of protein-DNA interactions, protein-protein interactions, docking between proteins and phyto/biochemicals for drug design, and modeling of the 3D structure of proteins. These in silico approaches provided insight into analyzing pathogenic and nonpathogenic strains that helped in the identification of probable genes for vaccines and antimicrobial agents and comparing whole-genome sequences to microbial evolution. Artificial intelligence, more precisely machine learning (ML) and deep learning (DL), has proven to be a promising approach in the field of microbiology to handle, analyze, and utilize large data that are generated through nucleic acid sequencing and proteomics. This enabled the understanding of the functional and taxonomic diversity of microorganisms. ML and DL have been used in the prediction and forecasting of diseases and applied to trace environmental contaminants and environmental quality. This review presents an in-depth analysis of the recent application of silico approaches in microbial genomics, proteomics, functional diversity, vaccine development, and drug design.
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Affiliation(s)
- Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India
- Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, USA
| | - Garima Yadav
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India
| | - Mohammed Kuddus
- Department of Biochemistry, College of Medicine, University of Hail, Hail, Saudi Arabia
| | - Ghulam Md Ashraf
- Department of Medical Laboratory Sciences, College of Health Sciences, and Sharjah Institute for Medical Research, University of Sharjah, Sharjah , 27272, United Arab Emirates
| | - Rachana Singh
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India.
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4
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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.
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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.
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Cakmak A, Ayaz H, Arıkan S, Ibrahimzada AR, Demirkol Ş, Sönmez D, Hakan MT, Sürmen ST, Horozoğlu C, Doğan MB, Küçükhüseyin Ö, Cacına C, Kıran B, Zeybek Ü, Baysan M, Yaylım İ. Predicting the predisposition to colorectal cancer based on SNP profiles of immune phenotypes using supervised learning models. Med Biol Eng Comput 2023; 61:243-258. [PMID: 36357628 DOI: 10.1007/s11517-022-02707-9] [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: 04/21/2022] [Accepted: 10/22/2022] [Indexed: 11/12/2022]
Abstract
This study explores the machine learning-based assessment of predisposition to colorectal cancer based on single nucleotide polymorphisms (SNP). Such a computational approach may be used as a risk indicator and an auxiliary diagnosis method that complements the traditional methods such as biopsy and CT scan. Moreover, it may be used to develop a low-cost screening test for the early detection of colorectal cancers to improve public health. We employ several supervised classification algorithms. Besides, we apply data imputation to fill in the missing genotype values. The employed dataset includes SNPs observed in particular colorectal cancer-associated genomic loci that are located within DNA regions of 11 selected genes obtained from 115 individuals. We make the following observations: (i) random forest-based classifier using one-hot encoding and K-nearest neighbor (KNN)-based imputation performs the best among the studied classifiers with an F1 score of 89% and area under the curve (AUC) score of 0.96. (ii) One-hot encoding together with K-nearest neighbor-based data imputation increases the F1 scores by around 26% in comparison to the baseline approach which does not employ them. (iii) The proposed model outperforms a commonly employed state-of-the-art approach, ColonFlag, under all evaluated settings by up to 24% in terms of the AUC score. Based on the high accuracy of the constructed predictive models, the studied 11 genes may be considered a gene panel candidate for colon cancer risk screening.
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Affiliation(s)
- Ali Cakmak
- Department of Computer Engineering, Istanbul Technical University, Ayazaga Campus, Reşitpaşa, 34467, Sarıyer, Istanbul, Turkey.
