On Distributed Collaboration for Biomedical Analyses
Cooperation of research groups is nowadays common for the development and execution of biomedical analyses. Multiple partners contribute data in this context, data that is often centralized for processing at some cluster-based or supercomputer-based infrastructure. In contrast, real distributed coll...
- Autores:
-
Boujdad, Fatima-Zahra
Gaignard, Alban
Südholt, Mario
Garzón-Alfonso, Wilmer
Benavides Navarro, Luis Daniel
Redon, Richard
- Tipo de recurso:
- Documento de conferencia en no proceso
- Fecha de publicación:
- 2019
- Institución:
- Escuela Colombiana de Ingeniería Julio Garavito
- Repositorio:
- Repositorio Institucional ECI
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.escuelaing.edu.co:001/1797
- Acceso en línea:
- https://repositorio.escuelaing.edu.co/handle/001/1797
- Palabra clave:
- data privacy
distributed databases
groupware
medical administrative data processing
medical computing
Arquitectura de computadores
Protección de datos
Procesamiento electrónico de datos
Bioinformática
Computer architecture
Collaboration
Genetics
Bioinformatics
Cloud computing
Hospitals
Security
- Rights
- closedAccess
- License
- https://creativecommons.org/licenses/by/4.0/
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dc.title.eng.fl_str_mv |
On Distributed Collaboration for Biomedical Analyses |
title |
On Distributed Collaboration for Biomedical Analyses |
spellingShingle |
On Distributed Collaboration for Biomedical Analyses data privacy distributed databases groupware medical administrative data processing medical computing Arquitectura de computadores Protección de datos Procesamiento electrónico de datos Bioinformática Computer architecture Collaboration Genetics Bioinformatics Cloud computing Hospitals Security |
title_short |
On Distributed Collaboration for Biomedical Analyses |
title_full |
On Distributed Collaboration for Biomedical Analyses |
title_fullStr |
On Distributed Collaboration for Biomedical Analyses |
title_full_unstemmed |
On Distributed Collaboration for Biomedical Analyses |
title_sort |
On Distributed Collaboration for Biomedical Analyses |
dc.creator.fl_str_mv |
Boujdad, Fatima-Zahra Gaignard, Alban Südholt, Mario Garzón-Alfonso, Wilmer Benavides Navarro, Luis Daniel Redon, Richard |
dc.contributor.author.none.fl_str_mv |
Boujdad, Fatima-Zahra Gaignard, Alban Südholt, Mario Garzón-Alfonso, Wilmer Benavides Navarro, Luis Daniel Redon, Richard |
dc.contributor.researchgroup.spa.fl_str_mv |
Informática |
dc.subject.armarc.none.fl_str_mv |
data privacy distributed databases groupware medical administrative data processing medical computing |
topic |
data privacy distributed databases groupware medical administrative data processing medical computing Arquitectura de computadores Protección de datos Procesamiento electrónico de datos Bioinformática Computer architecture Collaboration Genetics Bioinformatics Cloud computing Hospitals Security |
dc.subject.armarc.spa.fl_str_mv |
Arquitectura de computadores Protección de datos Procesamiento electrónico de datos Bioinformática |
dc.subject.proposal.eng.fl_str_mv |
Computer architecture Collaboration Genetics Bioinformatics Cloud computing Hospitals Security |
description |
Cooperation of research groups is nowadays common for the development and execution of biomedical analyses. Multiple partners contribute data in this context, data that is often centralized for processing at some cluster-based or supercomputer-based infrastructure. In contrast, real distributed collaboration that involves processing of data from several partners at different sites is rare. However, such distributed analyses are often very interesting, in particular, for scalability, security and privacy reasons. In this article, we motivate the need for real distributed biomedical analyses in the context of several ongoing projects, including the ICAN project that involves 34 French hospitals and affiliated research groups. We present a set of distributed architectures for such analyses that we have derived from discussions with different medical research groups and a study of related work. These architectures allow for scalability, security/privacy and reproducibility issues to be taken into account. Finally, we illustrate that these architectures can serve as the basis of a development method for biomedical distributed analyses. |
