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Showing 13 to 24 of 1121 entries
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SSER: Species specific essential reactions database.

BMC systems biology

Labena AA, Ye YN, Dong C, Zhang FZ, Guo FB.
PMID: 28420402
BMC Syst Biol. 2017 Apr 19;11(1):50. doi: 10.1186/s12918-017-0426-0.

BACKGROUND: Essential reactions are vital components of cellular networks. They are the foundations of synthetic biology and are potential candidate targets for antimetabolic drug design. Especially if a single reaction is catalyzed by multiple enzymes, then inhibiting the reaction...

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Labena AA, Ye YN, Dong C, et al. SSER: Species specific essential reactions database. BMC Syst Biol. 2017;11(1):50doi: 10.1186/s12918-017-0426-0.
Labena, A. A., Ye, Y. N., Dong, C., Zhang, F. Z., & Guo, F. B. (2017). SSER: Species specific essential reactions database. BMC systems biology, 11(1), 50. https://doi.org/10.1186/s12918-017-0426-0
Labena, Abraham A, et al. "SSER: Species specific essential reactions database." BMC systems biology vol. 11,1 (2017): 50. doi: https://doi.org/10.1186/s12918-017-0426-0
Labena AA, Ye YN, Dong C, Zhang FZ, Guo FB. SSER: Species specific essential reactions database. BMC Syst Biol. 2017 Apr 19;11(1):50. doi: 10.1186/s12918-017-0426-0. PMID: 28420402; PMCID: PMC5395902.
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A heuristic approach to determine an appropriate number of topics in topic modeling.

BMC bioinformatics

Zhao W, Chen JJ, Perkins R, Liu Z, Ge W, Ding Y, Zou W.
PMID: 26424364
BMC Bioinformatics. 2015;16:S8. doi: 10.1186/1471-2105-16-S13-S8. Epub 2015 Sep 25.

BACKGROUND: Topic modelling is an active research field in machine learning. While mainly used to build models from unstructured textual data, it offers an effective means of data mining where samples represent documents, and different biological endpoints or omics...

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Zhao W, Chen JJ, Perkins R, et al. A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinformatics. 2015;16:S8doi: 10.1186/1471-2105-16-S13-S8.
Zhao, W., Chen, J. J., Perkins, R., Liu, Z., Ge, W., Ding, Y., & Zou, W. (2015). A heuristic approach to determine an appropriate number of topics in topic modeling. BMC bioinformatics, 16S8. https://doi.org/10.1186/1471-2105-16-S13-S8
Zhao, Weizhong, et al. "A heuristic approach to determine an appropriate number of topics in topic modeling." BMC bioinformatics vol. 16 (2015): S8. doi: https://doi.org/10.1186/1471-2105-16-S13-S8
Zhao W, Chen JJ, Perkins R, Liu Z, Ge W, Ding Y, Zou W. A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinformatics. 2015;16:S8. doi: 10.1186/1471-2105-16-S13-S8. Epub 2015 Sep 25. PMID: 26424364; PMCID: PMC4597325.
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Pathogen metadata platform: software for accessing and analyzing pathogen strain information.

BMC bioinformatics

Chang WE, Peterson MW, Garay CD, Korves T.
PMID: 27634291
BMC Bioinformatics. 2016 Sep 15;17(1):379. doi: 10.1186/s12859-016-1231-2.

BACKGROUND: Pathogen metadata includes information about where and when a pathogen was collected and the type of environment it came from. Along with genomic nucleotide sequence data, this metadata is growing rapidly and becoming a valuable resource not only...

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Chang WE, Peterson MW, Garay CD, et al. Pathogen metadata platform: software for accessing and analyzing pathogen strain information. BMC Bioinformatics. 2016;17(1):379doi: 10.1186/s12859-016-1231-2.
Chang, W. E., Peterson, M. W., Garay, C. D., & Korves, T. (2016). Pathogen metadata platform: software for accessing and analyzing pathogen strain information. BMC bioinformatics, 17(1), 379. https://doi.org/10.1186/s12859-016-1231-2
Chang, Wenling E, et al. "Pathogen metadata platform: software for accessing and analyzing pathogen strain information." BMC bioinformatics vol. 17,1 (2016): 379. doi: https://doi.org/10.1186/s12859-016-1231-2
Chang WE, Peterson MW, Garay CD, Korves T. Pathogen metadata platform: software for accessing and analyzing pathogen strain information. BMC Bioinformatics. 2016 Sep 15;17(1):379. doi: 10.1186/s12859-016-1231-2. PMID: 27634291; PMCID: PMC5025631.
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A review of the literature and proposed classification on e-prescribing: Functions, assimilation stages, benefits, concerns, and risks.

