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Fifty years of PULHHEEMS--the British Army's system of medical classification.

Annals of the Academy of Medicine, Singapore

Finnegan TP.
PMID: 11603147
Ann Acad Med Singap. 2001 Sep;30(5):556-7.

No abstract available.

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Finnegan TP. Fifty years of PULHHEEMS--the British Army's system of medical classification. Ann Acad Med Singap. 2001;30(5):556-7
Finnegan, T. P. (2001). Fifty years of PULHHEEMS--the British Army's system of medical classification. Annals of the Academy of Medicine, Singapore, 30(5), 556-7.
Finnegan, T P. "Fifty years of PULHHEEMS--the British Army's system of medical classification." Annals of the Academy of Medicine, Singapore vol. 30,5 (2001): 556-7.
Finnegan TP. Fifty years of PULHHEEMS--the British Army's system of medical classification. Ann Acad Med Singap. 2001 Sep;30(5):556-7. PMID: 11603147.
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A novel method for large tree visualization.

Bioinformatics (Oxford, England)

Heard J, Kaufmann W, Guan X.
PMID: 19129207
Bioinformatics. 2009 Feb 15;25(4):557-8. doi: 10.1093/bioinformatics/btn656. Epub 2009 Jan 07.

SUMMARY: Many genomic and proteomic analyses generate as a result a tree of genes or proteins. These trees are often large (containing tens of thousands of nodes and edges), and need a visualization tool to fully display all the...

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Heard J, Kaufmann W, Guan X. A novel method for large tree visualization. Bioinformatics. 2009;25(4):557-8doi: 10.1093/bioinformatics/btn656.
Heard, J., Kaufmann, W., & Guan, X. (2009). A novel method for large tree visualization. Bioinformatics (Oxford, England), 25(4), 557-8. https://doi.org/10.1093/bioinformatics/btn656
Heard, Jeff, et al. "A novel method for large tree visualization." Bioinformatics (Oxford, England) vol. 25,4 (2009): 557-8. doi: https://doi.org/10.1093/bioinformatics/btn656
Heard J, Kaufmann W, Guan X. A novel method for large tree visualization. Bioinformatics. 2009 Feb 15;25(4):557-8. doi: 10.1093/bioinformatics/btn656. Epub 2009 Jan 07. PMID: 19129207; PMCID: PMC2642634.
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Student perceptions regarding the usefulness of explicit discussion of "Structure of the Observed Learning Outcome" taxonomy.

Advances in physiology education

Prakash ES, Narayan KA, Sethuraman KR.
PMID: 20826769
Adv Physiol Educ. 2010 Sep;34(3):145-9. doi: 10.1152/advan.00026.2010.

One method of grading responses of the descriptive type is by using Structure of Observed Learning Outcomes (SOLO) taxonomy. The basis of this study was the expectation that if students were oriented to SOLO taxonomy, it would provide them...

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Prakash ES, Narayan KA, Sethuraman KR. Student perceptions regarding the usefulness of explicit discussion of "Structure of the Observed Learning Outcome" taxonomy. Adv Physiol Educ. 2010;34(3):145-9doi: 10.1152/advan.00026.2010.
Prakash, E. S., Narayan, K. A., & Sethuraman, K. R. (2010). Student perceptions regarding the usefulness of explicit discussion of "Structure of the Observed Learning Outcome" taxonomy. Advances in physiology education, 34(3), 145-9. https://doi.org/10.1152/advan.00026.2010
Prakash, E S, et al. "Student perceptions regarding the usefulness of explicit discussion of "Structure of the Observed Learning Outcome" taxonomy." Advances in physiology education vol. 34,3 (2010): 145-9. doi: https://doi.org/10.1152/advan.00026.2010
Prakash ES, Narayan KA, Sethuraman KR. Student perceptions regarding the usefulness of explicit discussion of "Structure of the Observed Learning Outcome" taxonomy. Adv Physiol Educ. 2010 Sep;34(3):145-9. doi: 10.1152/advan.00026.2010. PMID: 20826769.
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MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression.

BMC bioinformatics

Kim M, Zhang X, Ligo JG, Farnoud F, Veeravalli VV, Milenkovic O.
PMID: 26895947
BMC Bioinformatics. 2016 Feb 19;17:94. doi: 10.1186/s12859-016-0932-x.

BACKGROUND: Metagenomics is a genomics research discipline devoted to the study of microbial communities in environmental samples and human and animal organs and tissues. Sequenced metagenomic samples usually comprise reads from a large number of different bacterial communities and...