publishDate |
2019 |
dc.date.issued.none.fl_str_mv |
2019 |
dc.date.accessioned.none.fl_str_mv |
2021-11-04T16:15:06Z |
dc.date.available.none.fl_str_mv |
2021-11-04T16:15:06Z |
dc.type.spa.fl_str_mv |
Documento de Conferencia |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 http://purl.org/coar/resource_type/c_c94f |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_18cp |
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Text |
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info:eu-repo/semantics/conferenceObject |
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http://purl.org/redcol/resource_type/ART |
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publishedVersion |
dc.identifier.isbn.none.fl_str_mv |
9781728109121 |
dc.identifier.uri.none.fl_str_mv |
https://repositorio.escuelaing.edu.co/handle/001/1797 |
identifier_str_mv |
9781728109121 |
url |
https://repositorio.escuelaing.edu.co/handle/001/1797 |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.relation.conferencedate.spa.fl_str_mv |
14-17 May 2019 |
dc.relation.conferenceplace.spa.fl_str_mv |
Larnaca, Cyprus. |
dc.relation.indexed.spa.fl_str_mv |
N/A |
dc.relation.ispartofconference.spa.fl_str_mv |
2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) |
dc.relation.references.spa.fl_str_mv |
T. Manolio, "Collaborative genome-wide association studies of diverse diseases: programs of the nhgris office of population genomics", Pharmacogenomics, vol. 10, no. 2, 2009. Y. Zhang, M. Blanton and G. Almashaqbeh, "Secure distributed genome analysis for gwas and sequence comparison computation", BMC medical informatics and decision making, vol. 15, no. 5, 2015. Q. Li and T. Yang, "Large-scale collaborative imaging genetics studies of risk genetic factors for alzheimers disease across multiple institutions", Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, 2016. C. Fuchsberger, J. Flannick et al., "The genetic architecture of type 2 diabetes", Nature, 2016. J. Luo, M. Wu et al., "Big data application in biomedical research and health care: a literature review", Biomedical informatics insights, vol. 8, 2016. G. Cattaneo, R. Giancarlo et al., "Mapreduce in computational biology-a synopsis", Italian WS on Artificial Life and Evolutionary Computation, 2016. L. Dai, X. Gao et al., "Bioinformatics clouds for big data manipulation", Biology direct, vol. 7, no. 1, 2012. R. Taylor, "An overview of the hadoop/mapreduce/hbase framework and its current applications in bioinformatics", BMC bioinformatics, vol. 11, no. 12, 2010. N. Homer, S. Szelinger et al., "Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays", PLOS Genetics, vol. 4, no. 8, 08 2008. F.-z. Boujdad and M. Sudholt, "Constructive Privacy for Shared Genetic Data", CLOSER 2018 -8th Int. Conf. on Cloud Computing and Services Science ser. Proceedings of CLOSER 2018, Mar. 2018. J. S. Sousa et al., "Efficient and secure outsourcing of genomic data storage", BMC medical genomics, vol. 10(Suppl 2), pp. 46, 2017. Y. Zhang, W. Dai et al., "Foresee: Fully outsourced secure genome study based on homomorphic encryption", ”BMC Medical Informatics and Decision Making, vol. 15, no. 5, Dec. 2015. W. Lu, Y. Yamada and J. Sakuma, "Privacy-preserving genome-wide association studies on cloud environment using fully homomorphic encryption", BMC medical informatics and decision making, vol. 15 Suppl 5, 2015. S. Wang et al., "Healer: homomorphic computation of exact logistic regression for secure rare disease variants analysis in gwas", Bioinformatics, vol. 32, no. 2, 2016. A. Acar et al., "A survey on homomorphic encryption schemes: Theory and implementation", ACM Comput. Surv., vol. 51, no. 4, Jul. 2018. M. Canim, M. Kantarcioglu and B. Malin, "Secure management of biomedical data with cryptographic hardware", Trans. Info. Tech. Biomed., vol. 16, no. 1, Jan. 2012. F. Chen, C. Wang et al., "Presage: Privacy-preserving genetic testing via software guard extension", BMC medical genomics, vol. 10(Suppl 2), pp. 48, 2017. N. Sadat, M. A. Aziz et al., "SAFETY: secure gwas in federated environment through a hybrid solution with intel SGX and homomorphic encryption", CoRR, vol. abs/1703.02577, 2017. V. Ciriani, S. Vimercati et al., "Combining fragmentation and encryption to protect privacy in data storage", ACM Trans. Inf. Syst. Secur., vol. 13, no. 3, Jul. 2010. X. Li, L. Zhang et al., "A novel workflow-level data placement strategy for data-sharing scientific cloud workflows", IEEE Trans. on Services Computing, 2016. Z. Er-Dun, Q. Yong-Qiang et al., "A data placement strategy based on genetic algorithm for scientific workflows", 2012 Eighth Int. Conf. on Computational Intelligence and Security, 2012. D. Yuan, Y. Yang et al., "A data placement strategy in scientific cloud workflows", Future Generation Computer Systems, vol. 26, no. 8, 2010. R. Stewart, P. Trinder et al., "Comparing high level mapreduce query languages", Int. WS on Adv. Parallel Proc. Techn, 2011. M. Ebrahimi, Data placement and task mapping optimization for big data workflows in the cloud, 2017. D. Agrawal, A. El Abbadi et al., "Data management challenges in cloud computing infrastructures", Int. WS on Databases in Networked Information Systems, 2010. M. Ebrahimi, A. Mohan et al., "Bdap: a big data placement strategy for cloud-based scientific workflows", 2015 IEEE First Int. Conf. on Big Data Computing Service and Applications, 2015. C. Tan, L. Sun and K. Liu, "Big data architecture for pervasive healthcare: A literature review", ECIS, 2015. K. Naganuma et al., "Privacy preserving analysis technique for secure cloud based big data analytics", Hitachi Rev, vol. 63, no. 9, 2014. C. Hasti and A. Hasti, "Data security in cloud-based analytics", Big Data Analytics, 2018. V. Kumar, R. Kumar et al., "Fully homomorphic encryption scheme with probabilistic encryption based on eulers theorem and application in cloud computing", Big Data Analytics, 2018. T. Doel and D. o. Shakir, "Gift-cloud: A data sharing and collaboration platform for medical imaging research", computer methods and programs in biomedicine, vol. 139, 2017. L. Ohno-Machado, V. Bafna et al., "idash: integrating data for analysis anonymization and sharing", J. of the American Medical Informatics Association, vol. 19, no. 2, 2011. Y. Gil, W. Cheung et al., "Privacy enforcement in data analysis workflows", Proceedings of the 2007 Int. Conf. on Privacy Enforcement and Accountability with Semantics-Volume 320. Citeseer, 2007. S. Davidson, S. Khanna et al., "Privacy issues in scientific workflow provenance", Proceedings of the 1st Int. WS on Workflow Approaches to New Data-centric Science, 2010. A. Chebotko, S. Chang et al., "Scientific workflow provenance querying with security views", 2008 The Ninth Int. Conf. on Web-Age Information Management, 2008. A. McKenna, M. Hanna et al., "The genome analysis toolkit: a mapreduce framework for analyzing next-generation dna sequencing data", Genome research, vol. 20, no. 9, 2010. J. Gurtowski, M. Schatz and B. Langmead, "Genotyping in the cloud with crossbow", Current protocols in bioinformatics, vol. 39, no. 1, 2012. R. Karim, A. Michel et al., "Improving data workflow systems with cloud services and use of open data for bioinformatics research", Briefings in bioinformatics, vol. 19, no. 5, 2017. S. Cohen-Boulakia, K. Belhajjame et al., "Scientific workflows for computational reproducibility in the life sciences: Status challenges and opportunities", Future Generation Computer Systems, vol. 75, 2017. M. Atkinson, S. Gesing et al., Scientific workflows: Past present and future, 2017. V. Geoffroy, C. Pizot et al., "Varank: a simple and powerful tool for ranking genetic variants", PeerJ, vol. 3, 2015. A. Dander, M. Handler et al., "[kd 3] a workflow-based application for exploration of biomedical data sets", Trans. on large-scale data-and knowledge-centered systems IV, 2011. Y. Lu, K. Tang et al., "Cafe: accelerated alignment-free sequence analysis: Supplementary material", The University of Southern California, 2017. M. Zytnicki and H. Quesneville, "S-mart a software toolbox to aid rnaseq data analysis", PloS one, vol. 6, no. 10, 2011. |