Research in social & administrative pharmacy : RSAP

Esmaeil Zadeh P, Tremblay MC.
PMID: 25847858
Res Social Adm Pharm. 2016 Jan-Feb;12(1):1-19. doi: 10.1016/j.sapharm.2015.03.001. Epub 2015 Mar 14.

BACKGROUND: Evidence from the literature indicates that besides its benefits, e-prescribing also generates new types of unintended medication errors that have the potential to harm patient safety. Analyzing both the benefits and risks of e-prescribing can give health care...

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Esmaeil Zadeh P, Tremblay MC. A review of the literature and proposed classification on e-prescribing: Functions, assimilation stages, benefits, concerns, and risks. Res Social Adm Pharm. 2015;12(1):1-19doi: 10.1016/j.sapharm.2015.03.001.
Esmaeil Zadeh, P., & Tremblay, M. C. (2016). A review of the literature and proposed classification on e-prescribing: Functions, assimilation stages, benefits, concerns, and risks. Research in social & administrative pharmacy : RSAP, 12(1), 1-19. https://doi.org/10.1016/j.sapharm.2015.03.001
Esmaeil Zadeh, Pouyan, and Tremblay, Monica Chiarini. "A review of the literature and proposed classification on e-prescribing: Functions, assimilation stages, benefits, concerns, and risks." Research in social & administrative pharmacy : RSAP vol. 12,1 (2016): 1-19. doi: https://doi.org/10.1016/j.sapharm.2015.03.001
Esmaeil Zadeh P, Tremblay MC. A review of the literature and proposed classification on e-prescribing: Functions, assimilation stages, benefits, concerns, and risks. Res Social Adm Pharm. 2016 Jan-Feb;12(1):1-19. doi: 10.1016/j.sapharm.2015.03.001. Epub 2015 Mar 14. PMID: 25847858.
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Case for fMRI data repositories.

Proceedings of the National Academy of Sciences of the United States of America

Iyengar S.
PMID: 27357671
Proc Natl Acad Sci U S A. 2016 Jul 12;113(28):7699-700. doi: 10.1073/pnas.1608146113. Epub 2016 Jun 29.

No abstract available.

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Iyengar S. Case for fMRI data repositories. Proc Natl Acad Sci U S A. 2016;113(28):7699-700doi: 10.1073/pnas.1608146113.
Iyengar, S. (2016). Case for fMRI data repositories. Proceedings of the National Academy of Sciences of the United States of America, 113(28), 7699-700. https://doi.org/10.1073/pnas.1608146113
Iyengar, Satish. "Case for fMRI data repositories." Proceedings of the National Academy of Sciences of the United States of America vol. 113,28 (2016): 7699-700. doi: https://doi.org/10.1073/pnas.1608146113
Iyengar S. Case for fMRI data repositories. Proc Natl Acad Sci U S A. 2016 Jul 12;113(28):7699-700. doi: 10.1073/pnas.1608146113. Epub 2016 Jun 29. PMID: 27357671; PMCID: PMC4948350.
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Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database.

Studies in health technology and informatics

Nienhoff H, Huebner U, Frey A, Przysucha M, Kimura M.
PMID: 26262371
Stud Health Technol Inform. 2015;216:1072.

The aim of this study was to investigate whether standard Big Data mining methods lead to clinically useful results. An association analysis was performed using the apriori algorithm to discover associations among co-morbidities of diabetes patients. Selected data were...

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Nienhoff H, Huebner U, Frey A, et al. Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database. Stud Health Technol Inform. 2015;216:1072
Nienhoff, H., Huebner, U., Frey, A., Przysucha, M., & Kimura, M. (2015). Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database. Studies in health technology and informatics, 2161072.
Nienhoff, Hendrik, et al. "Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database." Studies in health technology and informatics vol. 216 (2015): 1072.
Nienhoff H, Huebner U, Frey A, Przysucha M, Kimura M. Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database. Stud Health Technol Inform. 2015;216:1072. PMID: 26262371.
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SPANG: a SPARQL client supporting generation and reuse of queries for distributed RDF databases.

