BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Yoon BH, Kim SK, Kim SY. Use of Graph Database for the Integration of Heterogeneous Biological Data. Genomics Inform 2017;15:19-27. [PMID: 28416946 DOI: 10.5808/GI.2017.15.1.19] [Cited by in Crossref: 54] [Cited by in F6Publishing: 55] [Article Influence: 10.8] [Reference Citation Analysis]
Number Citing Articles
1 Feng F, Tang F, Gao Y, Zhu D, Li T, Yang S, Yao Y, Huang Y, Liu J. GenomicKB: a knowledge graph for the human genome. Nucleic Acids Research 2022. [DOI: 10.1093/nar/gkac957] [Reference Citation Analysis]
2 Xiao G, Pfaff E, Prud'hommeaux E, Booth D, Sharma DK, Huo N, Yu Y, Zong N, Ruddy KJ, Chute CG, Jiang G. FHIR-Ontop-OMOP: Building clinical knowledge graphs in FHIR RDF with the OMOP Common data Model. J Biomed Inform 2022;134:104201. [PMID: 36089199 DOI: 10.1016/j.jbi.2022.104201] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
3 Robin V, Bodein A, Scott-boyer M, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context. Front Mol Biosci 2022;9:962799. [DOI: 10.3389/fmolb.2022.962799] [Reference Citation Analysis]
4 Gabbar HA, Chahid A, Khan MJA, Adegboro OG, Samson MI. CTIMS: Automated Defect Detection Framework Using Computed Tomography. Applied Sciences 2022;12:2175. [DOI: 10.3390/app12042175] [Reference Citation Analysis]
5 Santos A, Colaço AR, Nielsen AB, Niu L, Strauss M, Geyer PE, Coscia F, Albrechtsen NJW, Mundt F, Jensen LJ, Mann M. A knowledge graph to interpret clinical proteomics data. Nat Biotechnol. [DOI: 10.1038/s41587-021-01145-6] [Cited by in Crossref: 16] [Cited by in F6Publishing: 17] [Article Influence: 16.0] [Reference Citation Analysis]
6 Tuteja S, Kumar R. Query-driven graph models in e-commerce. Innovations Syst Softw Eng. [DOI: 10.1007/s11334-021-00421-7] [Reference Citation Analysis]
7 Yang JJ, Gessner CR, Duerksen JL, Biber D, Binder JL, Ozturk M, Foote B, Mcentire R, Stirling K, Ding Y, Wild DJ. Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination. BMC Bioinformatics 2022;23. [DOI: 10.1186/s12859-021-04530-9] [Reference Citation Analysis]
8 Saad A, Nissen O, Eilertsen E, Bjørnson FO, Hagtun TN, Aspaas O, Baikas AA, Ohrem SJ. Towards Improved Visualization and Optimization of Aquaculture Production Process. Procedia Computer Science 2022;207:3439-3448. [DOI: 10.1016/j.procs.2022.09.531] [Reference Citation Analysis]
9 Friedrichs M. Automation in Graph-Based Data Integration and Mapping. Integrative Bioinformatics 2022. [DOI: 10.1007/978-981-16-6795-4_5] [Reference Citation Analysis]
10 Chakraborty P, Sen Gupta PS, Dey S, Chandra Das N, Patra R, Mukherjee S. Recent advances in processing, interpreting, and managing biological data for therapeutic intervention of human infectious disease. Big Data Analytics for Healthcare 2022. [DOI: 10.1016/b978-0-323-91907-4.00009-1] [Reference Citation Analysis]
11 Tuteja S, Kumar R. A Unification of Heterogeneous Data Sources into a Graph Model in E-commerce. Data Sci Eng 2022;7:57-70. [DOI: 10.1007/s41019-021-00174-0] [Reference Citation Analysis]
12 Zhu Q, Nguyễn ÐT, Sheils T, Alyea G, Sid E, Xu Y, Dickens J, Mathé EA, Pariser A. Scientific evidence based rare disease research discovery with research funding data in knowledge graph. Orphanet J Rare Dis 2021;16:483. [PMID: 34794473 DOI: 10.1186/s13023-021-02120-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
13 Williams CJ, Richardson DC, Richardson JS. The importance of residue-level filtering and the Top2018 best-parts dataset of high-quality protein residues. Protein Sci 2021. [PMID: 34779043 DOI: 10.1002/pro.4239] [Reference Citation Analysis]
14 Kim L, Yahia E, Segonds F, Véron P, Mallet A. i-Dataquest: A heterogeneous information retrieval tool using data graph for the manufacturing industry. Computers in Industry 2021;132:103527. [DOI: 10.1016/j.compind.2021.103527] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
15 Bundhoo E, Ghoorah AW, Jaufeerally-Fakim Y. TAGOPSIN: collating taxa-specific gene and protein functional and structural information. BMC Bioinformatics 2021;22:517. [PMID: 34688246 DOI: 10.1186/s12859-021-04429-5] [Reference Citation Analysis]
