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OUP accepted manuscript. Mutagenesis 2022; 37:191-202. [DOI: 10.1093/mutage/geac010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 04/09/2022] [Indexed: 11/14/2022] Open
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2
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Zhou B, Zhao X, Lu J, Sun Z, Liu M, Zhou Y, Liu R, Wang Y. Relating Substructures and Side Effects of Drugs with Chemical-chemical Interactions. Comb Chem High Throughput Screen 2020; 23:285-294. [DOI: 10.2174/1386207322666190702102752] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Revised: 03/11/2019] [Accepted: 04/16/2019] [Indexed: 12/17/2022]
Abstract
Background:Drugs are very important for human life because they can provide treatment, cure, prevention, or diagnosis of different diseases. However, they also cause side effects, which can increase the risks for humans and pharmaceuticals companies. It is essential to identify drug side effects in drug discovery. To date, lots of computational methods have been proposed to predict the side effects of drugs and most of them used the fact that similar drugs always have similar side effects. However, previous studies did not analyze which substructures are highly related to which kind of side effect.Method:In this study, we conducted a computational investigation. In this regard, we extracted a drug set for each side effect, which consisted of drugs having the side effect. Also, for each substructure, a set was constructed by picking up drugs owing such substructure. The relationship between one side effect and one substructure was evaluated based on linkages between drugs in their corresponding drug sets, resulting in an Es value. Then, the statistical significance of Es value was measured by a permutation test.Results and Conclusion:A number of highly related pairs of side effects and substructures were obtained and some were extensively analyzed to confirm the reliability of the results reported in this study.
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Affiliation(s)
- Bo Zhou
- Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
| | - Xian Zhao
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Jing Lu
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai 264005, China
| | - Zuntao Sun
- Informatization Office, Shanghai Maritime University, Shanghai 201306, China
| | - Min Liu
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Yilu Zhou
- Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Rongzhi Liu
- Center for Medical Device Evaluation, China Drug Administration, State Administration for Market Regulation, Beijing 100081, China
| | - Yihua Wang
- Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom
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3
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Hasson SS, H Al-Shubi AS, Al-Busaidi JZ, Al-Balushi MS, Hakkim FL, Rashan L, Aleemallah GM, Al-Jabri AA. Potential of Aucklandia Lappa Decne Ethanolic Extract to Trigger Apoptosis of Human T47D and Hela Cells. Asian Pac J Cancer Prev 2018; 19:1917-1925. [PMID: 30051673 PMCID: PMC6165671 DOI: 10.22034/apjcp.2018.19.7.1917] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Breast and cervical cancers are global health concerns and major cause of deaths among women. Current treatments such as chemotherapy are associated with several drawbacks that limit their effectiveness. Several anticancer remedies have been found with natural products in the past and the search continues for more examples. Cytotoxic natural compounds may have considerable benefits for cancer therapy either in potentiating the impact of chemotherapy or curtailment of harmful effects. Therefore, discovery and identification of new drugs for breast and cervical cancer treatment are of high priority. The present study addressed the potential role of the ALD (Aucklandia lappa Decne) in suppressing proliferation of T-47D, HeLa and HEp-2 cells in comparison with the non-cancer HCC1937 BL cell line. Treatment with an ALD extract of T-47D, HeLa, and HEp-2 cells resulted in reduction in cell viability in MMT assays. Furthermore, lyophilized ALD principally suppressed cancer cell line growth and proliferation through induction of either intrinsic or extrinsic apoptotic pathways as demonstrated by significantly suppressed release of LDH, and NO production in a dose-dependent manner, and activation of death receptors in T-47D and HeLa cells but not the HEp-2 cell line. Interestingly, lyophilized ALD significantly (p<0.005) repressed the growth of HEp-2 and T-47D cells after treatment for 48hrs while 24hrs treatment significantly suppressed T-47D and HeLa cells. We report for the first time that lyophilized ALD selectively influences apoptosis through alternative apoptotic pathways in both breast and cervical human cancer cells.