BMC bioinformatics

Chiba H, Uchiyama I.
PMID: 28178937
BMC Bioinformatics. 2017 Feb 08;18(1):93. doi: 10.1186/s12859-017-1531-1.

BACKGROUND: Toward improved interoperability of distributed biological databases, an increasing number of datasets have been published in the standardized Resource Description Framework (RDF). Although the powerful SPARQL Protocol and RDF Query Language (SPARQL) provides a basis for exploiting RDF...

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Chiba H, Uchiyama I. SPANG: a SPARQL client supporting generation and reuse of queries for distributed RDF databases. BMC Bioinformatics. 2017;18(1):93doi: 10.1186/s12859-017-1531-1.
Chiba, H., & Uchiyama, I. (2017). SPANG: a SPARQL client supporting generation and reuse of queries for distributed RDF databases. BMC bioinformatics, 18(1), 93. https://doi.org/10.1186/s12859-017-1531-1
Chiba, Hirokazu, and Uchiyama, Ikuo. "SPANG: a SPARQL client supporting generation and reuse of queries for distributed RDF databases." BMC bioinformatics vol. 18,1 (2017): 93. doi: https://doi.org/10.1186/s12859-017-1531-1
Chiba H, Uchiyama I. SPANG: a SPARQL client supporting generation and reuse of queries for distributed RDF databases. BMC Bioinformatics. 2017 Feb 08;18(1):93. doi: 10.1186/s12859-017-1531-1. PMID: 28178937; PMCID: PMC5299738.
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Comparison of ancient and modern Chinese based on complex weighted networks.

PloS one

Cui X, Qi J, Tan H, Chen F.
PMID: 29125870
PLoS One. 2017 Nov 10;12(11):e0187854. doi: 10.1371/journal.pone.0187854. eCollection 2017.

In this study, we compare statistical properties of ancient and modern Chinese within the framework of weighted complex networks. We examine two language networks based on different Chinese versions of the Records of the Grand Historian. The comparative results...

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Cui X, Qi J, Tan H, et al. Comparison of ancient and modern Chinese based on complex weighted networks. PLoS One. 2017;12(11):e0187854doi: 10.1371/journal.pone.0187854.
Cui, X., Qi, J., Tan, H., & Chen, F. (2017). Comparison of ancient and modern Chinese based on complex weighted networks. PloS one, 12(11), e0187854. https://doi.org/10.1371/journal.pone.0187854
Cui, Xinru, et al. "Comparison of ancient and modern Chinese based on complex weighted networks." PloS one vol. 12,11 (2017): e0187854. doi: https://doi.org/10.1371/journal.pone.0187854
Cui X, Qi J, Tan H, Chen F. Comparison of ancient and modern Chinese based on complex weighted networks. PLoS One. 2017 Nov 10;12(11):e0187854. doi: 10.1371/journal.pone.0187854. eCollection 2017. PMID: 29125870; PMCID: PMC5681291.
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Incidence and mortality of lung cancer: global trends and association with socioeconomic status.

Scientific reports

Wong MCS, Lao XQ, Ho KF, Goggins WB, Tse SLA.
PMID: 29085026
Sci Rep. 2017 Oct 30;7(1):14300. doi: 10.1038/s41598-017-14513-7.

We examined the correlation between lung cancer incidence/mortality and country-specific socioeconomic development, and evaluated its most recent global trends. We retrieved its age-standardized incidence rates from the GLOBOCAN database, and temporal patterns were assessed from global databases. We employed...

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Wong MCS, Lao XQ, Ho KF, et al. Incidence and mortality of lung cancer: global trends and association with socioeconomic status. Sci Rep. 2017;7(1):14300doi: 10.1038/s41598-017-14513-7.
Wong, M. C. S., Lao, X. Q., Ho, K. F., Goggins, W. B., & Tse, S. L. A. (2017). Incidence and mortality of lung cancer: global trends and association with socioeconomic status. Scientific reports, 7(1), 14300. https://doi.org/10.1038/s41598-017-14513-7
Wong, Martin C S, et al. "Incidence and mortality of lung cancer: global trends and association with socioeconomic status." Scientific reports vol. 7,1 (2017): 14300. doi: https://doi.org/10.1038/s41598-017-14513-7
Wong MCS, Lao XQ, Ho KF, Goggins WB, Tse SLA. Incidence and mortality of lung cancer: global trends and association with socioeconomic status. Sci Rep. 2017 Oct 30;7(1):14300. doi: 10.1038/s41598-017-14513-7. PMID: 29085026; PMCID: PMC5662733.
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Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities.