16 Williams CJ, Richardson DC, Richardson JS. The importance of residue-level filtering, and the Top2018 best-parts dataset of high-quality protein residues.. [DOI: 10.1101/2021.10.05.463241] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Dedié A, Bleimehl T, Täger J, Preusse M, Hrabě de angelis M, Jarasch A. DZDconnect: mit vernetzten Daten gegen Diabetes. Diabetologe 2021;17:780-7. [DOI: 10.1007/s11428-021-00807-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Bukhari SAC, Pawar S, Mandell J, Kleinstein SH, Cheung KH. LinkedImm: a linked data graph database for integrating immunological data. BMC Bioinformatics 2021;22:105. [PMID: 34433410 DOI: 10.1186/s12859-021-04031-9] [Reference Citation Analysis]
19 Tarazona S, Arzalluz-luque A, Conesa A. Undisclosed, unmet and neglected challenges in multi-omics studies. Nat Comput Sci 2021;1:395-402. [DOI: 10.1038/s43588-021-00086-z] [Cited by in Crossref: 14] [Cited by in F6Publishing: 16] [Article Influence: 14.0] [Reference Citation Analysis]
20 Timón-Reina S, Rincón M, Martínez-Tomás R. An overview of graph databases and their applications in the biomedical domain. Database (Oxford) 2021;2021:baab026. [PMID: 34003247 DOI: 10.1093/database/baab026] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
21 Mawkhiew HB, Sahoo L, Kharshiing EV. Gene-to-trait knowledge graphs show association of plant photoreceptors with physiological and developmental processes that can confer agronomic benefits. Genet Resour Crop Evol 2021;68:2727-35. [DOI: 10.1007/s10722-021-01214-4] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
22 Hassani-Pak K, Singh A, Brandizi M, Hearnshaw J, Parsons JD, Amberkar S, Phillips AL, Doonan JH, Rawlings C. KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species. Plant Biotechnol J 2021;19:1670-8. [PMID: 33750020 DOI: 10.1111/pbi.13583] [Cited by in Crossref: 17] [Cited by in F6Publishing: 21] [Article Influence: 17.0] [Reference Citation Analysis]
23 Struck A, Walsh B, Buchanan A, Lee JA, Spangler R, Stuart JM, Ellrott K. Exploring Integrative Analysis Using the BioMedical Evidence Graph. JCO Clin Cancer Inform 2020;4:147-59. [PMID: 32097025 DOI: 10.1200/CCI.19.00110] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
24 Friedrichs M. BioDWH2: an automated graph-based data warehouse and mapping tool. J Integr Bioinform 2021;18:167-76. [PMID: 33618440 DOI: 10.1515/jib-2020-0033] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
25 Yang JJ, Gessner CR, Duerksen JL, Biber D, Binder JL, Ozturk M, Foote B, Mcentire R, Stirling K, Ding Y, Wild DJ. Knowledge graph analytics platform with LINCS and IDG for Parkinson’s disease target illumination.. [DOI: 10.1101/2020.12.30.424881] [Reference Citation Analysis]
26 Kamm S, Jazdi N, Weyrich M. Knowledge Discovery in Heterogeneous and Unstructured Data of Industry 4.0 Systems: Challenges and Approaches. Procedia CIRP 2021;104:975-80. [DOI: 10.1016/j.procir.2021.11.164] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
27 Simpson CM, Gnad F. Applying graph database technology for analyzing perturbed co-expression networks in cancer. Database (Oxford) 2020;2020:baaa110. [PMID: 33306799 DOI: 10.1093/database/baaa110] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
28 Afandi MI, Wahyuni ED. University Research Graph Database For Efficient Multi-Perspective Data Analysis Using Neo4j. 2020 6th Information Technology International Seminar (ITIS) 2020. [DOI: 10.1109/itis50118.2020.9320965] [Reference Citation Analysis]
29 Adoni WYH, Nahhal T, Krichen M, El Byed A, Assayad I. DHPV: a distributed algorithm for large-scale graph partitioning. J Big Data 2020;7:76. [PMID: 32953386 DOI: 10.1186/s40537-020-00357-y] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
30 Bolduc B, Hodgkins SB, Varner RK, Crill PM, Mccalley CK, Chanton JP, Tyson GW, Riley WJ, Palace M, Duhaime MB, Hough MA, Saleska SR, Sullivan MB, Rich VI; IsoGenie Project Coordinators, IsoGenie Project Team, A2A Project Team. The IsoGenie database: an interdisciplinary data management solution for ecosystems biology and environmental research. PeerJ 2020;8:e9467. [DOI: 10.7717/peerj.9467] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
31 Nentidis A, Bougiatiotis K, Krithara A, Paliouras G. iASiS Open Data Graph: Automated Semantic Integration of Disease-Specific Knowledge. 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) 2020. [DOI: 10.1109/cbms49503.2020.00049] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
32 Santos A, Colaço AR, Nielsen AB, Niu L, Geyer PE, Coscia F, Albrechtsen NJW, Mundt F, Jensen LJ, Mann M. Clinical Knowledge Graph Integrates Proteomics Data into Clinical Decision-Making.. [DOI: 10.1101/2020.05.09.084897] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 6.5] [Reference Citation Analysis]