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Affiliation(s)
- S S Hasson
- Department of Microbiology and Immunology, College of Medicine and Health Sciences, Sultan Qaboos University, Oman
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Chen L, Lu J, Huang T, Cai YD. A computational method for the identification of candidate drugs for non-small cell lung cancer. PLoS One 2017; 12:e0183411. [PMID: 28820893 PMCID: PMC5562320 DOI: 10.1371/journal.pone.0183411] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Accepted: 08/03/2017] [Indexed: 11/25/2022] Open
Abstract
Lung cancer causes a large number of deaths per year. Until now, a cure for this disease has not been found or developed. Finding an effective drug through traditional experimental methods invariably costs millions of dollars and takes several years. It is imperative that computational methods be developed to integrate several types of existing information to identify candidate drugs for further study, which could reduce the cost and time of development. In this study, we tried to advance this effort by proposing a computational method to identify candidate drugs for non-small cell lung cancer (NSCLC), a major type of lung cancer. The method used three steps: (1) preliminary screening, (2) screening compounds by an association test and a permutation test, (3) screening compounds using an EM clustering algorithm. In the first step, based on the chemical-chemical interaction information reported in STITCH, a well-known database that reports interactions between chemicals and proteins, and approved NSCLC drugs, compounds that can interact with at least one approved NSCLC drug were picked. In the second step, the association test selected compounds that can interact with at least one NSCLC-related chemical and at least one NSCLC-related gene, and subsequently, the permutation test was used to discard nonspecific compounds from the remaining compounds. In the final step, core compounds were selected using a powerful clustering algorithm, the EM algorithm. Six putative compounds, protoporphyrin IX, hematoporphyrin, canertinib, lapatinib, pelitinib, and dacomitinib, were identified by this method. Previously published data show that all of the selected compounds have been reported to possess anti-NSCLC activity, indicating high probabilities of these compounds being novel candidate drugs for NSCLC.
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Affiliation(s)
- Lei Chen
- College of Life Science, Shanghai University, Shanghai, People’s Republic of China
- College of Information Engineering, Shanghai Maritime University, Shanghai, People’s Republic of China
| | - Jing Lu
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, People’s Republic of China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Yu-Dong Cai
- College of Life Science, Shanghai University, Shanghai, People’s Republic of China
- * E-mail:
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5
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Akbar S, Hayat M, Iqbal M, Jan MA. iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space. Artif Intell Med 2017; 79:62-70. [PMID: 28655440 DOI: 10.1016/j.artmed.2017.06.008] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 06/12/2017] [Accepted: 06/16/2017] [Indexed: 01/10/2023]
Abstract
Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of anticancer peptides. In this paper, evolutionary intelligent genetic algorithm-based ensemble model, 'iACP-GAEnsC', is proposed for the identification of anticancer peptides. In this model, the protein sequences are formulated, using three different discrete feature representation methods, i.e., amphiphilic Pseudo amino acid composition, g-Gap dipeptide composition, and Reduce amino acid alphabet composition. The performance of the extracted feature spaces are investigated separately and then merged to exhibit the significance of hybridization. In addition, the predicted results of individual classifiers are combined together, using optimized genetic algorithm and simple majority technique in order to enhance the true classification rate. It is observed that genetic algorithm-based ensemble classification outperforms than individual classifiers as well as simple majority voting base ensemble. The performance of genetic algorithm-based ensemble classification is highly reported on hybrid feature space, with an accuracy of 96.45%. In comparison to the existing techniques, 'iACP-GAEnsC' model has achieved remarkable improvement in terms of various performance metrics. Based on the simulation results, it is observed that 'iACP-GAEnsC' model might be a leading tool in the field of drug design and proteomics for researchers.
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Affiliation(s)
- Shahid Akbar
- Department of Computer Science, Abdul Wali Khan University Mardan, KP 23200, Pakistan.
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, KP 23200, Pakistan.
| | - Muhammad Iqbal
- Department of Computer Science, Abdul Wali Khan University Mardan, KP 23200, Pakistan.
| | - Mian Ahmad Jan
- Department of Computer Science, Abdul Wali Khan University Mardan, KP 23200, Pakistan.