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Kim M, Zhang X, Ligo JG, et al. MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression. BMC Bioinformatics. 2016;17:94doi: 10.1186/s12859-016-0932-x.
Kim, M., Zhang, X., Ligo, J. G., Farnoud, F., Veeravalli, V. V., & Milenkovic, O. (2016). MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression. BMC bioinformatics, 1794. https://doi.org/10.1186/s12859-016-0932-x
Kim, Minji, et al. "MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression." BMC bioinformatics vol. 17 (2016): 94. doi: https://doi.org/10.1186/s12859-016-0932-x
Kim M, Zhang X, Ligo JG, Farnoud F, Veeravalli VV, Milenkovic O. MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression. BMC Bioinformatics. 2016 Feb 19;17:94. doi: 10.1186/s12859-016-0932-x. PMID: 26895947; PMCID: PMC4759986.
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MetaboAnalyst 2.0--a comprehensive server for metabolomic data analysis.

Nucleic acids research

Xia J, Mandal R, Sinelnikov IV, Broadhurst D, Wishart DS.
PMID: 22553367
Nucleic Acids Res. 2012 Jul;40:W127-33. doi: 10.1093/nar/gks374. Epub 2012 May 02.

First released in 2009, MetaboAnalyst (www.metaboanalyst.ca) was a relatively simple web server designed to facilitate metabolomic data processing and statistical analysis. With continuing advances in metabolomics along with constant user feedback, it became clear that a substantial upgrade to...

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Xia J, Mandal R, Sinelnikov IV, et al. MetaboAnalyst 2.0--a comprehensive server for metabolomic data analysis. Nucleic Acids Res. 2012;40:W127-33doi: 10.1093/nar/gks374.
Xia, J., Mandal, R., Sinelnikov, I. V., Broadhurst, D., & Wishart, D. S. (2012). MetaboAnalyst 2.0--a comprehensive server for metabolomic data analysis. Nucleic acids research, 40W127-33. https://doi.org/10.1093/nar/gks374
Xia, Jianguo, et al. "MetaboAnalyst 2.0--a comprehensive server for metabolomic data analysis." Nucleic acids research vol. 40 (2012): W127-33. doi: https://doi.org/10.1093/nar/gks374
Xia J, Mandal R, Sinelnikov IV, Broadhurst D, Wishart DS. MetaboAnalyst 2.0--a comprehensive server for metabolomic data analysis. Nucleic Acids Res. 2012 Jul;40:W127-33. doi: 10.1093/nar/gks374. Epub 2012 May 02. PMID: 22553367; PMCID: PMC3394314.
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Joint Ranking SVM and Binary Relevance with robust Low-rank learning for multi-label classification.

Neural networks : the official journal of the International Neural Network Society

Wu G, Zheng R, Tian Y, Liu D.
PMID: 31675625
Neural Netw. 2020 Feb;122:24-39. doi: 10.1016/j.neunet.2019.10.002. Epub 2019 Oct 18.

Multi-label classification studies the task where each example belongs to multiple labels simultaneously. As a representative method, Ranking Support Vector Machine (Rank-SVM) aims to minimize the Ranking Loss and can also mitigate the negative influence of the class-imbalance issue....

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Wu G, Zheng R, Tian Y, et al. Joint Ranking SVM and Binary Relevance with robust Low-rank learning for multi-label classification. Neural Netw. 2019;122:24-39doi: 10.1016/j.neunet.2019.10.002.
Wu, G., Zheng, R., Tian, Y., & Liu, D. (2020). Joint Ranking SVM and Binary Relevance with robust Low-rank learning for multi-label classification. Neural networks : the official journal of the International Neural Network Society, 12224-39. https://doi.org/10.1016/j.neunet.2019.10.002
Wu, Guoqiang, et al. "Joint Ranking SVM and Binary Relevance with robust Low-rank learning for multi-label classification." Neural networks : the official journal of the International Neural Network Society vol. 122 (2020): 24-39. doi: https://doi.org/10.1016/j.neunet.2019.10.002
Wu G, Zheng R, Tian Y, Liu D. Joint Ranking SVM and Binary Relevance with robust Low-rank learning for multi-label classification. Neural Netw. 2020 Feb;122:24-39. doi: 10.1016/j.neunet.2019.10.002. Epub 2019 Oct 18. PMID: 31675625.
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The Estimating effectiveness from efficacy taxonomy (EFFECT): A tool to estimate the real-world impact of health interventions.

Diabetes research and clinical practice

Galaviz KI, Ali MK, Haw JS, Magee MJ, Kowalski A, Wei J, Straus A, Weber MB, Vos T, Murray C, Narayan KMV.
PMID: 31152807
Diabetes Res Clin Pract. 2020 Jan;159:107751. doi: 10.1016/j.diabres.2019.05.030. Epub 2019 May 29.