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Boujdad, Fatima-Zahra3c4eb87f57c988d8a784224eda8f2a66600Gaignard, Alban305a4d5d491f0b3ea34919a8ee940281600Südholt, Mario0250aeec3f859c0b7205d04882c0260d600Garzón-Alfonso, Wilmera344f444bfae1640ae24d18e8a70f522600Benavides Navarro, Luis Daniel6fb8a207341d8b2f068df4c441aa67dd600Redon, Richard5ed9441eeeca41b1416a7ad8aaef8c05600Informática2021-11-04T16:15:06Z2021-11-04T16:15:06Z20199781728109121https://repositorio.escuelaing.edu.co/handle/001/1797Cooperation of research groups is nowadays common for the development and execution of biomedical analyses. Multiple partners contribute data in this context, data that is often centralized for processing at some cluster-based or supercomputer-based infrastructure. In contrast, real distributed collaboration that involves processing of data from several partners at different sites is rare. However, such distributed analyses are often very interesting, in particular, for scalability, security and privacy reasons. In this article, we motivate the need for real distributed biomedical analyses in the context of several ongoing projects, including the ICAN project that involves 34 French hospitals and affiliated research groups. We present a set of distributed architectures for such analyses that we have derived from discussions with different medical research groups and a study of related work. These architectures allow for scalability, security/privacy and reproducibility issues to be taken into account. Finally, we illustrate that these architectures can serve as the basis of a development method for biomedical distributed analyses.La cooperación de grupos de investigación es hoy en día común para el desarrollo y ejecución de análisis biomédicos. Múltiples socios aportan datos en este contexto, datos que a menudo se centralizan para su procesamiento en alguna infraestructura basada en clústeres o basada en supercomputadoras. Por el contrario, la colaboración distribuida real que involucra el procesamiento de datos de varios socios en diferentes sitios es rara. Sin embargo, tales análisis distribuidos suelen ser muy interesantes, en particular, por razones de escalabilidad, seguridad y privacidad. En este artículo, motivamos la necesidad de análisis biomédicos distribuidos reales en el contexto de varios proyectos en curso, incluido el proyecto ICAN que involucra a 34 hospitales franceses y grupos de investigación afiliados. Presentamos un conjunto de arquitecturas distribuidas para tales análisis que hemos derivado de discusiones con diferentes grupos de investigación médica y un estudio de trabajo relacionado. Estas arquitecturas permiten tener en cuenta cuestiones de escalabilidad, seguridad/privacidad y reproducibilidad. Finalmente, ilustramos que estas arquitecturas pueden servir como base de un método de desarrollo para análisis biomédicos distribuidos.10 páginas.application/pdfengIEEE XploreLarnaca, Cyprushttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/closedAccessAtribución 4.0 Internacional (CC BY 4.0)http://purl.org/coar/access_right/c_14cbOn Distributed Collaboration for Biomedical AnalysesDocumento de Conferenciainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_18cphttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/resource_type/c_c94fTextinfo:eu-repo/semantics/conferenceObjecthttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a8514-17 May 2019Larnaca, Cyprus.N/A2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)T. Manolio, "Collaborative genome-wide association studies of diverse diseases: programs of the nhgris office of population genomics", Pharmacogenomics, vol. 10, no. 2, 2009.Y. Zhang, M. Blanton and G. Almashaqbeh, "Secure distributed genome analysis for gwas and sequence comparison computation", BMC medical informatics and decision making, vol. 15, no. 5, 2015.Q. Li and T. Yang, "Large-scale collaborative imaging genetics studies of risk genetic factors for alzheimers disease across multiple