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In current DCABM-TCM, 1816 blood constituents with chemical structures of 194 herbs and 192 prescriptions were manually mined from literature, together with the detailed experimental conditions under which the blood constituents were detected, including extraction method, experimental animal and animal model, administration method and dose, blood collection time and location etc. In addition, 1893 blood constituents without structures were also been recorded in the DCABM-TCM as they were given in the source publications.","url":"http://bionet.ncpsb.org.cn/dcabm-tcm/","foundedYear":"2021","hostInstitution":"Beijing Institute of Lifeomics","address":"State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.","city":"Beijing","province":"","country":"China","currentVersion":"v1.0","contactName":"Zhongyang Liu","contactEmail":"liuzy1984@163.com","availableProtocol":"1","lastUpdate":"2023-06","age":3,"citation":24,"token":null,"expireDate":null,"ins":0,"groupEmail":null,"isPartner":0,"funding":"","submitter":null,"pv":null,"isNew":0,"bigsearchId":null,"recordCreated":1692705919000,"lastModified":"2025-10-11 16:46:36","dataTypeList":[{"id":26063,"datatypeName":"Other","lastModified":1693830150000}],"categoryList":[{"id":31339,"name":"Health and medicine","lastModified":1693830150000}],"keywordsList":[{"id":44939,"name":"traditional chinese medicine","lastModified":1693830150000},{"id":44940,"name":"blood constituents","lastModified":1693830150000},{"id":44941,"name":"drug discovery","lastModified":1693830150000}],"dataObjectList":[{"id":23601,"name":"Plant","lastModified":1693830150000}],"publicationList":[{"id":74731,"pmid":"37486750","title":"DCABM-TCM: A Database of Constituents Absorbed into the Blood and Metabolites of Traditional Chinese Medicine.","author":"Xinyue Liu, Jinying Liu, Bangze Fu, Ruzhen Chen, Jianzhou Jiang, He Chen, Runa Li, Lin Xing, Liying Yuan, Xuetai Chen, Jing Zhang, Honglei Li, Shuzhen Guo, Feifei Guo, Jiachen Guo, Yuan Liu, Yaning Qi, Biyue Yu, Feng Xu, Dong Li, Zhongyang Liu","journal":"J Chem Inf Model","year":"2023","issue":"15","volume":"63","abst":"Traditional Chinese medicine (TCM) not only maintains the health of Asian people but also provides a great resource of active natural products for modern drug development. Herein, we developed a Database of Constituents Absorbed into the Blood and Metabolites of TCM (DCABM-TCM), the first database systematically collecting blood constituents of TCM prescriptions and herbs, including prototypes and metabolites experimentally detected in the blood, together with the corresponding detailed detection conditions through manual literature mining. The DCABM-TCM has collected 1816 blood constituents with chemical structures of 192 prescriptions and 194 herbs and integrated their related annotations, including physicochemical, absorption, distribution, metabolism, excretion, and toxicity properties, and associated targets, pathways, and diseases. Furthermore, the DCABM-TCM supported two blood constituent-based analysis functions, the network pharmacology analysis for TCM molecular mechanism elucidation, and the target/pathway/disease-based screening of candidate blood constituents, herbs, or prescriptions for TCM-based drug discovery. The DCABM-TCM is freely accessible at http://bionet.ncpsb.org.cn/dcabm-tcm/. The DCABM-TCM will contribute to the elucidation of effective constituents and molecular mechanism of TCMs and the discovery of TCM-derived drug-like compounds that are both bioactive and bioavailable.