Genome medicine

Beckmann JS, Lew D.
PMID: 27993174
Genome Med. 2016 Dec 19;8(1):134. doi: 10.1186/s13073-016-0388-7.

This era of groundbreaking scientific developments in high-resolution, high-throughput technologies is allowing the cost-effective collection and analysis of huge, disparate datasets on individual health. Proper data mining and translation of the vast datasets into clinically actionable knowledge will require...

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Beckmann JS, Lew D. Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities. Genome Med. 2016;8(1):134doi: 10.1186/s13073-016-0388-7.
Beckmann, J. S., & Lew, D. (2016). Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities. Genome medicine, 8(1), 134. https://doi.org/10.1186/s13073-016-0388-7
Beckmann, Jacques S, and Lew, Daniel. "Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities." Genome medicine vol. 8,1 (2016): 134. doi: https://doi.org/10.1186/s13073-016-0388-7
Beckmann JS, Lew D. Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities. Genome Med. 2016 Dec 19;8(1):134. doi: 10.1186/s13073-016-0388-7. PMID: 27993174; PMCID: PMC5165712.
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BioCreative V CDR task corpus: a resource for chemical disease relation extraction.

Database : the journal of biological databases and curation

Li J, Sun Y, Johnson RJ, Sciaky D, Wei CH, Leaman R, Davis AP, Mattingly CJ, Wiegers TC, Lu Z.
PMID: 27161011
Database (Oxford). 2016 May 09;2016. doi: 10.1093/database/baw068. Print 2016.

Community-run, formal evaluations and manually annotated text corpora are critically important for advancing biomedical text-mining research. Recently in BioCreative V, a new challenge was organized for the tasks of disease named entity recognition (DNER) and chemical-induced disease (CID) relation...

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Li J, Sun Y, Johnson RJ, et al. BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database (Oxford). 2016;2016doi: 10.1093/database/baw068.
Li, J., Sun, Y., Johnson, R. J., Sciaky, D., Wei, C. H., Leaman, R., Davis, A. P., Mattingly, C. J., Wiegers, T. C., & Lu, Z. (2016). BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database : the journal of biological databases and curation, 2016. https://doi.org/10.1093/database/baw068
Li, Jiao, et al. "BioCreative V CDR task corpus: a resource for chemical disease relation extraction." Database : the journal of biological databases and curation vol. 2016 (2016). doi: https://doi.org/10.1093/database/baw068
Li J, Sun Y, Johnson RJ, Sciaky D, Wei CH, Leaman R, Davis AP, Mattingly CJ, Wiegers TC, Lu Z. BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database (Oxford). 2016 May 09;2016. doi: 10.1093/database/baw068. Print 2016. PMID: 27161011; PMCID: PMC4860626.
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Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data.

PloS one

Mudunuri US, Khouja M, Repetski S, Venkataraman G, Che A, Luke BT, Girard FP, Stephens RM.
PMID: 24312478
PLoS One. 2013 Dec 02;8(12):e80503. doi: 10.1371/journal.pone.0080503. eCollection 2013.

As the discipline of biomedical science continues to apply new technologies capable of producing unprecedented volumes of noisy and complex biological data, it has become evident that available methods for deriving meaningful information from such data are simply not...

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Mudunuri US, Khouja M, Repetski S, et al. Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data. PLoS One. 2013;8(12):e80503doi: 10.1371/journal.pone.0080503.
Mudunuri, U. S., Khouja, M., Repetski, S., Venkataraman, G., Che, A., Luke, B. T., Girard, F. P., & Stephens, R. M. (2013). Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data. PloS one, 8(12), e80503. https://doi.org/10.1371/journal.pone.0080503
Mudunuri, Uma S, et al. "Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data." PloS one vol. 8,12 (2013): e80503. doi: https://doi.org/10.1371/journal.pone.0080503
Mudunuri US, Khouja M, Repetski S, Venkataraman G, Che A, Luke BT, Girard FP, Stephens RM. Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data. PLoS One. 2013 Dec 02;8(12):e80503. doi: 10.1371/journal.pone.0080503. eCollection 2013. PMID: 24312478; PMCID: PMC3846626.
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Showing 13 to 24 of 1121 entries