institutions", Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, 2016.C. Fuchsberger, J. Flannick et al., "The genetic architecture of type 2 diabetes", Nature, 2016.J. Luo, M. Wu et al., "Big data application in biomedical research and health care: a literature review", Biomedical informatics insights, vol. 8, 2016.G. Cattaneo, R. Giancarlo et al., "Mapreduce in computational biology-a synopsis", Italian WS on Artificial Life and Evolutionary Computation, 2016.L. Dai, X. Gao et al., "Bioinformatics clouds for big data manipulation", Biology direct, vol. 7, no. 1, 2012.R. Taylor, "An overview of the hadoop/mapreduce/hbase framework and its current applications in bioinformatics", BMC bioinformatics, vol. 11, no. 12, 2010.N. Homer, S. Szelinger et al., "Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays", PLOS Genetics, vol. 4, no. 8, 08 2008.F.-z. Boujdad and M. Sudholt, "Constructive Privacy for Shared Genetic Data", CLOSER 2018 -8th Int. Conf. on Cloud Computing and Services Science ser. Proceedings of CLOSER 2018, Mar. 2018.J. S. Sousa et al., "Efficient and secure outsourcing of genomic data storage", BMC medical genomics, vol. 10(Suppl 2), pp. 46, 2017.Y. Zhang, W. Dai et al., "Foresee: Fully outsourced secure genome study based on homomorphic encryption", ”BMC Medical Informatics and Decision Making, vol. 15, no. 5, Dec. 2015.W. Lu, Y. Yamada and J. Sakuma, "Privacy-preserving genome-wide association studies on cloud environment using fully homomorphic encryption", BMC medical informatics and decision making, vol. 15 Suppl 5, 2015.S. Wang et al., "Healer: homomorphic computation of exact logistic regression for secure rare disease variants analysis in gwas", Bioinformatics, vol. 32, no. 2, 2016.A. Acar et al., "A survey on homomorphic encryption schemes: Theory and implementation", ACM Comput. Surv., vol. 51, no. 4, Jul. 2018.M. Canim, M. Kantarcioglu and B. Malin, "Secure management of biomedical data with cryptographic hardware", Trans. Info. Tech. Biomed., vol. 16, no. 1, Jan. 2012.F. Chen, C. Wang et al., "Presage: Privacy-preserving genetic testing via software guard extension", BMC medical genomics, vol. 10(Suppl 2), pp. 48, 2017.N. Sadat, M. A. Aziz et al., "SAFETY: secure gwas in federated environment through a hybrid solution with intel SGX and homomorphic encryption", CoRR, vol. abs/1703.02577, 2017.V. Ciriani, S. Vimercati et al., "Combining fragmentation and encryption to protect privacy in data storage", ACM Trans. Inf. Syst. Secur., vol. 13, no. 3, Jul. 2010.X. Li, L. Zhang et al., "A novel workflow-level data placement strategy for data-sharing scientific cloud workflows", IEEE Trans. on Services Computing, 2016.Z. Er-Dun, Q. Yong-Qiang et al., "A data placement strategy based on genetic algorithm for scientific workflows", 2012 Eighth Int. Conf. on Computational Intelligence and Security, 2012.D. Yuan, Y. Yang et al., "A data placement strategy in scientific cloud workflows", Future Generation Computer Systems, vol. 26, no. 8, 2010.R. Stewart, P. Trinder et al., "Comparing high level mapreduce query languages", Int. WS on Adv. Parallel Proc. Techn, 2011.M. Ebrahimi, Data placement and task mapping optimization for big data workflows in the cloud, 2017.D. Agrawal, A. El Abbadi et al., "Data management challenges in cloud computing infrastructures", Int. WS on Databases in Networked Information Systems, 2010.M. Ebrahimi, A. Mohan et al., "Bdap: a big data placement strategy for cloud-based scientific workflows", 2015 IEEE First Int. Conf. on Big Data Computing Service and Applications, 2015.C. Tan, L. Sun and K. Liu, "Big data architecture for pervasive healthcare: A literature review", ECIS, 2015.K. Naganuma et al., "Privacy preserving analysis technique for secure cloud based big data analytics", Hitachi Rev, vol. 63, no. 9, 2014.C. Hasti and A. Hasti, "Data security in cloud-based analytics", Big Data Analytics, 2018.V. Kumar, R. Kumar et al., "Fully homomorphic encryption scheme with probabilistic encryption based on eulers theorem and application in cloud computing", Big Data Analytics, 2018.T. Doel and D. o. Shakir, "Gift-cloud: A data sharing and collaboration platform