<br>","citation":31,"citationDate":"2026-04-25","lastModified":1777080862000,"createDate":1692176271000,"citationsList":"41797446,41765716,41588523,41588462,41349710,41232630,41214635,41260788,41261625,40854416,40838302,41052279,41626567,40697595,40985417,40516490,40480395,40356679,40303920,40084567,40005250,39947372,39899688,39835270,PPR963509,39392404,39327620,39208658,38496269,38398522,38747231","isNew":0,"isParse":1,"isExclude":0,"isMultipleHttp":0,"affiliations":null,"doi":null,"pubMonth":null,"firstPub":null}],"ratingList":[{"id":210,"ratingEmail":"1628549030@qq.com","dataScore":0,"contentScore":0,"systemScore":0,"ratingDate":1765284916000}],"organismList":[{"id":33344,"organismName":"Homo sapiens","lastModified":1693830150000}],"biodbRanks":[],"themeList":[],"zindex":8.0,"firstPublicationYear":0,"searchExample":null,"citedDate":"2026-01-04 00:00:47","ess":0},{"dbId":7670,"shortName":"TIDB","accession":"DBC007670","fullName":"Trained Immunity DataBase","description":"TIDB is a database that collects all previously identified genes associated with trained immunity from three species (Homo sapiens, Mus musculus and Rattus norvegicus) using the text-mining and manual curation.","url":"http://www.ieom-tm.com/tidb","foundedYear":"2021","hostInstitution":"Tianjin Institute of Environmental and Operational Medicine","address":"Department of Environmental Medicine, Tianjin Institute of Environmental and Operational Medicine, No.1 Dali Road, Heping District, Tianjin 300050, China\r\n","city":"Tianjin","province":"Tianjin","country":"China","currentVersion":"","contactName":"Chao Niu ","contactEmail":"niuchao601@126.com","availableProtocol":"1","lastUpdate":"","age":5,"citation":12,"token":null,"expireDate":null,"ins":0,"groupEmail":null,"isPartner":0,"funding":"","submitter":null,"pv":null,"isNew":0,"bigsearchId":null,"recordCreated":1650294739000,"lastModified":"2025-10-11 16:45:33","dataTypeList":[{"id":23228,"datatypeName":"Other","lastModified":1659507208000}],"categoryList":[{"id":27599,"name":"Literature","lastModified":1659507208000}],"keywordsList":[{"id":38800,"name":"immunity","lastModified":1659507208000}],"dataObjectList":[{"id":20691,"name":"Animal","lastModified":1659507208000}],"publicationList":[{"id":72517,"pmid":"34244719","title":"TIDB: a comprehensive database of trained immunity.","author":"Yang Cao, Qingyang Dong, Dan Wang, Ying Liu, Pengcheng Zhang, Xiaobo Yu, Chao Niu","journal":"Database (Oxford)","year":"2021","issue":null,"volume":"2021","abst":"Trained immunity is a newly emerging concept that defines the ability of the innate immune system to form immune memory and provide long-lasting protection against previously encountered antigens. Accumulating evidence reveals that trained immunity not only has broad benefits to host defense but is also harmful to the host in chronic inflammatory diseases. However, all trained immunity-related information is scattered in the literature and thus is difficult to access. Here, we describe Trained Immunity DataBase (TIDB), a comprehensive database that provides well-studied trained immunity-related genes from human, rat and mouse as well as the related literature evidence. Moreover, TIDB also provides three modules to analyze the function of the trained-immunity-related genes of interest, including Reactome pathway over-representation analysis, Gene Ontology enrichment analysis and protein-protein interaction subnetwork reconstruction. We believe TIDB will help developing valuable strategies for vaccine design and immune-mediated disease therapy. Database URL: http://www.ieom-tm.com/tidb.<br>","citation":12,"citationDate":"2026-04-25","lastModified":1777078625000,"createDate":null,"citationsList":"40572419,40232249,39024553,38892046,38705359,38198850,37696920,36713396,35896333,PPR513730,35571184,35251030","isNew":0,"isParse":1,"isExclude":0,"isMultipleHttp":0,"affiliations":null,"doi":null,"pubMonth":null,"firstPub":null}],"ratingList":[],"organismList":[{"id":28586,"organismName":"Homo sapiens","lastModified":1659507208000},{"id":28587,"organismName":"Mus musculus","lastModified":1659507208000},{"id":28588,"organismName":"Rattus norvegicus","lastModified":1659507208000}],"biodbRanks":[],"themeList":[],"zindex":2.4,"firstPublicationYear":0,"searchExample":null,"citedDate":"2026-01-04 00:00:43","ess":0},{"dbId":7329,"shortName":"PROLOD","accession":"DBC007329","fullName":"Protein