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Maria NI, Rapicavoli RV, Alaimo S, Bischof E, Stasuzzo A, Broek JA, Pulvirenti A, Mishra B, Duits AJ, Ferro A. Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing. Heliyon 2023; 9:e14115. [PMID: 36911878 PMCID: PMC9986505 DOI: 10.1016/j.heliyon.2023.e14115] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 02/20/2023] [Accepted: 02/22/2023] [Indexed: 03/08/2023] Open
Abstract
The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which -by leveraging available transcriptomic and proteomic databases-allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both>96%) the viral effects on cellular host-immune response, resulting in specific cellular SARS-CoV-2 signatures and ii) utilize these cell-specific signatures to identify promising repurposable therapeutics. Powered by this tool, coupled with domain expertise, we identify several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential druggable targets in COVID-19 pathogenesis.
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Key Words
- 2DG, 2-Deoxy-Glucose
- ACE2, Angiotensin-converting enzyme 2
- COVID-19
- COVID-19, Coronavirus disease 2019
- Caco-2, Human colon epithelial carcinoma cell line
- Calu-3, Epithelial cell line
- Cellular SARS-CoV-2 signatures
- Cellular host-immune response
- Cellular simulation models
- DEGs, Differentially Expressed Genes
- DEPs, Differentially expressed proteins
- Drug repurposing
- HCQ-CQ, (Hydroxy)chloroquine
- IFN, Interferon
- ISGs, IFN-stimulated genes
- MITHrIL, Mirna enrIched paTHway Impact anaLysis
- MOI, Multiplicity of infection
- MP, Methylprednisolone
- NHBE, Normal human bronchial epithelial cells
- PHENSIM, PHENotype SIMulator
- SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2
- Systems biology
- TLR, Toll-like Receptor
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Affiliation(s)
- Naomi I. Maria
- Department of Computer Science, Mathematics, Engineering and Cell Biology, Courant Institute, Tandon and School of Medicine, New York University, New York, USA
- Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Department of Medicine, Donald and Barbara Zucker School of Medicine at Hofstra, Northwell Health, Manhasset, NY, USA
- Red Cross Blood Bank Foundation Curaçao, Willemstad, Curaçao
- Department of Medical Microbiology and Immunology, St. Antonius Ziekenhuis, Niewegein, the Netherlands
- Corresponding author. Department of Computer Science, Mathematics, Engineering and Cell Biology, Courant Institute, Tandon and School of Medicine, New York University, New York, USA.
| | - Rosaria Valentina Rapicavoli
- Department of Physics and Astronomy, University of Catania, Italy
- Bioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, Italy
| | - Salvatore Alaimo
- Bioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, Italy
| | - Evelyne Bischof
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini, Naples, Italy
- School of Clinical Medicine, Shanghai University of Medicine and Health Sciences, Pudong, Shanghai, China
- Insilico Medicine, Hong Kong Special Administrative Region, China
| | | | - Jantine A.C. Broek
- Department of Computer Science, Mathematics, Engineering and Cell Biology, Courant Institute, Tandon and School of Medicine, New York University, New York, USA
| | - Alfredo Pulvirenti
- Bioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, Italy
| | - Bud Mishra
- Department of Computer Science, Mathematics, Engineering and Cell Biology, Courant Institute, Tandon and School of Medicine, New York University, New York, USA
- Simon Center for Quantitative Biology, Cold Spring Harbor Lab, Long Island, USA
- Corresponding author. Courant Institute of Mathematical Sciences, Room 405, 251 Mercer Street, NY, USA.
| | - Ashley J. Duits
- Red Cross Blood Bank Foundation Curaçao, Willemstad, Curaçao
- Curaçao Biomedical Health Research Institute, Willemstad, Curaçao
- Institute for Medical Education, University Medical Center Groningen, Groningen, the Netherlands
| | - Alfredo Ferro
- Bioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, Italy
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Lang X, Liu J, Zhang G, Feng X, Dan W. Knowledge Mapping of Drug Repositioning's Theme and Development. Drug Des Devel Ther 2023; 17:1157-1174. [PMID: 37096060 PMCID: PMC10122475 DOI: 10.2147/dddt.s405906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/11/2023] [Indexed: 04/26/2023] Open
Abstract
Background In recent years, the emergence of new diseases and resistance to known diseases have led to increasing demand for new drugs. By means of bibliometric analysis, this paper studied the relevant articles on drug repositioning in recent years and analyzed the current research foci and trends. Methodology The Web of Science database was searched to collect all relevant literature on drug repositioning from 2001 to 2022. These data were imported into CiteSpace and bibliometric online analysis platforms for bibliometric analysis. The processed data and visualized images predict the development trends in the research field. Results The quality and quantity of articles published after 2011 have improved significantly, with 45 of them cited more than 100 times. Articles posted by journals from different countries have high citation values. Authors from other institutions have also collaborated to analyze drug rediscovery. Keywords found in the literature include molecular docking (N=223), virtual screening (N=170), drug discovery (N=126), machine learning (N=125), and drug-target interaction (N=68); these words represent the core content of drug repositioning. Conclusion The key focus of drug research and development is related to the discovery of new indications for drugs. Researchers are starting to retarget drugs after analyzing online databases and clinical trials. More and more drugs are being targeted at other diseases to treat more patients, based on saving money and time. It is worth noting that researchers need more financial and technical support to complete drug development.