| | | | - Soykan Arıkan
- Başakşehir Çam and Sakura City Hospital, Istanbul, Turkey
| | | | | | - Dilara Sönmez
- Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Turkey
| | - Mehmet T Hakan
- Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Turkey
| | - Saime T Sürmen
- Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Turkey
| | | | - Mehmet B Doğan
- Istanbul Research and Training Hospital, Istanbul, Turkey
| | - Özlem Küçükhüseyin
- Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Turkey
| | - Canan Cacına
- Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Turkey
| | | | - Ümit Zeybek
- Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Turkey
| | - Mehmet Baysan
- Department of Computer Engineering, Istanbul Technical University, Ayazaga Campus, Reşitpaşa, 34467, Sarıyer, Istanbul, Turkey
| | - İlhan Yaylım
- Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Turkey
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6
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Pharmacological Activities and Characterization of Phenolic and Flavonoid Compounds in Solenostemma argel Extract. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27238118. [PMID: 36500211 PMCID: PMC9741047 DOI: 10.3390/molecules27238118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/05/2022] [Accepted: 07/09/2022] [Indexed: 11/23/2022]
Abstract
Solenostemma argel is a desert medicinal plant indigenous to African countries. This research aims to study the pharmacological properties of Solenostemma argel plant. Aerial parts (leaves and flowers) of Solenostemma argel (Delile) Hayane were tested for antibacterial activity, antioxidant activity, anticancer, and anti-inflammatory activity. Phenolic and flavonoid contents of the plant were characterized. There was an increase in the antioxidant activity of Solenostemma argel extract from 12.16% to 94.37% by increasing concentration from10 µg/mL to 1280 µg/mL. The most sensitive organism was S. epidermidis with chloroform extract. The MTT assay revealed that methanolic extracts of Solenostemma argel showed potent cytotoxic effects on the A549, Caco-2, and MDAMB-231 cell lines, respectively. The anti-inflammatory activity increased by increasing the concentration of methanolic extract of Solenostemma argel, using indomethacin as a standard. Gallic acid was the most abundant phenolic acid, followed by synergic acid and p-coumaric acid, respectively. Catechin, quercetin, luteolin, kaempferol and rutin flavonoids were also found in the methanolic extract. GC-mass analysis showed that aerial parts of Solenostemma argel were rich in 2-(5-methyl-5 vinyl tetrahydro-2-furanyl)-2-propanol (11.63%), hexanoic acid methyl ester (10.93%), 3-dioxolane,4-methyl-2-pentadecyl (9.69%), phenol, 2-(1,1-dimethylethyl) (8.50%). It can be concluded that Solenostemma argel methanolic extract contain natural bioactive constituents with potential medicinal importance such as antioxidants, antimicrobial, anti-inflammatory, and anticancer activities.
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7
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Heterogeneous Question Answering Community Detection Based on Graph Neural Network. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.10.126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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8
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Wang Y, Lei X, Lu C, Pan Y. Predicting Microbe-Disease Association Based on Multiple Similarities and LINE Algorithm. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2399-2408. [PMID: 34014827 DOI: 10.1109/tcbb.2021.3082183] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Numerous microbes have been found to have vital impacts on human health through affecting biological processes. Therefore, exploring potential associations between microbes and diseases will promote the understanding and diagnosis of diseases. In this study, we present a novel computational model, named MSLINE, to infer potential microbe-disease associations by integrating Multiple Similarities and Large-scale Information Network Embedding (LINE) based on known associations. Specifically, on the basis of known microbe-disease associations from the Human Microbe-Disease Association Database, we first increase the known associations by collecting proven associations from existing literatures. We then construct a microbe-disease heterogeneous network (MDHN) by integrating known associations and multiple similarities (including Gaussian interaction profile kernel similarity, microbe function similarity, disease semantic similarity and disease-symptom similarity). After that, we implement random walk and LINE algorithm on MDHN to learn its structure information. Finally, we score the microbe-disease associations according to the structure information for every nodes. In the Leave-one-out cross validation and 5-fold cross validation, MSLINE performs better compared to other existing methods. Moreover, case studies of different diseases proved that MSLINE could predict the potential microbe-disease associations efficiently.
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9
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Chen Y, Lei X. Metapath Aggregated Graph Neural Network and Tripartite Heterogeneous Networks for Microbe-Disease Prediction. Front Microbiol 2022; 13:919380. [PMID: 35711758 PMCID: PMC9194683 DOI: 10.3389/fmicb.2022.919380] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 04/29/2022] [Indexed: 11/25/2022] Open
Abstract
More and more studies have shown that understanding microbe-disease associations cannot only reveal the pathogenesis of diseases, but also promote the diagnosis and prognosis of diseases. Because traditional medical experiments are time-consuming and expensive, many computational methods have been proposed in recent years to identify potential microbe-disease associations. In this study, we propose a method based on heterogeneous network and metapath aggregated graph neural network (MAGNN) to predict microbe-disease associations, called MATHNMDA. First, we introduce microbe-drug interactions, drug-disease associations, and microbe-disease associations to construct a microbe-drug-disease heterogeneous network. Then we take the heterogeneous network as input to MAGNN. Second, for each layer of MAGNN, we carry out intra-metapath aggregation with a multi-head attention mechanism to learn the structural and semantic information embedded in the target node context, the metapath-based neighbor nodes, and the context between them, by encoding the metapath instances under the metapath definition mode. We then use inter-metapath aggregation with an attention mechanism to combine the semantic information of all different metapaths. Third, we can get the final embedding of microbe nodes and disease nodes based on the output of the last layer in the MAGNN. Finally, we predict potential microbe-disease associations by reconstructing the microbe-disease association matrix. In addition, we evaluated the performance of MATHNMDA by comparing it with that of its variants, some state-of-the-art methods, and different datasets. The results suggest that MATHNMDA is an effective prediction method. The case studies on asthma, inflammatory bowel disease (IBD), and coronavirus disease 2019 (COVID-19) further validate the effectiveness of MATHNMDA.
