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Minimum Technical Data Elements for Liquid Biopsy Data Submitted to Public Databases.

Clinical pharmacology and therapeutics

Febbo PG, Martin AM, Scher HI, Barrett JC, Beaver JA, Beresford PJ, Blumenthal GM, Bramlett K, Compton C, Dittamore R, Eberhard DA, Edelstein D, Godsey J, Gruen A, Hanlon SE, Hicks J, Hovelson D, Hullings M, Johann D, Johnson J, Kolatkar A, Kuhn P, Levine R, Martini JF, Miller DP, Moore C, Moy B, Pathak A, Philip R, Reese D, Royalty W, Ryder M, Sakul H, Salvatore LM, Schade A, Silvestro A, Simmons JK, Simons J, Singh Bhan S, Smalley MD, Somiari SB, Talasaz A, Tewari M, Tseng HR, Vinson J, Wells W, Welsh A, Grossman RL, Lee JSH, Leiman LC.
PMID: 32017048
Clin Pharmacol Ther. 2020 Apr;107(4):730-734. doi: 10.1002/cpt.1747. Epub 2020 Feb 04.

No abstract available.

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Febbo PG, Martin AM, Scher HI, et al. Minimum Technical Data Elements for Liquid Biopsy Data Submitted to Public Databases. Clin Pharmacol Ther. 2020;107(4):730-734doi: 10.1002/cpt.1747.
Febbo, P. G., Martin, A. M., Scher, H. I., Barrett, J. C., Beaver, J. A., Beresford, P. J., Blumenthal, G. M., Bramlett, K., Compton, C., Dittamore, R., Eberhard, D. A., Edelstein, D., Godsey, J., Gruen, A., Hanlon, S. E., Hicks, J., Hovelson, D., Hullings, M., Johann, D., Johnson, J., Kolatkar, A., Kuhn, P., Levine, R., Martini, J. F., Miller, D. P., Moore, C., Moy, B., Pathak, A., Philip, R., Reese, D., Royalty, W., Ryder, M., Sakul, H., Salvatore, L. M., Schade, A., Silvestro, A., Simmons, J. K., Simons, J., Singh Bhan, S., Smalley, M. D., Somiari, S. B., Talasaz, A., Tewari, M., Tseng, H. R., Vinson, J., Wells, W., Welsh, A., Grossman, R. L., Lee, J. S. H., & Leiman, L. C. (2020). Minimum Technical Data Elements for Liquid Biopsy Data Submitted to Public Databases. Clinical pharmacology and therapeutics, 107(4), 730-734. https://doi.org/10.1002/cpt.1747
Febbo, Phillip G, et al. "Minimum Technical Data Elements for Liquid Biopsy Data Submitted to Public Databases." Clinical pharmacology and therapeutics vol. 107,4 (2020): 730-734. doi: https://doi.org/10.1002/cpt.1747
Febbo PG, Martin AM, Scher HI, Barrett JC, Beaver JA, Beresford PJ, Blumenthal GM, Bramlett K, Compton C, Dittamore R, Eberhard DA, Edelstein D, Godsey J, Gruen A, Hanlon SE, Hicks J, Hovelson D, Hullings M, Johann D, Johnson J, Kolatkar A, Kuhn P, Levine R, Martini JF, Miller DP, Moore C, Moy B, Pathak A, Philip R, Reese D, Royalty W, Ryder M, Sakul H, Salvatore LM, Schade A, Silvestro A, Simmons JK, Simons J, Singh Bhan S, Smalley MD, Somiari SB, Talasaz A, Tewari M, Tseng HR, Vinson J, Wells W, Welsh A, Grossman RL, Lee JSH, Leiman LC. Minimum Technical Data Elements for Liquid Biopsy Data Submitted to Public Databases. Clin Pharmacol Ther. 2020 Apr;107(4):730-734. doi: 10.1002/cpt.1747. Epub 2020 Feb 04. PMID: 32017048; PMCID: PMC7158216.
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Next Steps in the Implementation of Learning Analytics in Medical Education: Consensus From an International Cohort of Medical Educators.

Journal of graduate medical education

Thoma B, Warm E, Hamstra SJ, Cavalcanti R, Pusic M, Shaw T, Verma A, Frank JR, Hauer KE.
PMID: 32595850
J Grad Med Educ. 2020 Jun;12(3):303-311. doi: 10.4300/JGME-D-19-00493.1.

BACKGROUND: With the implementation of competency-based assessment systems, education programs are collecting increasing amounts of data about medical learners. However, learning analytics are rarely employed to use this data to improve medical education.OBJECTIVE: We identified outstanding issues that are...