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Affiliation(s)
- Xiaona Lang
- Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China
| | - Jinlei Liu
- Cardiology Department, Guang ‘anmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, People’s Republic of China
| | - Guangzhong Zhang
- Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China
| | - Xin Feng
- Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China
| | - Wenchao Dan
- Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China
- Correspondence: Wenchao Dan, Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China, Tel +86 13652001152, Email
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Ma J, Zhang L, Li S, Liu H. BRPCA: Bounded Robust Principal Component Analysis to Incorporate Similarity Network for N7-Methylguanosine(m 7G) Site-Disease Association Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3295-3306. [PMID: 34469307 DOI: 10.1109/tcbb.2021.3109055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Recent studies have revealed that N7-methylguanosine(m7G) plays a pivotal role in various biological processes and disease pathogenesis. To date, transcriptome-wide m7G modification sites have been identified by high-throughput sequencing approaches, and some related information has been recorded in a few biological databases. However, the mechanism of site action in disease remains uncharted. Wet experiments can help identify true m7G sites with high confidence, but it is time-consuming to find the true ones in such a large number of sites, which will also cost too much. Thus, computational methods are emergently needed to predict the associations between m7G sites and various diseases, thus help to uncover potential active sites for specific diseases. In this article, we proposed a bounded robust principal component analysis (BRPCA) method to predict unknown m7G-disease association based on similarity information. Importantly, BRPCA tolerates the noise and redundancy existing in association and similarity information. Moreover, a suitable bounded constraint is incorporated into BRPCA to ensure that the predicted association scores locate in a meaningful interval. The extensive experiments demonstrate the superiority and robustness of the BRPCA.
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Xu X, Xuan P, Zhang T, Chen B, Sheng N. Inferring Drug-Target Interactions Based on Random Walk and Convolutional Neural Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2294-2304. [PMID: 33729947 DOI: 10.1109/tcbb.2021.3066813] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Computational strategies for identifying new drug-target interactions (DTIs) can guide the process of drug discovery, reduce the cost and time of drug development, and thus promote drug development. Most recently proposed methods predict DTIs via integration of heterogeneous data related to drugs and proteins. However, previous methods have failed to deeply integrate these heterogeneous data and learn deep feature representations of multiple original similarities and interactions related to drugs and proteins. We therefore constructed a heterogeneous network by integrating a variety of connection relationships about drugs and proteins, including drugs, proteins, and drug side effects, as well as their similarities, interactions, and associations. A DTI prediction method based on random walk and convolutional neural network was proposed and referred to as DTIPred. DTIPred not only takes advantage of various original features related to drugs and proteins, but also integrates the topological information of heterogeneous networks. The prediction model is composed of two sides and learns the deep feature representation of a drug-protein pair. On the left side, random walk with restart is applied to learn the topological vectors of drug and protein nodes. The topological representation is further learned by the constructed deep learning frame based on convolutional neural network. The right side of the model focuses on integrating multiple original similarities and interactions of drugs and proteins to learn the original representation of the drug-protein pair. The results of cross-validation experiments demonstrate that DTIPred achieves better prediction performance than several state-of-the-art methods. During the validation process, DTIPred can retrieve more actual drug-protein interactions within the top part of the predicted results, which may be more helpful to biologists. In addition, case studies on five drugs further demonstrate the ability of DTIPred to discover potential drug-protein interactions.
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Computational Methods for Drug Repurposing. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:119-141. [PMID: 35230686 DOI: 10.1007/978-3-030-91836-1_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The wealth of knowledge and multi-omics data available in drug research has allowed the rise of several computational methods in the drug discovery field, resulting in a novel and exciting strategy called drug repurposing. Drug repurposing consists in finding new applications for existing drugs. Numerous computational methods perform a high-level integration of different knowledge sources to facilitate the discovery of unknown mechanisms. In this chapter, we present a survey of data resources and computational tools available for drug repositioning.
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Xuan P, Chen B, Zhang T, Yang Y. Prediction of Drug-Target Interactions Based on Network Representation Learning and Ensemble Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2671-2681. [PMID: 32340959 DOI: 10.1109/tcbb.2020.2989765] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Identifying interactions between drugs and target proteins is a critical step in the drug development process, as it helps identify new targets for drugs and accelerate drug development. The number of known drug-protein interactions (positive samples) is much lower than that of the unknown ones (negative samples), which forms a class imbalance. Most previous methods only utilised part of the negative samples to train the prediction model, so most of the information on negative samples was neglected. Therefore, a new method must be developed to predict candidate drug-related proteins and fully utilise negative samples to improve prediction performance. We present a method based on non-negative matrix factorisation and gradient boosting decision tree (GBDT), named NGDTP, to identify the candidate drug-protein interactions. NGDTP integrates multiple kinds of protein similarities, drugs-proteins interactions, and multiple kinds of drugs similarities at different levels, including target proteins of drugs, drug-related diseases, and side effects of drugs. We propose a network representation learning method based on matrix factorisation to learn low-dimensional vector representations of drug and protein nodes. On the basis of these low-dimensional node representations, a GBDT-based prediction model was constructed and it obtains the association scores through establishing multiple decision trees for a drug-protein pairs. NGDTP is an ensemble learning model that fully utilises all the negative samples to effectively alleviate the problem of class imbalance. NGDTP achieves superior prediction performance when it is compared with several state-of-the-art methods. The experimental results indicate that NGDTP also retrieves more actual drug-protein interactions in the top part of prediction result, which drew significant attention from the biologists. In addition, case studies on 10 drugs further confirmed the ability of the NGDTP to identify potential candidate proteins for drugs.
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Xuan P, Hu K, Cui H, Zhang T, Nakaguchi T. Learning multi-scale heterogeneous representations and global topology for drug-target interaction prediction. IEEE J Biomed Health Inform 2021; 26:1891-1902. [PMID: 34673498 DOI: 10.1109/jbhi.2021.3121798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Identification of drug-target interactions (DTIs) plays a critical role in drug discovery and repositioning. Deep integration of inter-connections and intra-similarities between heterogeneous multi-source data related to drugs and targets, however, is a challenging issue. We propose a DTI prediction model by learning from drug and protein related multi-scale attributes and global topology formed by heterogeneous connections. A drug-protein-disease heterogeneous network (RPD-Net) is firstly constructed to associate diverse similarities, interactions and associations across nodes. Secondly, we propose a multi-scale pairwise deep representation learning module consisting of a new embedding strategy to integrate diverse inter-relations and intra-relations, and dilation convolutions for multi-scale deep representation extraction. A global topology learning module is proposed which is composed of strategy based on non-negative matrix factorization (NMF) to extract topology from RPD-Net, and a new relational-level attention mechanism for discriminative topology embedding. Experimental results using public dataset demonstrate improved performance over state-of-the-art methods and contributions of our major innovations. Evaluation results by top k recall rates and case studies on five drugs further show the effectiveness in retrieving potential target candidates for drugs.