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Affiliation(s)
- Yali Chen
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi'an, China
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10
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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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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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12
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Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0. BIG DATA AND COGNITIVE COMPUTING 2022; 6. [PMID: 35936510 PMCID: PMC9351549 DOI: 10.3390/bdcc6010027] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Literature-based discovery (LBD) summarizes information and generates insight from large text corpuses. The SemNet framework utilizes a large heterogeneous information network or “knowledge graph” of nodes and edges to compute relatedness and rank concepts pertinent to a user-specified target. SemNet provides a way to perform multi-factorial and multi-scalar analysis of complex disease etiology and therapeutic identification using the 33+ million articles in PubMed. The present work improves the efficacy and efficiency of LBD for end users by augmenting SemNet to create SemNet 2.0. A custom Python data structure replaced reliance on Neo4j to improve knowledge graph query times by several orders of magnitude. Additionally, two randomized algorithms were built to optimize the HeteSim metric calculation for computing metapath similarity. The unsupervised learning algorithm for rank aggregation (ULARA), which ranks concepts with respect to the user-specified target, was reconstructed using derived mathematical proofs of correctness and probabilistic performance guarantees for optimization. The upgraded ULARA is generalizable to other rank aggregation problems outside of SemNet. In summary, SemNet 2.0 is a comprehensive open-source software for significantly faster, more effective, and user-friendly means of automated biomedical LBD. An example case is performed to rank relationships between Alzheimer’s disease and metabolic co-morbidities.
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13
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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.
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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
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14
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Lei X, Mudiyanselage TB, Zhang Y, Bian C, Lan W, Yu N, Pan Y. A comprehensive survey on computational methods of non-coding RNA and disease association prediction. Brief Bioinform 2020; 22:6042241. [PMID: 33341893 DOI: 10.1093/bib/bbaa350] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/20/2020] [Accepted: 11/01/2020] [Indexed: 02/06/2023] Open
Abstract
The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types: network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.
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Affiliation(s)
- Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | | | - Yuchen Zhang
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Chen Bian
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Wei Lan
- School of Computer, Electronics and Information at Guangxi University, Nanning, China
| | - Ning Yu
- Department of Computing Sciences at the College at Brockport, State University of New York, Rochester, NY, USA
| | - Yi Pan
- Computer Science Department at Georgia State University, Atlanta, GA, USA
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15
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A Decision Support Method for Prehospital Emergency Care Based on Ranking the Importance of Physiological Variables. Healthcare (Basel) 2020; 8:healthcare8030295. [PMID: 32847006 PMCID: PMC7551753 DOI: 10.3390/healthcare8030295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 08/17/2020] [Accepted: 08/20/2020] [Indexed: 11/16/2022] Open
Abstract
To the on-site nursing staff or field management in prehospital emergency care, it seems baffling to conduct more targeted checklist tests for a specific disease. To address this problem, we proposed a decision support method for prehospital emergency care based on ranking the importance of physiological variables. We used multiple logistic regression models to explore the effects of various physiological variables on diseases based on the area under the curve (AUC) value. We implemented the method on the intensive care database (i.e., the Medical Information Mart for Intensive Care (MIMIC-III) database) and explored the importance of 17 physiological variables for 24 diseases, both chronic and acute. We included 33,798 adult patients, using the full physiological dataset as experiment data. We ranked the importance of the physiological variables related to the diseases according to the experiments’ AUC value. We discussed which physiological variables should be considered more important in adult intensive care units (ICUs) for prehospital emergency care conditions. We also discussed the relationships among the diseases based on ranking the importance of physiological variables. We used large-scale ICU patient data to obtain a cohort of physiological variables related to specific diseases. Ranking a cohort of physiological variables is a cost-effective means of reducing morbidity and mortality under prehospital emergency care conditions.
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16
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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.
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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
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17
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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.