AIMS: To develop and pilot test a taxonomy that empirically estimates health intervention effectiveness from efficacy data.METHODS: We developed a taxonomy to score health interventions across 11 items on a scale from 0-100. The taxonomy was pilot-tested in efficacy...

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Galaviz KI, Ali MK, Haw JS, et al. The Estimating effectiveness from efficacy taxonomy (EFFECT): A tool to estimate the real-world impact of health interventions. Diabetes Res Clin Pract. 2019;159:107751doi: 10.1016/j.diabres.2019.05.030.
Galaviz, K. I., Ali, M. K., Haw, J. S., Magee, M. J., Kowalski, A., Wei, J., Straus, A., Weber, M. B., Vos, T., Murray, C., & Narayan, K. M. V. (2020). The Estimating effectiveness from efficacy taxonomy (EFFECT): A tool to estimate the real-world impact of health interventions. Diabetes research and clinical practice, 159107751. https://doi.org/10.1016/j.diabres.2019.05.030
Galaviz, Karla I, et al. "The Estimating effectiveness from efficacy taxonomy (EFFECT): A tool to estimate the real-world impact of health interventions." Diabetes research and clinical practice vol. 159 (2020): 107751. doi: https://doi.org/10.1016/j.diabres.2019.05.030
Galaviz KI, Ali MK, Haw JS, Magee MJ, Kowalski A, Wei J, Straus A, Weber MB, Vos T, Murray C, Narayan KMV. The Estimating effectiveness from efficacy taxonomy (EFFECT): A tool to estimate the real-world impact of health interventions. Diabetes Res Clin Pract. 2020 Jan;159:107751. doi: 10.1016/j.diabres.2019.05.030. Epub 2019 May 29. PMID: 31152807; PMCID: PMC6883153.
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Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images.

Computational intelligence and neuroscience

Li Y, Wang J, Gao T, Sun Q, Zhang L, Tang M.
PMID: 32952545
Comput Intell Neurosci. 2020 Sep 01;2020:8886932. doi: 10.1155/2020/8886932. eCollection 2020.

To overcome the difficulty of automating and intelligently classifying the ground features in remote-sensing hyperspectral images, machine learning methods are gradually introduced into the process of remote-sensing imaging. First, the PaviaU, Botswana, and Cuprite hyperspectral datasets are selected as...

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Li Y, Wang J, Gao T, et al. Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images. Comput Intell Neurosci. 2020;2020:8886932doi: 10.1155/2020/8886932.
Li, Y., Wang, J., Gao, T., Sun, Q., Zhang, L., & Tang, M. (2020). Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images. Computational intelligence and neuroscience, 20208886932. https://doi.org/10.1155/2020/8886932
Li, Yanyi, et al. "Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images." Computational intelligence and neuroscience vol. 2020 (2020): 8886932. doi: https://doi.org/10.1155/2020/8886932
Li Y, Wang J, Gao T, Sun Q, Zhang L, Tang M. Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images. Comput Intell Neurosci. 2020 Sep 01;2020:8886932. doi: 10.1155/2020/8886932. eCollection 2020. PMID: 32952545; PMCID: PMC7482030.
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A methodology and theoretical taxonomy for centrality measures: What are the best centrality indicators for student networks?.

PloS one

Vignery K, Laurier W.
PMID: 33378341
PLoS One. 2020 Dec 30;15(12):e0244377. doi: 10.1371/journal.pone.0244377. eCollection 2020.

In order to understand and represent the importance of nodes within networks better, most of the studies that investigate graphs compute the nodes' centrality within their network(s) of interest. In the literature, the most frequent measures used are degree,...

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Vignery K, Laurier W. A methodology and theoretical taxonomy for centrality measures: What are the best centrality indicators for student networks?. PLoS One. 2020;15(12):e0244377doi: 10.1371/journal.pone.0244377.
Vignery, K., & Laurier, W. (2020). A methodology and theoretical taxonomy for centrality measures: What are the best centrality indicators for student networks?. PloS one, 15(12), e0244377. https://doi.org/10.1371/journal.pone.0244377
Vignery, Kristel, and Laurier, Wim. "A methodology and theoretical taxonomy for centrality measures: What are the best centrality indicators for student networks?." PloS one vol. 15,12 (2020): e0244377. doi: https://doi.org/10.1371/journal.pone.0244377
Vignery K, Laurier W. A methodology and theoretical taxonomy for centrality measures: What are the best centrality indicators for student networks?. PLoS One. 2020 Dec 30;15(12):e0244377. doi: 10.1371/journal.pone.0244377. eCollection 2020. PMID: 33378341; PMCID: PMC7773201.
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Reflections on the introduction of the Digital Protologue Database - A partial success?.