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8
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Sonaye HV, Sheikh RY, Doifode CA. Drug repurposing: Iron in the fire for older drugs. Biomed Pharmacother 2021; 141:111638. [PMID: 34153846 DOI: 10.1016/j.biopha.2021.111638] [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: 09/07/2020] [Revised: 04/19/2021] [Accepted: 04/19/2021] [Indexed: 12/22/2022] Open
Abstract
Repositioning or "repurposing" of existing therapies for indications of alternative disease is an attractive approach that can generate lower costs and require a shorter approval time than developing a de novo drug. The development of experimental drugs is time-consuming, expensive, and limited to a fairly small number of targets. The incorporation of separate and complementary data should be used, as each type of data set exposes a specific feature of organism knowledge Drug repurposing opportunities are often focused on sporadic findings or on time-consuming pre-clinical drug tests which are often not guided by hypothesis. In comparison, repurposing in-silico drugs is a new, hypothesis-driven method that takes advantage of big-data use. Nonetheless, the widespread use of omics technology, enhanced data storage, data sense, machine learning algorithms, and computational modeling all give unparalleled knowledge of the methods of action of biological processes and drugs, providing wide availability, for both disease-related data and drug-related data. This review has taken an in-depth look at the current state, possibilities, and limitations of further progress in the field of drug repositioning.
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Affiliation(s)
- H V Sonaye
- Shri Sachhidanand Shikshan Santh's Taywade College of Pharmacy, Nagpur 441111, India.
| | - R Y Sheikh
- K.E.M. Hospital Research Centre, Pune 411011, India.
| | - C A Doifode
- Shri Sachhidanand Shikshan Santh's Taywade College of Pharmacy, Nagpur 441111, India.
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Maria NI, Rapicavoli RV, Alaimo S, Bischof E, Stasuzzo A, Broek JA, Pulvirenti A, Mishra B, Duits AJ, Ferro A. Rapid Identification of Druggable Targets and the Power of the PHENotype SIMulator for Effective Drug Repurposing in COVID-19. RESEARCH SQUARE 2021:rs.3.rs-287183. [PMID: 33880466 PMCID: PMC8057245 DOI: 10.21203/rs.3.rs-287183/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with very few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which - by leveraging available transcriptomic and proteomic databases - allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both > 96%) the viral effects on cellular host-immune response, resulting in a specific cellular SARS-CoV-2 signature and ii) utilize this specific signature to narrow down promising repurposable therapeutic strategies. Powered by this tool, coupled with domain expertise, we have identified several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential new druggable targets in COVID-19 pathogenesis.
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Affiliation(s)
- Naomi I. Maria
- Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Red Cross Blood Bank Foundation Curaçao, Willemstad, Curaçao
| | - Rosaria Valentina Rapicavoli
- Department of Physics and Astronomy, University of Catania
- Bioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, Italy
| | - Salvatore Alaimo
- Bioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, Italy
| | - Evelyne Bischof
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini, Naples, Italy
- School of Clinical Medicine, Shanghai University of Medicine and Health Sciences, Pudong, Shanghai, China
- Insilico Medicine, Hong Kong Special Administrative Region, China
| | | | - Jantine A.C. Broek
- Department of Computer Science, Mathematics, Engineering and Cell Biology, Courant Institute, Tandon and School of Medicine, New York University, New York, USA
| | - Alfredo Pulvirenti
- Bioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, Italy
| | - Bud Mishra
- Department of Computer Science, Mathematics, Engineering and Cell Biology, Courant Institute, Tandon and School of Medicine, New York University, New York, USA
- Simon Center for Quantitative Biology, Cold Spring Harbor Lab, Long Island, USA
| | - Ashley J. Duits
- Red Cross Blood Bank Foundation Curaçao, Willemstad, Curaçao
- Curaçao Biomedical Health Research Institute, Willemstad, Curaçao
| | - Alfredo Ferro
- Bioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, Italy
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Li H, Pei F, Taylor DL, Bahar I. QuartataWeb: Integrated Chemical-Protein-Pathway Mapping for Polypharmacology and Chemogenomics. Bioinformatics 2020; 36:3935-3937. [PMID: 32221612 PMCID: PMC7320630 DOI: 10.1093/bioinformatics/btaa210] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 02/04/2020] [Accepted: 03/24/2020] [Indexed: 01/31/2023] Open
Abstract
SUMMARY QuartataWeb is a user-friendly server developed for polypharmacological and chemogenomics analyses. Users can easily obtain information on experimentally verified (known) and computationally predicted (new) interactions between 5494 drugs and 2807 human proteins in DrugBank, and between 315 514 chemicals and 9457 human proteins in the STITCH database. In addition, QuartataWeb links targets to KEGG pathways and GO annotations, completing the bridge from drugs/chemicals to function via protein targets and cellular pathways. It allows users to query a series of chemicals, drug combinations or multiple targets, to enable multi-drug, multi-target, multi-pathway analyses, toward facilitating the design of polypharmacological treatments for complex diseases. AVAILABILITY AND IMPLEMENTATION QuartataWeb is freely accessible at http://quartata.csb.pitt.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hongchun Li
- Department of Computational and Systems Biology School of Medicine.,Research Center for Computer-Aided Drug Discovery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Fen Pei
- Department of Computational and Systems Biology School of Medicine.,Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - D Lansing Taylor
- Department of Computational and Systems Biology School of Medicine.,Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Ivet Bahar
- Department of Computational and Systems Biology School of Medicine.,Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15213, USA
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Chu Y, Shan X, Chen T, Jiang M, Wang Y, Wang Q, Salahub DR, Xiong Y, Wei DQ. DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method. Brief Bioinform 2020; 22:5910189. [PMID: 32964234 DOI: 10.1093/bib/bbaa205] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 08/06/2020] [Accepted: 08/10/2020] [Indexed: 12/20/2022] Open
Abstract
Identifying drug-target interactions (DTIs) is an important step for drug discovery and drug repositioning. To reduce the experimental cost, a large number of computational approaches have been proposed for this task. The machine learning-based models, especially binary classification models, have been developed to predict whether a drug-target pair interacts or not. However, there is still much room for improvement in the performance of current methods. Multi-label learning can overcome some difficulties caused by single-label learning in order to improve the predictive performance. The key challenge faced by multi-label learning is the exponential-sized output space, and considering label correlations can help to overcome this challenge. In this paper, we facilitate multi-label classification by introducing community detection methods for DTI prediction, named DTI-MLCD. Moreover, we updated the gold standard data set by adding 15,000 more positive DTI samples in comparison to the data set, which has widely been used by most of previously published DTI prediction methods since 2008. The proposed DTI-MLCD is applied to both data sets, demonstrating its superiority over other machine learning methods and several existing methods. The data sets and source code of this study are freely available at https://github.com/a96123155/DTI-MLCD.