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Thoma B, Warm E, Hamstra SJ, et al. Next Steps in the Implementation of Learning Analytics in Medical Education: Consensus From an International Cohort of Medical Educators. J Grad Med Educ. 2020;12(3):303-311doi: 10.4300/JGME-D-19-00493.1.
Thoma, B., Warm, E., Hamstra, S. J., Cavalcanti, R., Pusic, M., Shaw, T., Verma, A., Frank, J. R., & Hauer, K. E. (2020). Next Steps in the Implementation of Learning Analytics in Medical Education: Consensus From an International Cohort of Medical Educators. Journal of graduate medical education, 12(3), 303-311. https://doi.org/10.4300/JGME-D-19-00493.1
Thoma, Brent, et al. "Next Steps in the Implementation of Learning Analytics in Medical Education: Consensus From an International Cohort of Medical Educators." Journal of graduate medical education vol. 12,3 (2020): 303-311. doi: https://doi.org/10.4300/JGME-D-19-00493.1
Thoma B, Warm E, Hamstra SJ, Cavalcanti R, Pusic M, Shaw T, Verma A, Frank JR, Hauer KE. Next Steps in the Implementation of Learning Analytics in Medical Education: Consensus From an International Cohort of Medical Educators. J Grad Med Educ. 2020 Jun;12(3):303-311. doi: 10.4300/JGME-D-19-00493.1. PMID: 32595850; PMCID: PMC7301933.
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Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices.

Methods in molecular biology (Clifton, N.J.)

Stanford NJ, Scharm M, Dobson PD, Golebiewski M, Hucka M, Kothamachu VB, Nickerson D, Owen S, Pahle J, Wittig U, Waltemath D, Goble C, Mendes P, Snoep J.
PMID: 31602618
Methods Mol Biol. 2019;2049:285-314. doi: 10.1007/978-1-4939-9736-7_17.

Computational systems biology involves integrating heterogeneous datasets in order to generate models. These models can assist with understanding and prediction of biological phenomena. Generating datasets and integrating them into models involves a wide range of scientific expertise. As a...

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Stanford NJ, Scharm M, Dobson PD, et al. Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices. Methods Mol Biol. 2019;2049:285-314doi: 10.1007/978-1-4939-9736-7_17.
Stanford, N. J., Scharm, M., Dobson, P. D., Golebiewski, M., Hucka, M., Kothamachu, V. B., Nickerson, D., Owen, S., Pahle, J., Wittig, U., Waltemath, D., Goble, C., Mendes, P., & Snoep, J. (2019). Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices. Methods in molecular biology (Clifton, N.J.), 2049285-314. https://doi.org/10.1007/978-1-4939-9736-7_17
Stanford, Natalie J, et al. "Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices." Methods in molecular biology (Clifton, N.J.) vol. 2049 (2019): 285-314. doi: https://doi.org/10.1007/978-1-4939-9736-7_17
Stanford NJ, Scharm M, Dobson PD, Golebiewski M, Hucka M, Kothamachu VB, Nickerson D, Owen S, Pahle J, Wittig U, Waltemath D, Goble C, Mendes P, Snoep J. Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices. Methods Mol Biol. 2019;2049:285-314. doi: 10.1007/978-1-4939-9736-7_17. PMID: 31602618.
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Software tools, databases and resources in metabolomics: updates from 2018 to 2019.

Metabolomics : Official journal of the Metabolomic Society

O'Shea K, Misra BB.
PMID: 32146531
Metabolomics. 2020 Mar 07;16(3):36. doi: 10.1007/s11306-020-01657-3.

Metabolomics has evolved as a discipline from a discovery and functional genomics tool, and is now a cornerstone in the era of big data-driven precision medicine. Sample preparation strategies and analytical technologies have seen enormous growth, and keeping pace...

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O'Shea K, Misra BB. Software tools, databases and resources in metabolomics: updates from 2018 to 2019. Metabolomics. 2020;16(3):36doi: 10.1007/s11306-020-01657-3.
O'Shea, K., & Misra, B. B. (2020). Software tools, databases and resources in metabolomics: updates from 2018 to 2019. Metabolomics : Official journal of the Metabolomic Society, 16(3), 36. https://doi.org/10.1007/s11306-020-01657-3
O'Shea, Keiron, and Misra, Biswapriya B. "Software tools, databases and resources in metabolomics: updates from 2018 to 2019." Metabolomics : Official journal of the Metabolomic Society vol. 16,3 (2020): 36. doi: https://doi.org/10.1007/s11306-020-01657-3
O'Shea K, Misra BB. Software tools, databases and resources in metabolomics: updates from 2018 to 2019. Metabolomics. 2020 Mar 07;16(3):36. doi: 10.1007/s11306-020-01657-3. PMID: 32146531.
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queryMed: Semantic Web functions for linking pharmacological and medical knowledge to data.