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Wu JQ, Zhang SS, Gao H, Qi Z, Zhou CJ, Ji WW, Liu Y, Chen Y, Li Q, Li X, Wang H. Experimental and Theoretical Studies on Rhodium-Catalyzed Coupling of Benzamides with 2,2-Difluorovinyl Tosylate: Diverse Synthesis of Fluorinated Heterocycles. J Am Chem Soc 2017; 139:3537-3545. [DOI: 10.1021/jacs.7b00118] [Citation(s) in RCA: 202] [Impact Index Per Article: 28.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Jia-Qiang Wu
- School
of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Shang-Shi Zhang
- School
of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Hui Gao
- School
of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Zisong Qi
- Dalian
Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Chu-Jun Zhou
- School
of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Wei-Wei Ji
- School
of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Yao Liu
- School
of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Yunyun Chen
- School
of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Qingjiang Li
- School
of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Xingwei Li
- Dalian
Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Honggen Wang
- School
of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
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Lu J, Chen L, Yin J, Huang T, Bi Y, Kong X, Zheng M, Cai YD. Identification of new candidate drugs for lung cancer using chemical-chemical interactions, chemical-protein interactions and a K-means clustering algorithm. J Biomol Struct Dyn 2016; 34:906-17. [PMID: 26849843 DOI: 10.1080/07391102.2015.1060161] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Lung cancer, characterized by uncontrolled cell growth in the lung tissue, is the leading cause of global cancer deaths. Until now, effective treatment of this disease is limited. Many synthetic compounds have emerged with the advancement of combinatorial chemistry. Identification of effective lung cancer candidate drug compounds among them is a great challenge. Thus, it is necessary to build effective computational methods that can assist us in selecting for potential lung cancer drug compounds. In this study, a computational method was proposed to tackle this problem. The chemical-chemical interactions and chemical-protein interactions were utilized to select candidate drug compounds that have close associations with approved lung cancer drugs and lung cancer-related genes. A permutation test and K-means clustering algorithm were employed to exclude candidate drugs with low possibilities to treat lung cancer. The final analysis suggests that the remaining drug compounds have potential anti-lung cancer activities and most of them have structural dissimilarity with approved drugs for lung cancer.
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Affiliation(s)
- Jing Lu
- a School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong , Yantai University , Yantai , 264005 , P.R. China
| | - Lei Chen
- b College of Information Engineering , Shanghai Maritime University , Shanghai 201306 , P.R. China
| | - Jun Yin
- b College of Information Engineering , Shanghai Maritime University , Shanghai 201306 , P.R. China
| | - Tao Huang
- c The Key Laboratory of Stem Cell Biology , Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) , Shanghai 200025 , P.R. China
| | - Yi Bi
- a School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong , Yantai University , Yantai , 264005 , P.R. China
| | - Xiangyin Kong
- c The Key Laboratory of Stem Cell Biology , Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) , Shanghai 200025 , P.R. China
| | - Mingyue Zheng
- d Drug Discovery and Design Center , Shanghai Institute of Materia Medica , Shanghai 201203 , P.R. China
| | - Yu-Dong Cai
- e College of Life Science , Shanghai University , Shanghai 200444 , P.R. China
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Kabir M, Hayat M. iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples. Mol Genet Genomics 2015; 291:285-96. [DOI: 10.1007/s00438-015-1108-5] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Accepted: 08/19/2015] [Indexed: 10/23/2022]
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Chen L, Chu C, Lu J, Kong X, Huang T, Cai YD. A computational method for the identification of new candidate carcinogenic and non-carcinogenic chemicals. MOLECULAR BIOSYSTEMS 2015; 11:2541-50. [PMID: 26194467 DOI: 10.1039/c5mb00276a] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Cancer is one of the leading causes of human death. Based on current knowledge, one of the causes of cancer is exposure to toxic chemical compounds, including radioactive compounds, dioxin, and arsenic. The identification of new carcinogenic chemicals may warn us of potential danger and help to identify new ways to prevent cancer. In this study, a computational method was proposed to identify potential carcinogenic chemicals, as well as non-carcinogenic chemicals. According to the current validated carcinogenic and non-carcinogenic chemicals from the CPDB (Carcinogenic Potency Database), the candidate chemicals were searched in a weighted chemical network constructed according to chemical-chemical interactions. Then, the obtained candidate chemicals were further selected by a randomization test and information on chemical interactions and structures. The analyses identified several candidate carcinogenic chemicals, while those candidates identified as non-carcinogenic were supported by a literature search. In addition, several candidate carcinogenic/non-carcinogenic chemicals exhibit structural dissimilarity with validated carcinogenic/non-carcinogenic chemicals.