for medical imaging research", computer methods and programs in biomedicine, vol. 139, 2017.L. Ohno-Machado, V. Bafna et al., "idash: integrating data for analysis anonymization and sharing", J. of the American Medical Informatics Association, vol. 19, no. 2, 2011.Y. Gil, W. Cheung et al., "Privacy enforcement in data analysis workflows", Proceedings of the 2007 Int. Conf. on Privacy Enforcement and Accountability with Semantics-Volume 320. Citeseer, 2007.S. Davidson, S. Khanna et al., "Privacy issues in scientific workflow provenance", Proceedings of the 1st Int. WS on Workflow Approaches to New Data-centric Science, 2010.A. Chebotko, S. Chang et al., "Scientific workflow provenance querying with security views", 2008 The Ninth Int. Conf. on Web-Age Information Management, 2008.A. McKenna, M. Hanna et al., "The genome analysis toolkit: a mapreduce framework for analyzing next-generation dna sequencing data", Genome research, vol. 20, no. 9, 2010.J. Gurtowski, M. Schatz and B. Langmead, "Genotyping in the cloud with crossbow", Current protocols in bioinformatics, vol. 39, no. 1, 2012.R. Karim, A. Michel et al., "Improving data workflow systems with cloud services and use of open data for bioinformatics research", Briefings in bioinformatics, vol. 19, no. 5, 2017.S. Cohen-Boulakia, K. Belhajjame et al., "Scientific workflows for computational reproducibility in the life sciences: Status challenges and opportunities", Future Generation Computer Systems, vol. 75, 2017.M. Atkinson, S. Gesing et al., Scientific workflows: Past present and future, 2017.V. Geoffroy, C. Pizot et al., "Varank: a simple and powerful tool for ranking genetic variants", PeerJ, vol. 3, 2015.A. Dander, M. Handler et al., "[kd 3] a workflow-based application for exploration of biomedical data sets", Trans. on large-scale data-and knowledge-centered systems IV, 2011.Y. Lu, K. Tang et al., "Cafe: accelerated alignment-free sequence analysis: Supplementary material", The University of Southern California, 2017.M. Zytnicki and H. Quesneville, "S-mart a software toolbox to aid rnaseq data analysis", PloS one, vol. 6, no. 10, 2011.data privacydistributed databasesgroupwaremedical administrative data processingmedical computingArquitectura de computadoresProtección de datosProcesamiento electrónico de datosBioinformáticaComputer architectureCollaborationGeneticsBioinformaticsCloud computingHospitalsSecurityORIGINALOn Distributed Collaboration for Biomedical Analyses.pdfOn Distributed Collaboration for Biomedical Analyses.pdfapplication/pdf146969https://repositorio.escuelaing.edu.co/bitstream/001/1797/1/On%20Distributed%20Collaboration%20for%20Biomedical%20Analyses.pdff86ef00dff6b7b69fd1a469746c1ba13MD51metadata only accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81881https://repositorio.escuelaing.edu.co/bitstream/001/1797/2/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD52open accessTEXTIEEE.pdf.txtIEEE.pdf.txtExtracted texttext/plain2https://repositorio.escuelaing.edu.co/bitstream/001/1797/3/IEEE.pdf.txtd784fa8b6d98d27699781bd9a7cf19f0MD53metadata only accessOn Distributed Collaboration for Biomedical Analyses.pdf.txtOn Distributed Collaboration for Biomedical Analyses.pdf.txtExtracted texttext/plain2https://repositorio.escuelaing.edu.co/bitstream/001/1797/5/On%20Distributed%20Collaboration%20for%20Biomedical%20Analyses.pdf.txtd784fa8b6d98d27699781bd9a7cf19f0MD55metadata only accessTHUMBNAILIEEE.pdf.jpgIEEE.pdf.jpgGenerated Thumbnailimage/jpeg5458https://repositorio.escuelaing.edu.co/bitstream/001/1797/4/IEEE.pdf.jpgdb0936910005dffff6edc788e1430c6cMD54metadata only accessOn Distributed Collaboration for Biomedical Analyses.pdf.jpgOn Distributed Collaboration for Biomedical Analyses.pdf.jpgGenerated Thumbnailimage/jpeg5458https://repositorio.escuelaing.edu.co/bitstream/001/1797/6/On%20Distributed%20Collaboration%20for%20Biomedical%20Analyses.pdf.jpgdb0936910005dffff6edc788e1430c6cMD56metadata only access001/1797oai:repositorio.escuelaing.edu.co:001/17972022-07-27 11:29:55.859metadata only accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.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 |