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Affiliation(s)
- Yanyi Chu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Xiaoqi Shan
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Tianhang Chen
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Mingming Jiang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Yanjing Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Qiankun Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | | | - Yi Xiong
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Dong-Qing Wei
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
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Fiannaca A, Paglia LL, Rosa ML, Rizzo R, Urso A. miRTissue ce: extending miRTissue web service with the analysis of ceRNA-ceRNA interactions. BMC Bioinformatics 2020; 21:199. [PMID: 32938402 PMCID: PMC7493844 DOI: 10.1186/s12859-020-3520-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Accepted: 04/29/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Non-coding RNAs include different classes of molecules with regulatory functions. The most studied are microRNAs (miRNAs) that act directly inhibiting mRNA expression or protein translation through the interaction with a miRNAs-response element. Other RNA molecules participate in the complex network of gene regulation. They behave as competitive endogenous RNA (ceRNA), acting as natural miRNA sponges to inhibit miRNA functions and modulate the expression of RNA messenger (mRNA). It became evident that understanding the ceRNA-miRNA-mRNA crosstalk would increase the functional information across the transcriptome, contributing to identify new potential biomarkers for translational medicine. RESULTS We present miRTissue ce, an improvement of our original miRTissue web service. By introducing a novel computational pipeline, miRTissue ce provides an easy way to search for ceRNA interactions in several cancer tissue types. Moreover it extends the functionalities of previous miRTissue release about miRNA-target interaction in order to provide a complete insight about miRNA mediated regulation processes. miRTissue ce is freely available at http://tblab.pa.icar.cnr.it/mirtissue.html . CONCLUSIONS The study of ceRNA networks and its dynamics in cancer tissue could be applied in many fields of translational biology, as the investigation of new cancer biomarker, both diagnostic and prognostic, and also in the investigation of new therapeutic strategies of intervention. In this scenario, miRTissue ce can offer a powerful instrument for the analysis and characterization of ceRNA-ceRNA interactions in different tissue types, representing a fundamental step in order to understand more complex regulation mechanisms.
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Affiliation(s)
- Antonino Fiannaca
- CNR-ICAR, National Research Council of Italy, via Ugo La Malfa 153, Palermo, 90146 Italy
| | - Laura La Paglia
- CNR-ICAR, National Research Council of Italy, via Ugo La Malfa 153, Palermo, 90146 Italy
| | - Massimo La Rosa
- CNR-ICAR, National Research Council of Italy, via Ugo La Malfa 153, Palermo, 90146 Italy
| | - Riccardo Rizzo
- CNR-ICAR, National Research Council of Italy, via Ugo La Malfa 153, Palermo, 90146 Italy
| | - Alfonso Urso
- CNR-ICAR, National Research Council of Italy, via Ugo La Malfa 153, Palermo, 90146 Italy
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Wang W, Lv H, Zhao Y. Predicting DNA binding protein-drug interactions based on network similarity. BMC Bioinformatics 2020; 21:322. [PMID: 32689927 PMCID: PMC7372772 DOI: 10.1186/s12859-020-03664-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 07/15/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The study of DNA binding protein (DBP)-drug interactions can open a breakthrough for the treatment of genetic diseases and cancers. Currently, network-based methods are widely used for protein-drug interaction prediction, and many hidden relationships can be found through network analysis. We proposed a DCA (drug-cluster association) model for predicting DBP-drug interactions. The clusters are some similarities in the drug-binding site trimmers with their physicochemical properties. First, DBPs-drug binding sites are extracted from scPDB database. Second, each binding site is represented as a trimer which is obtained by sliding the window in the binding sites. Third, the trimers are clustered based on the physicochemical properties. Fourth, we build the network by generating the interaction matrix for representing the DCA network. Fifth, three link prediction methods are detected in the network. Finally, the common neighbor (CN) method is selected to predict drug-cluster associations in the DBP-drug network model. RESULT This network shows that drugs tend to bind to positively charged sites and the binding process is more likely to occur inside the DBPs. The results of the link prediction indicate that the CN method has better prediction performance than the PA and JA methods. The DBP-drug network prediction model is generated by using the CN method which predicted more accurately drug-trimer interactions and DBP-drug interactions. Such as, we found that Erythromycin (ERY) can establish an interaction relationship with HTH-type transcriptional repressor, which is fitted well with silico DBP-drug prediction. CONCLUSION The drug and protein bindings are local events. The binding of the drug-DBPs binding site represents this local binding event, which helps to understand the mechanism of DBP-drug interactions.