33 Ermolaev V, Klangberg I, Madhwal Y, Vapper S, Wels S, Yanovich Y. Incorruptible Auditing: Blockchain-Powered Graph Database Management. 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) 2020. [DOI: 10.1109/icbc48266.2020.9169431] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
34 Schrodt J, Dudchenko A, Knaup-Gregori P, Ganzinger M. Graph-Representation of Patient Data: a Systematic Literature Review. J Med Syst 2020;44:86. [PMID: 32166501 DOI: 10.1007/s10916-020-1538-4] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
35 Thapa I, Ali H. A New Graph Database System for Multi-omics Data Integration and Mining Complex Biological Information. Computational Advances in Bio and Medical Sciences 2020. [DOI: 10.1007/978-3-030-46165-2_14] [Reference Citation Analysis]
36 Kim L, Yahia E, Segonds F, Véron P, Mallet A. i-DATAQUEST: A Proposal for a Manufacturing Data Query System Based on a Graph. Product Lifecycle Management Enabling Smart X 2020. [DOI: 10.1007/978-3-030-62807-9_19] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
37 [DOI: 10.1109/bigdata47090.2019.9006469] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
38 Mantzaris AV, Walker TG, Taylor CE, Ehling D. Adaptive network diagram constructions for representing big data event streams on monitoring dashboards. J Big Data 2019;6. [DOI: 10.1186/s40537-019-0187-2] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
39 Adoni HWY, Nahhal T, Krichen M, Aghezzaf B, Elbyed A. A survey of current challenges in partitioning and processing of graph-structured data in parallel and distributed systems. Distrib Parallel Databases 2020;38:495-530. [DOI: 10.1007/s10619-019-07276-9] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
40 Bukhari SAC, Mandell J, Kleinstein SH, Cheung KH. A linked data graph approach to integration of immunological data. Proceedings (IEEE Int Conf Bioinformatics Biomed) 2019;2019:1742-9. [PMID: 34707915 DOI: 10.1109/bibm47256.2019.8982986] [Reference Citation Analysis]
41 Struck A, Walsh B, Buchanan A, Lee JA, Spangler R, Stuart J, Ellrott K. Exploring Integrative Analysis using the BioMedical Evidence Graph.. [DOI: 10.1101/773911] [Reference Citation Analysis]
42 Sachs J, Page R, Baskauf SJ, Pender J, Lujan-toro B, Macklin J, Comspon Z. Training and hackathon on building biodiversity knowledge graphs. RIO 2019;5:e36152. [DOI: 10.3897/rio.5.e36152] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
43 Castillo-Lara S, Abril JF. PlanNET: homology-based predicted interactome for multiple planarian transcriptomes. Bioinformatics 2018;34:1016-23. [PMID: 29186384 DOI: 10.1093/bioinformatics/btx738] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 5.0] [Reference Citation Analysis]
44 Venkata RC, Ghersi D. Biological Pathway Analysis. Encyclopedia of Bioinformatics and Computational Biology 2019. [DOI: 10.1016/b978-0-12-809633-8.20476-7] [Reference Citation Analysis]
45 Godard P, van Eyll J. BED: a Biological Entity Dictionary based on a graph data model. F1000Res 2018;7:195. [PMID: 30026924 DOI: 10.12688/f1000research.13925.3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
46 Godard P, van Eyll J. BED: a Biological Entity Dictionary based on a graph data model. F1000Res 2018;7:195. [DOI: 10.12688/f1000research.13925.2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
47 Djemaiel Y, Berrahal S, Boudriga N. A Novel Graph-Based Approach for the Management of Health Data on Cloud-Based WSANs. J Grid Computing 2018;16:317-44. [DOI: 10.1007/s10723-018-9438-2] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 0.8] [Reference Citation Analysis]
48 Himmelstein DS, Lizee A, Hessler C, Brueggeman L, Chen SL, Hadley D, Green A, Khankhanian P, Baranzini SE. Systematic integration of biomedical knowledge prioritizes drugs for repurposing. Elife 2017;6:e26726. [PMID: 28936969 DOI: 10.7554/eLife.26726] [Cited by in Crossref: 189] [Cited by in F6Publishing: 199] [Article Influence: 37.8] [Reference Citation Analysis]
49 Himmelstein DS, Lizee A, Hessler C, Brueggeman L, Chen SL, Hadley D, Green A, Khankhanian P, Baranzini SE. Systematic integration of biomedical knowledge prioritizes drugs for repurposing.. [DOI: 10.1101/087619] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]