Ontology Linked Open Data","description":"Protein Ontology Linked Open Data (LOD) exposes, shares, and connects knowledge about protein-related entities on the Semantic Web using Resource Description Framework (RDF), thus enabling integration with other Linked Open Data for biological knowledge discovery. ","url":"https://lod.proconsortium.org","foundedYear":"2020","hostInstitution":"University of Delaware","address":"Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, 19716, USA. chenc@udel.edu.","city":"Newark","province":"New Jersey","country":"United States","currentVersion":"","contactName":"Chuming Chen","contactEmail":"chenc@udel.edu","availableProtocol":"1","lastUpdate":"2020-10-12","age":6,"citation":9,"token":null,"expireDate":null,"ins":0,"groupEmail":null,"isPartner":0,"funding":null,"submitter":null,"pv":null,"isNew":0,"bigsearchId":null,"recordCreated":1605056120000,"lastModified":"2025-10-11 16:45:03","dataTypeList":[{"id":20191,"datatypeName":"Other","lastModified":1614157615000},{"id":20192,"datatypeName":"Protein","lastModified":1614157615000}],"categoryList":[{"id":23357,"name":"Standard ontology and nomenclature","lastModified":1614157615000},{"id":23358,"name":"Metadata","lastModified":1614157615000}],"keywordsList":[{"id":33712,"name":"protein ontology","lastModified":1614157615000}],"dataObjectList":[{"id":17128,"name":"NA","lastModified":1614157615000}],"publicationList":[{"id":71312,"pmid":"33046717","title":"Protein ontology on the semantic web for knowledge discovery.","author":"Chuming Chen, Hongzhan Huang, Karen E Ross, Julie E Cowart, Cecilia N Arighi, Cathy H Wu, Darren A Natale","journal":"Sci Data","year":"2020","issue":"1","volume":"7","abst":"The Protein Ontology (PRO) provides an ontological representation of protein-related entities, ranging from protein families to proteoforms to complexes. Protein Ontology Linked Open Data (LOD) exposes, shares, and connects knowledge about protein-related entities on the Semantic Web using Resource Description Framework (RDF), thus enabling integration with other Linked Open Data for biological knowledge discovery. For example, proteins (or variants thereof) can be retrieved on the basis of specific disease associations. As a community resource, we strive to follow the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles, disseminate regular updates of our data, support multiple methods for accessing, querying and downloading data in various formats, and provide documentation both for scientists and programmers. PRO Linked Open Data can be browsed via faceted browser interface and queried using SPARQL via YASGUI. RDF data dumps are also available for download. Additionally, we developed RESTful APIs to support programmatic data access. We also provide W3C HCLS specification compliant metadata description for our data. The PRO Linked Open Data is available at https://lod.proconsortium.org/ .<br>","citation":11,"citationDate":"2026-04-25","lastModified":1777077017000,"createDate":1613165966000,"citationsList":"40982457,39921684,39403518,39565895,38638909,37346701,36175836,35253974,34613368,PPR408524,PPR387292","isNew":0,"isParse":1,"isExclude":0,"isMultipleHttp":0,"affiliations":"chenc@udel.edu","doi":null,"pubMonth":null,"firstPub":null}],"ratingList":[],"organismList":[{"id":24099,"organismName":"NA","lastModified":1614157615000}],"biodbRanks":[],"themeList":[],"zindex":1.5,"firstPublicationYear":0,"searchExample":null,"citedDate":"2026-01-04 00:00:41","ess":0},{"dbId":3378,"shortName":"AAgMarker","accession":"DBC003378","fullName":"Human Serum Autoantigen Biomarker Resource ","description":"Human Serum Autoantigen Biomarker Resource (AAgMarker 1.0) is a free database that provides information of non-invasive serum autoantigen biomarker candidates identified from high-throughput autoantibody profiling projects. For basic search, autoantigen biomarkers are those desregulated autoantigens which could distinguish patients