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Affiliation(s)
- Wei Wang
- Department of Computer Science and Technology, College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China. .,Big Data Engineering Laboratory for Teaching Resources & assessment of Education Quality, Henan Province, Xinxiang, China.
| | - Hehe Lv
- Department of Computer Science and Technology, College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China
| | - Yuan Zhao
- Department of Computer Science and Technology, College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China
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14
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A computational drug repositioning method applied to rare diseases: Adrenocortical carcinoma. Sci Rep 2020; 10:8846. [PMID: 32483162 PMCID: PMC7264316 DOI: 10.1038/s41598-020-65658-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 05/08/2020] [Indexed: 01/12/2023] Open
Abstract
Rare or orphan diseases affect only small populations, thereby limiting the economic incentive for the drug development process, often resulting in a lack of progress towards treatment. Drug repositioning is a promising approach in these cases, due to its low cost. In this approach, one attempts to identify new purposes for existing drugs that have already been developed and approved for use. By applying the process of drug repositioning to identify novel treatments for rare diseases, we can overcome the lack of economic incentives and make concrete progress towards new therapies. Adrenocortical Carcinoma (ACC) is a rare disease with no practical and definitive therapeutic approach. We apply Heter-LP, a new method of drug repositioning, to suggest novel therapeutic avenues for ACC. Our analysis identifies innovative putative drug-disease, drug-target, and disease-target relationships for ACC, which include Cosyntropin (drug) and DHCR7, IGF1R, MC1R, MAP3K3, TOP2A (protein targets). When results are analyzed using all available information, a number of novel predicted associations related to ACC appear to be valid according to current knowledge. We expect the predicted relations will be useful for drug repositioning in ACC since the resulting ranked lists of drugs and protein targets can be used to expedite the necessary clinical processes.
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15
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Chu Y, Kaushik AC, Wang X, Wang W, Zhang Y, Shan X, Salahub DR, Xiong Y, Wei DQ. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Brief Bioinform 2019; 22:451-462. [PMID: 31885041 DOI: 10.1093/bib/bbz152] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Revised: 11/01/2019] [Accepted: 11/04/2019] [Indexed: 12/18/2022] Open
Abstract
Drug-target interactions (DTIs) play a crucial role in target-based drug discovery and development. Computational prediction of DTIs can effectively complement experimental wet-lab techniques for the identification of DTIs, which are typically time- and resource-consuming. However, the performances of the current DTI prediction approaches suffer from a problem of low precision and high false-positive rate. In this study, we aim to develop a novel DTI prediction method for improving the prediction performance based on a cascade deep forest (CDF) model, named DTI-CDF, with multiple similarity-based features between drugs and the similarity-based features between target proteins extracted from the heterogeneous graph, which contains known DTIs. In the experiments, we built five replicates of 10-fold cross-validation under three different experimental settings of data sets, namely, corresponding DTI values of certain drugs (SD), targets (ST), or drug-target pairs (SP) in the training sets are missed but existed in the test sets. The experimental results demonstrate that our proposed approach DTI-CDF achieves a significantly higher performance than that of the traditional ensemble learning-based methods such as random forest and XGBoost, deep neural network, and the state-of-the-art methods such as DDR. Furthermore, there are 1352 newly predicted DTIs which are proved to be correct by KEGG and DrugBank databases. The data sets and source code are freely available at https://github.com//a96123155/DTI-CDF.
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Affiliation(s)
- Yanyi Chu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | | | - Xiangeng Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Wei Wang
- Mathematical Sciences, Shanghai Jiao Tong University
| | - Yufang Zhang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | | | | | - Yi Xiong
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Dong-Qing Wei
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
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16
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Sarsaiya S, Shi J, Chen J. Bioengineering tools for the production of pharmaceuticals: current perspective and future outlook. Bioengineered 2019; 10:469-492. [PMID: 31656120 PMCID: PMC6844412 DOI: 10.1080/21655979.2019.1682108] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Revised: 09/08/2019] [Accepted: 10/11/2019] [Indexed: 01/18/2023] Open
Abstract
The bioengineering tools have significant advantages through less time-consuming and utilized as a promising stage for the production of pharmaceutical bioproducts under the single platform. This review highlighted the advantages and current improvement in the plant, animal and microbial bioengineering tools and outlines feasible approaches by biological and process's bioengineering levels for advancing the economic feasibility of pharmaceutical's production. The critical analysis results revealed that system biology and synthetic biology along with advanced bioengineering tools like transcriptome, proteome, metabolome and nano bioengineering tools have shown a promising impact on the development of pharmaceutical's bioproducts. Tools to overcome and resolve the accompanying encounters of pharmaceutical's production that include nano bioengineering tools are also discussed. As a summary and prospect, it also gives new insight into the challenges and possible breakthrough of the development of pharmaceutical's bioproducts through bioengineering tools.
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Affiliation(s)
- Surendra Sarsaiya
- Key Laboratory of Basic Pharmacology and Joint International Research Laboratory of Ethnomedicine of Ministry of Education, Zunyi Medical University, Zunyi, China
- Bioresource Institute for Healthy Utilization, Zunyi Medical University, Zunyi, China
| | - Jingshan Shi
- Key Laboratory of Basic Pharmacology and Joint International Research Laboratory of Ethnomedicine of Ministry of Education, Zunyi Medical University, Zunyi, China
| | - Jishuang Chen
- Bioresource Institute for Healthy Utilization, Zunyi Medical University, Zunyi, China
- College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, China
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17
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Zhang W, Lin W, Zhang D, Wang S, Shi J, Niu Y. Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction. Curr Drug Metab 2019; 20:194-202. [PMID: 30129407 DOI: 10.2174/1389200219666180821094047] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 01/18/2018] [Accepted: 03/19/2018] [Indexed: 12/28/2022]
Abstract
BACKGROUND The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods. RESULTS In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods. CONCLUSION This study provides the guide to the development of computational methods for the drug-target interaction prediction.
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Affiliation(s)
- Wen Zhang
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Weiran Lin
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Ding Zhang
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Siman Wang
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Jingwen Shi
- School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China
| | - Yanqing Niu
- School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074, China
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18
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Masoudi-Sobhanzadeh Y, Omidi Y, Amanlou M, Masoudi-Nejad A. Drug databases and their contributions to drug repurposing. Genomics 2019; 112:1087-1095. [PMID: 31226485 DOI: 10.1016/j.ygeno.2019.06.021] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 05/23/2019] [Accepted: 06/17/2019] [Indexed: 12/31/2022]
Abstract
Drug repurposing is an interesting field in the drug discovery scope because of reducing time and cost. It is also considered as an appropriate method for finding medications for orphan and rare diseases. Hence, many researchers have proposed novel methods based on databases which contain different information. Thus, a suitable organization of data which facilitates the repurposing applications and provides a tool or a web service can be beneficial. In this review, we categorize drug databases and discuss their advantages and disadvantages. Surprisingly, to the best of our knowledge, the importance and potential of databases in drug repurposing are yet to be emphasized. Indeed, the available databases can be divided into several groups based on data content, and different classes can be applied to find a new application of the existing drugs. Furthermore, we propose some suggestions for making databases more effective and popular in this field.