Systematic and applied microbiology

Rossello-Mora R, Sutcliffe IC.
PMID: 30554810
Syst Appl Microbiol. 2019 Jan;42(1):1-2. doi: 10.1016/j.syapm.2018.12.002. Epub 2018 Dec 10.

No abstract available.

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Rossello-Mora R, Sutcliffe IC. Reflections on the introduction of the Digital Protologue Database - A partial success?. Syst Appl Microbiol. 2018;42(1):1-2doi: 10.1016/j.syapm.2018.12.002.
Rossello-Mora, R., & Sutcliffe, I. C. (2019). Reflections on the introduction of the Digital Protologue Database - A partial success?. Systematic and applied microbiology, 42(1), 1-2. https://doi.org/10.1016/j.syapm.2018.12.002
Rossello-Mora, Ramon, and Sutcliffe, Iain C. "Reflections on the introduction of the Digital Protologue Database - A partial success?." Systematic and applied microbiology vol. 42,1 (2019): 1-2. doi: https://doi.org/10.1016/j.syapm.2018.12.002
Rossello-Mora R, Sutcliffe IC. Reflections on the introduction of the Digital Protologue Database - A partial success?. Syst Appl Microbiol. 2019 Jan;42(1):1-2. doi: 10.1016/j.syapm.2018.12.002. Epub 2018 Dec 10. PMID: 30554810.
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Swapping Birth and Death: Symmetries and Transformations in Phylodynamic Models.

Systematic biology

Stadler T, Steel M.
PMID: 31135030
Syst Biol. 2019 Sep 01;68(5):852-858. doi: 10.1093/sysbio/syz039.

Stochastic birth-death models provide the foundation for studying and simulating evolutionary trees in phylodynamics. A curious feature of such models is that they exhibit fundamental symmetries when the birth and death rates are interchanged. In this article, we first...

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Stadler T, Steel M. Swapping Birth and Death: Symmetries and Transformations in Phylodynamic Models. Syst Biol. 2019;68(5):852-858doi: 10.1093/sysbio/syz039.
Stadler, T., & Steel, M. (2019). Swapping Birth and Death: Symmetries and Transformations in Phylodynamic Models. Systematic biology, 68(5), 852-858. https://doi.org/10.1093/sysbio/syz039
Stadler, Tanja, and Steel, Mike. "Swapping Birth and Death: Symmetries and Transformations in Phylodynamic Models." Systematic biology vol. 68,5 (2019): 852-858. doi: https://doi.org/10.1093/sysbio/syz039
Stadler T, Steel M. Swapping Birth and Death: Symmetries and Transformations in Phylodynamic Models. Syst Biol. 2019 Sep 01;68(5):852-858. doi: 10.1093/sysbio/syz039. PMID: 31135030; PMCID: PMC6701459.
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The new International Classification of Diseases (ICD-10): the hypertension community needs a greater input.

Journal of clinical hypertension (Greenwich, Conn.)

Giles TD, Sander GE.
PMID: 22235815
J Clin Hypertens (Greenwich). 2012 Jan;14(1):1-2. doi: 10.1111/j.1751-7176.2011.00546.x.

No abstract available.

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Giles TD, Sander GE. The new International Classification of Diseases (ICD-10): the hypertension community needs a greater input. J Clin Hypertens (Greenwich). 2012;14(1):1-2doi: 10.1111/j.1751-7176.2011.00546.x.
Giles, T. D., & Sander, G. E. (2012). The new International Classification of Diseases (ICD-10): the hypertension community needs a greater input. Journal of clinical hypertension (Greenwich, Conn.), 14(1), 1-2. https://doi.org/10.1111/j.1751-7176.2011.00546.x
Giles, Thomas D, and Sander, Gary E. "The new International Classification of Diseases (ICD-10): the hypertension community needs a greater input." Journal of clinical hypertension (Greenwich, Conn.) vol. 14,1 (2012): 1-2. doi: https://doi.org/10.1111/j.1751-7176.2011.00546.x
Giles TD, Sander GE. The new International Classification of Diseases (ICD-10): the hypertension community needs a greater input. J Clin Hypertens (Greenwich). 2012 Jan;14(1):1-2. doi: 10.1111/j.1751-7176.2011.00546.x. PMID: 22235815.
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Showing 1 to 12 of 48 entries