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Affiliation(s)
- Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People's Republic of China.
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10
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Zhang R, Cairelli MJ, Fiszman M, Kilicoglu H, Rindflesch TC, Pakhomov SV, Melton GB. Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs. Cancer Inform 2014; 13:103-11. [PMID: 25392688 PMCID: PMC4216049 DOI: 10.4137/cin.s13889] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Revised: 07/01/2014] [Accepted: 07/01/2014] [Indexed: 11/12/2022] Open
Abstract
In this study, we report on the performance of an automated approach to discovery of potential prostate cancer drugs from the biomedical literature. We used the semantic relationships in SemMedDB, a database of structured knowledge extracted from all MEDLINE citations using SemRep, to extract potential relationships using knowledge of cancer drugs pathways. Two cancer drugs pathway schemas were constructed using these relationships extracted from SemMedDB. Through both pathway schemas, we found drugs already used for prostate cancer therapy and drugs not currently listed as the prostate cancer medications. Our study demonstrates that the appropriate linking of relevant structured semantic relationships stored in SemMedDB can support the discovery of potential prostate cancer drugs.
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Affiliation(s)
- Rui Zhang
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA. ; Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Michael J Cairelli
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Marcelo Fiszman
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Halil Kilicoglu
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Thomas C Rindflesch
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Serguei V Pakhomov
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA. ; College of Pharmacy, University of Minnesota, Minneapolis, MN, USA
| | - Genevieve B Melton
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA. ; Department of Surgery, University of Minnesota, Minneapolis, MN, USA
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Chen L, Lu J, Huang T, Yin J, Wei L, Cai YD. Finding candidate drugs for hepatitis C based on chemical-chemical and chemical-protein interactions. PLoS One 2014; 9:e107767. [PMID: 25225900 PMCID: PMC4166673 DOI: 10.1371/journal.pone.0107767] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2014] [Accepted: 08/14/2014] [Indexed: 11/18/2022] Open
Abstract
Hepatitis C virus (HCV) is an infectious virus that can cause serious illnesses. Only a few drugs have been reported to effectively treat hepatitis C. To have greater diversity in drug choice and better treatment options, it is necessary to develop more drugs to treat the infection. However, it is time-consuming and expensive to discover candidate drugs using experimental methods, and computational methods may complement experimental approaches as a preliminary filtering process. This type of approach was proposed by using known chemical-chemical interactions to extract interactive compounds with three known drug compounds of HCV, and the probabilities of these drug compounds being able to treat hepatitis C were calculated using chemical-protein interactions between the interactive compounds and HCV target genes. Moreover, the randomization test and expectation-maximization (EM) algorithm were both employed to exclude false discoveries. Analysis of the selected compounds, including acyclovir and ganciclovir, indicated that some of these compounds had potential to treat the HCV. Hopefully, this proposed method could provide new insights into the discovery of candidate drugs for the treatment of HCV and other diseases.
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Affiliation(s)
- Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, People's Republic of China
| | - Jing Lu
- Department of Medicinal Chemistry, School of Pharmacy, Yantai University, Shandong, Yantai, People's Republic of China
| | - Tao Huang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Jun Yin
- College of Information Engineering, Shanghai Maritime University, Shanghai, People's Republic of China
| | - Lai Wei
- College of Information Engineering, Shanghai Maritime University, Shanghai, People's Republic of China
| | - Yu-Dong Cai
- Institute of Systems Biology, Shanghai University, Shanghai, People's Republic of China
- * E-mail:
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