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Affiliation(s)
- Yosef Masoudi-Sobhanzadeh
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Yadollah Omidi
- Research Center for Pharmaceutical Nanotechnology and Department of Pharamaceutics, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Massoud Amanlou
- Drug Design and Development Research Center, The Institute of Pharmaceutical Sciences (TIPS), Tehran University of Medical Sciences, Tehran 14176-53955, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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19
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Zhou L, Li Z, Yang J, Tian G, Liu F, Wen H, Peng L, Chen M, Xiang J, Peng L. Revealing Drug-Target Interactions with Computational Models and Algorithms. Molecules 2019; 24:molecules24091714. [PMID: 31052598 PMCID: PMC6540161 DOI: 10.3390/molecules24091714] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 04/24/2019] [Accepted: 04/26/2019] [Indexed: 12/02/2022] Open
Abstract
Background: Identifying possible drug-target interactions (DTIs) has become an important task in drug research and development. Although high-throughput screening is becoming available, experimental methods narrow down the validation space because of extremely high cost, low success rate, and time consumption. Therefore, various computational models have been exploited to infer DTI candidates. Methods: We introduced relevant databases and packages, mainly provided a comprehensive review of computational models for DTI identification, including network-based algorithms and machine learning-based methods. Specially, machine learning-based methods mainly include bipartite local model, matrix factorization, regularized least squares, and deep learning. Results: Although computational methods have obtained significant improvement in the process of DTI prediction, these models have their limitations. We discussed potential avenues for boosting DTI prediction accuracy as well as further directions.
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Affiliation(s)
- Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
| | - Zejun Li
- School of Computer Science, Hunan Institute of Technology, Henyang 421002, Hunan, China.
| | | | - Geng Tian
- Geneis (Beijing) Co. Ltd., Beijing 100102, China.
| | - Fuxing Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
| | - Hong Wen
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
| | - Li Peng
- School of Computer Science, University of Science and Technology of Hunan, Xiangtan 411201, Hunan, China.
| | - Min Chen
- School of Computer Science, Hunan Institute of Technology, Henyang 421002, Hunan, China.
| | - Ju Xiang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China.
- Neuroscience Research Center, Department of Basic Medical Sciences, Changsha Medical University, Changsha 410219, Hunan, China.
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
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20
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Lotfi Shahreza M, Ghadiri N, Mousavi SR, Varshosaz J, Green JR. A review of network-based approaches to drug repositioning. Brief Bioinform 2019; 19:878-892. [PMID: 28334136 DOI: 10.1093/bib/bbx017] [Citation(s) in RCA: 169] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Indexed: 01/17/2023] Open
Abstract
Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.
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Affiliation(s)
- Maryam Lotfi Shahreza
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Nasser Ghadiri
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | | | - Jaleh Varshosaz
- Drug Delivery Systems Research Center of Isfahan University of Medical Sciences
| | - James R Green
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
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21
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Long S, Yuan C, Wang Y, Zhang J, Li G. Network Pharmacology Analysis of Damnacanthus indicus C.F.Gaertn in Gene-Phenotype. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2019; 2019:1368371. [PMID: 30906409 PMCID: PMC6398045 DOI: 10.1155/2019/1368371] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 01/21/2019] [Accepted: 02/03/2019] [Indexed: 12/11/2022]
Abstract
Damnacanthus indicus C.F.Gaertn is known as Huci in traditional Chinese medicine. It contains a component having anthraquinone-like structure which is a part of the many used anticancer drugs. This study was to collect the evidence of disease-modulatory activities of Huci by analyzing the published literature on the chemicals and drugs. A list of its compounds and direct protein targets is predicted by using Bioinformatics Analysis Tool for Molecular Mechanism of TCM. A protein-protein interaction network using links between its directed targets and the other known targets was constructed. The DPT-associated genes in net were scrutinized by WebGestalt. Exploring the cancer genomics data related to Huci through cBio Portal. Survival analysis for the overlap genes is done by using UALCAN. We got 16 compounds and it predicts 62 direct protein targets and 100 DPTs and they were identified for these compounds. DPT-associated genes were analyzed by WebGestalt. Through the enrichment analysis, we got top 10 identified KEGG pathways. Refined analysis of KEGG pathways showed that one of these ten pathways is linked to Rap1 signaling pathway and another one is related to breast cancer. The survival analysis for the overlap genes shows the significant negative effect of these genes on the breast cancer patients. Through the research results of Damnacanthus indicus C.F.Gaertn, it is shown that medicine network pharmacology may be regarded as a new paradigm for guiding the future studies of the traditional Chinese medicine in different fields.
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Affiliation(s)
- Shengrong Long
- Department of Neurosurgery, The First Affiliated Hospital of China Medical University, NanJing Bei Road, Heping District, Shenyang, 110001, LiaoNing Province, China
| | - Caihong Yuan
- Department of Chinese Medicine, The First Affiliated Hospital of China Medical University, NanJing Bei Road, Heping District, Shenyang, 110001, LiaoNing Province, China
| | - Yue Wang
- Department of Neurosurgery, The First Affiliated Hospital of China Medical University, NanJing Bei Road, Heping District, Shenyang, 110001, LiaoNing Province, China
| | - Jie Zhang
- Department of Chinese Medicine, The First Affiliated Hospital of China Medical University, NanJing Bei Road, Heping District, Shenyang, 110001, LiaoNing Province, China
| | - Guangyu Li
- Department of Neurosurgery, The First Affiliated Hospital of China Medical University, NanJing Bei Road, Heping District, Shenyang, 110001, LiaoNing Province, China
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22
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Alaimo S, Pulvirenti A. Network-Based Drug Repositioning: Approaches, Resources, and Research Directions. Methods Mol Biol 2019; 1903:97-113. [PMID: 30547438 DOI: 10.1007/978-1-4939-8955-3_6] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The wealth of knowledge and omic data available in drug research allowed the rising of several computational methods in drug discovery field yielding a novel and exciting application called drug repositioning. Several computational methods try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter we present an in-depth review of data resources and computational models for drug repositioning.