with diseases from healthy controls (AUC≥0.6, P value≤0.05) and be easily detected with high signaling rate (≥10%) in either group; Sensitivity, specificity and discriminative ability are also introduced to quantitatively evaluate the performance of biomarkers.","url":"http://bioinfo.wilmer.jhu.edu/AAgMarker/","foundedYear":"2018","hostInstitution":"Johns Hopkins University","address":"The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA\r\nTel: +1 443 287 3882; Fax: +1 410 502 5382","city":"Baltimore","province":"","country":"United States","currentVersion":"1.0","contactName":"Jiang Qian","contactEmail":"jiang.qian@jhmi.edu","availableProtocol":"1","lastUpdate":"","age":8,"citation":4,"token":"$2a$10$MYjlE.ltGFLtTb9OeLsXNOMi5gdqs7NHdFs4fNJTIIOGUuyF7l3LS","expireDate":"1544062756657","ins":0,"groupEmail":null,"isPartner":0,"funding":null,"submitter":null,"pv":null,"isNew":0,"bigsearchId":null,"recordCreated":1516993220000,"lastModified":"2025-10-11 16:39:06","dataTypeList":[{"id":16528,"datatypeName":"Protein","lastModified":1545979242000}],"categoryList":[{"id":18474,"name":"Health and medicine","lastModified":1545979242000}],"keywordsList":[{"id":27152,"name":"high-throughput autoantibody profiling projects","lastModified":1545979242000},{"id":27153,"name":"non-invasive serum autoantigen biomarker","lastModified":1545979242000}],"dataObjectList":[{"id":13246,"name":"Animal","lastModified":1545979242000}],"publicationList":[{"id":56224,"pmid":"28977551","title":"AAgMarker 1.0: a resource of serological autoantigen biomarkers for clinical diagnosis and prognosis of various human diseases.","author":"Pan J, Liu S, Zhu H, Qian J.","journal":"Nucleic Acids Res","year":"2018","issue":"D1","volume":"46","abst":"Autoantibodies are produced to target an individual's own antigens (e.g. proteins). They can trigger autoimmune responses and inflammation, and thus, cause many types of diseases. Many high-throughput autoantibody profiling projects have been reported for unbiased identification of serological autoantigen-based biomarkers. However, a lack of centralized data portal for these published assays has been a major obstacle to further data mining and cross-evaluate the quality of these datasets generated from different diseases. Here, we introduce a user-friendly database, AAgMarker 1.0, which collects many published raw datasets obtained from serum profiling assays on the proteome microarrays, and provides a toolbox for mining these data. The current version of AAgMarker 1.0 contains 854 serum samples, involving 136 092 proteins. A total of 7803 (4470 non-redundant) candidate autoantigen biomarkers were identified and collected for 12 diseases, such as Alzheimer's disease, Bechet's disease and Parkinson's disease. Seven statistical parameters are introduced to quantitatively assess these biomarkers. Users can retrieve, analyse and compare the datasets through basic search, advanced search and browse. These biomarkers are also downloadable by disease terms. The AAgMarker 1.0 is now freely accessible at http://bioinfo.wilmer.jhu.edu/AAgMarker/. We believe this database will be a valuable resource for the community of both biomedical and clinical research.<br>","citation":4,"citationDate":"2026-04-25","lastModified":1777059894000,"createDate":1613154256000,"citationsList":"38729610,38156953,34012435,32409234","isNew":0,"isParse":0,"isExclude":0,"isMultipleHttp":0,"affiliations":null,"doi":"10.1093/nar/gkx770","pubMonth":"1","firstPub":"2018-01-01"}],"ratingList":[],"organismList":[{"id":18498,"organismName":"Homo sapiens","lastModified":1545979242000}],"biodbRanks":[{"id":2672,"zindex":0.0,"citations":0,"year":2019,"month":11,"updateDate":1573450317000,"lastModified":1573450317000},{"id":7380,"zindex":0.0,"citations":0,"year":2018,"month":12,"updateDate":1543593600000,"lastModified":1573527618000}],"themeList":[],"zindex":0.5,"firstPublicationYear":2017,"searchExample":null,"citedDate":"2026-01-04 00:00:19","ess":0}]