Bioinformatics (Oxford, England)

Rivault Y, Dameron O, Le Meur N.
PMID: 30657867
Bioinformatics. 2019 Sep 01;35(17):3203-3205. doi: 10.1093/bioinformatics/btz034.

SUMMARY: In public health research and more precisely in the reuse of electronic health data, selecting patients, identifying specific events and interpreting results typically requires biomedical knowledge. The queryMed R package aims to facilitate the integration of medical and...

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Rivault Y, Dameron O, Le Meur N. queryMed: Semantic Web functions for linking pharmacological and medical knowledge to data. Bioinformatics. 2019;35(17):3203-3205doi: 10.1093/bioinformatics/btz034.
Rivault, Y., Dameron, O., & Le Meur, N. (2019). queryMed: Semantic Web functions for linking pharmacological and medical knowledge to data. Bioinformatics (Oxford, England), 35(17), 3203-3205. https://doi.org/10.1093/bioinformatics/btz034
Rivault, Y, et al. "queryMed: Semantic Web functions for linking pharmacological and medical knowledge to data." Bioinformatics (Oxford, England) vol. 35,17 (2019): 3203-3205. doi: https://doi.org/10.1093/bioinformatics/btz034
Rivault Y, Dameron O, Le Meur N. queryMed: Semantic Web functions for linking pharmacological and medical knowledge to data. Bioinformatics. 2019 Sep 01;35(17):3203-3205. doi: 10.1093/bioinformatics/btz034. PMID: 30657867.
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Letter to the Editor: Suggestions for Rational Management of Nonalcoholic Fatty Liver Disease.

Hepatology (Baltimore, Md.)

Luo L, Zou H, Zheng S, Duan Z, Chen Y.
PMID: 31152594
Hepatology. 2019 Oct;70(4):1492-1493. doi: 10.1002/hep.30797.

No abstract available.

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Luo L, Zou H, Zheng S, et al. Letter to the Editor: Suggestions for Rational Management of Nonalcoholic Fatty Liver Disease. Hepatology. 2019;70(4):1492-1493doi: 10.1002/hep.30797.
Luo, L., Zou, H., Zheng, S., Duan, Z., & Chen, Y. (2019). Letter to the Editor: Suggestions for Rational Management of Nonalcoholic Fatty Liver Disease. Hepatology (Baltimore, Md.), 70(4), 1492-1493. https://doi.org/10.1002/hep.30797
Luo, Lei, et al. "Letter to the Editor: Suggestions for Rational Management of Nonalcoholic Fatty Liver Disease." Hepatology (Baltimore, Md.) vol. 70,4 (2019): 1492-1493. doi: https://doi.org/10.1002/hep.30797
Luo L, Zou H, Zheng S, Duan Z, Chen Y. Letter to the Editor: Suggestions for Rational Management of Nonalcoholic Fatty Liver Disease. Hepatology. 2019 Oct;70(4):1492-1493. doi: 10.1002/hep.30797. PMID: 31152594.
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Data Management and Integration with Electronic Health Record Systems.

Cardiac electrophysiology clinics

Serwer GA.
PMID: 34330374
Card Electrophysiol Clin. 2021 Sep;13(3):473-481. doi: 10.1016/j.ccep.2021.05.001.

Movement of information from a cardiac implantable electronic device to an electronic health record (EHR) can be a complex and multistep process. It requires unambiguous patient identification, device identification, standardized semantic and syntactic data nomenclature, common secure data transfer...

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Serwer GA. Data Management and Integration with Electronic Health Record Systems. Card Electrophysiol Clin. 2021;13(3):473-481doi: 10.1016/j.ccep.2021.05.001.
Serwer, G. A. (2021). Data Management and Integration with Electronic Health Record Systems. Cardiac electrophysiology clinics, 13(3), 473-481. https://doi.org/10.1016/j.ccep.2021.05.001
Serwer, Gerald A. "Data Management and Integration with Electronic Health Record Systems." Cardiac electrophysiology clinics vol. 13,3 (2021): 473-481. doi: https://doi.org/10.1016/j.ccep.2021.05.001
Serwer GA. Data Management and Integration with Electronic Health Record Systems. Card Electrophysiol Clin. 2021 Sep;13(3):473-481. doi: 10.1016/j.ccep.2021.05.001. PMID: 34330374.
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Data Management for Quality Improvement: How to Collect and Manage Data.

AACN advanced critical care

Au S, Murray E.
PMID: 34161966
AACN Adv Crit Care. 2021 Jun 15;32(2):213-218. doi: 10.4037/aacnacc2021118.

No abstract available.