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Affiliation(s)
- Salvatore Alaimo
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Alfredo Pulvirenti
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy.
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23
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Bonnici V, Busato F, Aldegheri S, Akhmedov M, Cascione L, Carmena AA, Bertoni F, Bombieri N, Kwee I, Giugno R. cuRnet: an R package for graph traversing on GPU. BMC Bioinformatics 2018; 19:356. [PMID: 30367572 PMCID: PMC6191969 DOI: 10.1186/s12859-018-2310-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND R has become the de-facto reference analysis environment in Bioinformatics. Plenty of tools are available as packages that extend the R functionality, and many of them target the analysis of biological networks. Several algorithms for graphs, which are the most adopted mathematical representation of networks, are well-known examples of applications that require high-performance computing, and for which classic sequential implementations are becoming inappropriate. In this context, parallel approaches targeting GPU architectures are becoming pervasive to deal with the execution time constraints. Although R packages for parallel execution on GPUs are already available, none of them provides graph algorithms. RESULTS This work presents cuRnet, a R package that provides a parallel implementation for GPUs of the breath-first search (BFS), the single-source shortest paths (SSSP), and the strongly connected components (SCC) algorithms. The package allows offloading computing intensive applications to GPU devices for massively parallel computation and to speed up the runtime up to one order of magnitude with respect to the standard sequential computations on CPU. We have tested cuRnet on a benchmark of large protein interaction networks and for the interpretation of high-throughput omics data thought network analysis. CONCLUSIONS cuRnet is a R package to speed up graph traversal and analysis through parallel computation on GPUs. We show the efficiency of cuRnet applied both to biological network analysis, which requires basic graph algorithms, and to complex existing procedures built upon such algorithms.
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Affiliation(s)
- Vincenzo Bonnici
- Department of Computer Science, University of Verona, Strada le Grazie, 15, Italy, Verona, Italy
| | - Federico Busato
- Department of Computer Science, University of Verona, Strada le Grazie, 15, Italy, Verona, Italy
| | - Stefano Aldegheri
- Department of Computer Science, University of Verona, Strada le Grazie, 15, Italy, Verona, Italy
| | - Murodzhon Akhmedov
- Institute of Oncology Research (IOR), Via Vincenzo Vela 6, Bellinzona, Switzerland
| | - Luciano Cascione
- Institute of Oncology Research (IOR), Via Vincenzo Vela 6, Bellinzona, Switzerland
| | | | - Francesco Bertoni
- Institute of Oncology Research (IOR), Via Vincenzo Vela 6, Bellinzona, Switzerland
| | - Nicola Bombieri
- Department of Computer Science, University of Verona, Strada le Grazie, 15, Italy, Verona, Italy
| | - Ivo Kwee
- Institute of Oncology Research (IOR), Via Vincenzo Vela 6, Bellinzona, Switzerland
| | - Rosalba Giugno
- Department of Computer Science, University of Verona, Strada le Grazie, 15, Italy, Verona, Italy.
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24
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Allaway RJ, La Rosa S, Guinney J, Gosline SJC. Probing the chemical-biological relationship space with the Drug Target Explorer. J Cheminform 2018; 10:41. [PMID: 30128806 PMCID: PMC6102167 DOI: 10.1186/s13321-018-0297-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 08/16/2018] [Indexed: 01/29/2023] Open
Abstract
Modern phenotypic high-throughput screens (HTS) present several challenges including identifying the target(s) that mediate the effect seen in the screen, characterizing ‘hits’ with a polypharmacologic target profile, and contextualizing screen data within the large space of drugs and screening models. To address these challenges, we developed the Drug–Target Explorer. This tool allows users to query molecules within a database of experimentally-derived and curated compound-target interactions to identify structurally similar molecules and their targets. It enables network-based visualizations of the compound-target interaction space, and incorporates comparisons to publicly-available in vitro HTS datasets. Furthermore, users can identify molecules using a query target or set of targets. The Drug Target Explorer is a multifunctional platform for exploring chemical space as it relates to biological targets, and may be useful at several steps along the drug development pipeline including target discovery, structure–activity relationship, and lead compound identification studies.
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Affiliation(s)
- Robert J Allaway
- Sage Bionetworks, 1100 Fairview Avenue N, Seattle, WA, 98109, USA
| | | | - Justin Guinney
- Sage Bionetworks, 1100 Fairview Avenue N, Seattle, WA, 98109, USA
| | - Sara J C Gosline
- Sage Bionetworks, 1100 Fairview Avenue N, Seattle, WA, 98109, USA.
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25
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Musa A, Ghoraie LS, Zhang SD, Glazko G, Yli-Harja O, Dehmer M, Haibe-Kains B, Emmert-Streib F. A review of connectivity map and computational approaches in pharmacogenomics. Brief Bioinform 2018; 19:506-523. [PMID: 28069634 PMCID: PMC5952941 DOI: 10.1093/bib/bbw112] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.
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Affiliation(s)
- Aliyu Musa
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Laleh Soltan Ghoraie
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Shu-Dong Zhang
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, University of Ulster, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Derry/Londonderry, Northern Ireland, UK
| | - Galina Glazko
- University of Rochester Department of Biostatistics and Computational Biology, Rochester, New York, USA
| | - Olli Yli-Harja
- Computational Systems Biology, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Matthias Dehmer
- Institute for Bioinformatics and Translational Research, UMIT- The Health and Life Sciences University, Eduard Wallnoefer Zentrum 1, Hall in Tyrol, Austria
| | - Benjamin Haibe-Kains
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Ontario Institute of Cancer Research, Toronto, ON, Canada
| | - Frank Emmert-Streib
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
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26
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Musa A, Ghoraie LS, Zhang SD, Glazko G, Yli-Harja O, Dehmer M, Haibe-Kains B, Emmert-Streib F. A review of connectivity map and computational approaches in pharmacogenomics. Brief Bioinform 2018. [PMID: 28069634 DOI: 10.1093/bib] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023] Open
Abstract
Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.