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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
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18
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Lei X, Wang Y. Predicting Microbe-Disease Association by Learning Graph Representations and Rule-Based Inference on the Heterogeneous Network. Front Microbiol 2020; 11:579. [PMID: 32351464 PMCID: PMC7174569 DOI: 10.3389/fmicb.2020.00579] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 03/17/2020] [Indexed: 12/18/2022] Open
Abstract
More and more clinical observations have implied that microbes have great effects on human diseases. Understanding the relations between microbes and diseases are of profound significance for disease prevention and therapy. In this paper, we propose a predictive model based on the known microbe-disease associations to discover potential microbe-disease associations through integrating Learning Graph Representations and a modified Scoring mechanism on the Heterogeneous network (called LGRSH). Firstly, the similarity networks for microbe and disease are obtained based on the similarity of Gaussian interaction profile kernel. Then, we construct a heterogeneous network including these two similarity networks and microbe-disease associations' network. After that, the embedding algorithm Node2vec is implemented to learn representations of nodes in the heterogeneous network. Finally, according to these low-dimensional vector representations, we calculate the relevance between each microbe and disease by utilizing a modified rule-based inference method. By comparison with three other methods including LRLSHMDA, KATZHMDA and BiRWHMDA, LGRSH performs better than others. Moreover, in case studies of asthma, Chronic Obstructive Pulmonary Disease and Inflammatory Bowel Disease, there are 8, 8, and 10 out of the top-10 discovered disease-related microbes were validated respectively, demonstrating that LGRSH performs well in predicting potential microbe-disease associations.
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Affiliation(s)
- Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Yueyue Wang
- School of Computer Science, Shaanxi Normal University, Xi'an, China
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19
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Inferring Candidate CircRNA-Disease Associations by Bi-random Walk Based on CircRNA Regulatory Similarity. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7354774 DOI: 10.1007/978-3-030-53956-6_44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Identification of associations between circular RNAs (circRNA) and diseases has become a hot topic, which is beneficial for researchers to understand the disease mechanism. However, traditional biological experiments are expensive and time-consuming. In this study, we proposed a novel method named BWHCDA, which applied bi-random walk algorithm on the heterogeneous network for predicting circRNA-disease associations. First, circRNA regulatory similarity is measured based on circRNA-miRNA interactions, and circRNA similarity is calculated by the average of circRNA regulatory similarity and Gaussian interaction profiles (GIP) kernel similarity for circRNAs. Similarly, disease similarity is the mean of disease semantic similarity and GIP kernel similarity for diseases. Then, the heterogeneous network is constructed by integrating circRNA network, disease network via circRNA-disease associations. Subsequently, the bi-random walk algorithm is implemented on the heterogeneous network to predict circRNA-disease associations. Finally, we utilize leave-one-out cross validation and 10-fold cross validation frameworks to evaluate the prediction performance of BWHCDA method and obtain AUC of 0.9334 and 0.8764 ± 0.0038, respectively. Moreover, the predicted hsa_circ_0000519-gastric cancer association is analyzed. Results show that BWHCDA could be an effective resource for clinical experimental guidance.
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20
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Taxonomy dimension reduction for colorectal cancer prediction. Comput Biol Chem 2019; 83:107160. [DOI: 10.1016/j.compbiolchem.2019.107160] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 11/02/2019] [Accepted: 11/04/2019] [Indexed: 02/01/2023]
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21
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Qu K, Guo F, Liu X, Lin Y, Zou Q. Application of Machine Learning in Microbiology. Front Microbiol 2019; 10:827. [PMID: 31057526 PMCID: PMC6482238 DOI: 10.3389/fmicb.2019.00827] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 04/01/2019] [Indexed: 02/01/2023] Open
Abstract
Microorganisms are ubiquitous and closely related to people's daily lives. Since they were first discovered in the 19th century, researchers have shown great interest in microorganisms. People studied microorganisms through cultivation, but this method is expensive and time consuming. However, the cultivation method cannot keep a pace with the development of high-throughput sequencing technology. To deal with this problem, machine learning (ML) methods have been widely applied to the field of microbiology. Literature reviews have shown that ML can be used in many aspects of microbiology research, especially classification problems, and for exploring the interaction between microorganisms and the surrounding environment. In this study, we summarize the application of ML in microbiology.
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Affiliation(s)
- Kaiyang Qu
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Xiangrong Liu
- School of Information Science and Technology, Xiamen University, Xiamen, China
| | - Yuan Lin
- School of Information Science and Technology, Xiamen University, Xiamen, China
- Department of System Integration, Sparebanken Vest, Bergen, Norway
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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