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Affiliation(s)
- Aliyu Musa
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Laleh Soltan Ghoraie
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Shu-Dong Zhang
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, University of Ulster, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Derry/Londonderry BT47 6SB, Northern Ireland, UK
| | - Galina Glazko
- University of Rochester Department of Biostatistics and Computational Biology, Rochester, New York 14642, USA
| | - Olli Yli-Harja
- Computational Systems Biology, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Matthias Dehmer
- Institute for Bioinformatics and Translational Research, UMIT- The Health and Life Sciences University, Eduard Wallnoefer Zentrum 1, 6060 Hall in Tyrol, Austria
| | - Benjamin Haibe-Kains
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Ontario Institute of Cancer Research, Toronto, ON, Canada
| | - Frank Emmert-Streib
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
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27
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Ezzat A, Wu M, Li XL, Kwoh CK. Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey. Brief Bioinform 2018; 20:1337-1357. [DOI: 10.1093/bib/bby002] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 12/21/2017] [Indexed: 01/18/2023] Open
Abstract
Abstract
Computational prediction of drug–target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down the search space for interactions by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. In this article, we aim to provide a comprehensive overview and empirical evaluation on the computational DTI prediction techniques, to act as a guide and reference for our fellow researchers. Specifically, we first describe the data used in such computational DTI prediction efforts. We then categorize and elaborate the state-of-the-art methods for predicting DTIs. Next, an empirical comparison is performed to demonstrate the prediction performance of some representative methods under different scenarios. We also present interesting findings from our evaluation study, discussing the advantages and disadvantages of each method. Finally, we highlight potential avenues for further enhancement of DTI prediction performance as well as related research directions.
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Ravikumar B, Aittokallio T. Improving the efficacy-safety balance of polypharmacology in multi-target drug discovery. Expert Opin Drug Discov 2017; 13:179-192. [DOI: 10.1080/17460441.2018.1413089] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Balaguru Ravikumar
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
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29
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Sam E, Athri P. Web-based drug repurposing tools: a survey. Brief Bioinform 2017; 20:299-316. [DOI: 10.1093/bib/bbx125] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Indexed: 12/15/2022] Open
Affiliation(s)
- Elizabeth Sam
- Department of Computer Science & Engineering Amrita, University Bengaluru, India
| | - Prashanth Athri
- Department of Computer Science & Engineering Amrita, University Bengaluru, India
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Hao M, Bryant SH, Wang Y. Predicting drug-target interactions by dual-network integrated logistic matrix factorization. Sci Rep 2017; 7:40376. [PMID: 28079135 PMCID: PMC5227688 DOI: 10.1038/srep40376] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 12/05/2016] [Indexed: 12/20/2022] Open
Abstract
In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.
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Affiliation(s)
- Ming Hao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Stephen H Bryant
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Yanli Wang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
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31
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Li X, Qin G, Yang Q, Chen L, Xie L. Biomolecular Network-Based Synergistic Drug Combination Discovery. BIOMED RESEARCH INTERNATIONAL 2016; 2016:8518945. [PMID: 27891522 PMCID: PMC5116515 DOI: 10.1155/2016/8518945] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Revised: 09/20/2016] [Accepted: 10/11/2016] [Indexed: 12/11/2022]
Abstract
Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.
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Affiliation(s)
- Xiangyi Li
- Key Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture, College of Food Science and Technology, Shanghai Ocean University, 999 Hu Cheng Huan Road, Shanghai 201306, China
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, China
| | - Guangrong Qin
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, China
| | - Qingmin Yang
- Key Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture, College of Food Science and Technology, Shanghai Ocean University, 999 Hu Cheng Huan Road, Shanghai 201306, China
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, China
| | - Lanming Chen
- Key Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture, College of Food Science and Technology, Shanghai Ocean University, 999 Hu Cheng Huan Road, Shanghai 201306, China
| | - Lu Xie
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, China
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Sardina DS, Alaimo S, Ferro A, Pulvirenti A, Giugno R. A novel computational method for inferring competing endogenous interactions. Brief Bioinform 2016; 18:1071-1081. [DOI: 10.1093/bib/bbw084] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Indexed: 12/14/2022] Open
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33
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Peng L, Liao B, Zhu W, Li Z, Li K. Predicting Drug-Target Interactions With Multi-Information Fusion. IEEE J Biomed Health Inform 2015; 21:561-572. [PMID: 26731781 DOI: 10.1109/jbhi.2015.2513200] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most models only consider chemical structures and protein sequences, and other models are oversimplified. Moreover, datasets used for analysis contain only true-positive interactions, and experimentally validated negative samples are unavailable. To overcome these limitations, we developed a semi-supervised based learning framework called NormMulInf through collaborative filtering theory by using labeled and unlabeled interaction information. The proposed method initially determines similarity measures, such as similarities among samples and local correlations among the labels of the samples, by integrating biological information. The similarity information is then integrated into a robust principal component analysis model, which is solved using augmented Lagrange multipliers. Experimental results on four classes of drug-target interaction networks suggest that the proposed approach can accurately classify and predict drug-target interactions. Part of the predicted interactions are reported in public databases. The proposed method can also predict possible targets for new drugs and can be used to determine whether atropine may interact with alpha1B- and beta1- adrenergic receptors. Furthermore, the developed technique identifies potential drugs for new targets and can be used to assess whether olanzapine and propiomazine may target 5HT2B. Finally, the proposed method can potentially address limitations on studies of multitarget drugs and multidrug targets.
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Facchiano A, Angelini C, Bosotti R, Guffanti A, Marabotti A, Marangoni R, Pascarella S, Romano P, Zanzoni A, Helmer-Citterich M. Preface: BITS2014, the annual meeting of the Italian Society of Bioinformatics. BMC Bioinformatics 2015; 16 Suppl 9:S1. [PMID: 26050789 PMCID: PMC4464032 DOI: 10.1186/1471-2105-16-s9-s1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
This Preface introduces the content of the BioMed Central journal Supplements related to BITS2014 meeting, held in Rome, Italy, from the 26th to the 28th of February, 2014.
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