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Au S, Murray E. Data Management for Quality Improvement: How to Collect and Manage Data. AACN Adv Crit Care. 2021;32(2):213-218doi: 10.4037/aacnacc2021118.
Au, S., & Murray, E. (2021). Data Management for Quality Improvement: How to Collect and Manage Data. AACN advanced critical care, 32(2), 213-218. https://doi.org/10.4037/aacnacc2021118
Au, Sandra, and Murray, Evan. "Data Management for Quality Improvement: How to Collect and Manage Data." AACN advanced critical care vol. 32,2 (2021): 213-218. doi: https://doi.org/10.4037/aacnacc2021118
Au S, Murray E. Data Management for Quality Improvement: How to Collect and Manage Data. AACN Adv Crit Care. 2021 Jun 15;32(2):213-218. doi: 10.4037/aacnacc2021118. PMID: 34161966.
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An Indoor Navigation Methodology for Mobile Devices by Integrating Augmented Reality and Semantic Web.

Sensors (Basel, Switzerland)

Rubio-Sandoval JI, Martinez-Rodriguez JL, Lopez-Arevalo I, Rios-Alvarado AB, Rodriguez-Rodriguez AJ, Vargas-Requena DT.
PMID: 34450877
Sensors (Basel). 2021 Aug 12;21(16). doi: 10.3390/s21165435.

Indoor navigation systems incorporating augmented reality allow users to locate places within buildings and acquire more knowledge about their environment. However, although diverse works have been introduced with varied technologies, infrastructure, and functionalities, a standardization of the procedures for...

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Rubio-Sandoval JI, Martinez-Rodriguez JL, Lopez-Arevalo I, et al. An Indoor Navigation Methodology for Mobile Devices by Integrating Augmented Reality and Semantic Web. Sensors (Basel). 2021;21(16)doi: 10.3390/s21165435.
Rubio-Sandoval, J. I., Martinez-Rodriguez, J. L., Lopez-Arevalo, I., Rios-Alvarado, A. B., Rodriguez-Rodriguez, A. J., & Vargas-Requena, D. T. (2021). An Indoor Navigation Methodology for Mobile Devices by Integrating Augmented Reality and Semantic Web. Sensors (Basel, Switzerland), 21(16), . https://doi.org/10.3390/s21165435
Rubio-Sandoval, Jesus Ivan, et al. "An Indoor Navigation Methodology for Mobile Devices by Integrating Augmented Reality and Semantic Web." Sensors (Basel, Switzerland) vol. 21,16 (2021). doi: https://doi.org/10.3390/s21165435
Rubio-Sandoval JI, Martinez-Rodriguez JL, Lopez-Arevalo I, Rios-Alvarado AB, Rodriguez-Rodriguez AJ, Vargas-Requena DT. An Indoor Navigation Methodology for Mobile Devices by Integrating Augmented Reality and Semantic Web. Sensors (Basel). 2021 Aug 12;21(16). doi: 10.3390/s21165435. PMID: 34450877; PMCID: PMC8401022.
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Exploring Unsupervised Query Paraphrasing to Identify Relevant Search Phrases for a Literature Review.

Studies in health technology and informatics

Peltonen LM, Tommila M, Moen H.
PMID: 32604694
Stud Health Technol Inform. 2020 Jun 26;272:429-432. doi: 10.3233/SHTI200587.

Literature databases have multifaceted search options, but emerging research areas do not have an established terminology and therefore it is difficult to find relevant literature when conducting a review. This study aimed to explore if an unsupervised paraphrasing approach...

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Peltonen LM, Tommila M, Moen H. Exploring Unsupervised Query Paraphrasing to Identify Relevant Search Phrases for a Literature Review. Stud Health Technol Inform. 2020;272:429-432doi: 10.3233/SHTI200587.
Peltonen, L. M., Tommila, M., & Moen, H. (2020). Exploring Unsupervised Query Paraphrasing to Identify Relevant Search Phrases for a Literature Review. Studies in health technology and informatics, 272429-432. https://doi.org/10.3233/SHTI200587
Peltonen, Laura-Maria, et al. "Exploring Unsupervised Query Paraphrasing to Identify Relevant Search Phrases for a Literature Review." Studies in health technology and informatics vol. 272 (2020): 429-432. doi: https://doi.org/10.3233/SHTI200587
Peltonen LM, Tommila M, Moen H. Exploring Unsupervised Query Paraphrasing to Identify Relevant Search Phrases for a Literature Review. Stud Health Technol Inform. 2020 Jun 26;272:429-432. doi: 10.3233/SHTI200587. PMID: 32604694.
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Active semi-supervised learning for biological data classification.

PloS one

Camargo G, Bugatti PH, Saito PTM.
PMID: 32813738
PLoS One. 2020 Aug 19;15(8):e0237428. doi: 10.1371/journal.pone.0237428. eCollection 2020.

Due to datasets have continuously grown, efforts have been performed in the attempt to solve the problem related to the large amount of unlabeled data in disproportion to the scarcity of labeled data. Another important issue is related to...

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Camargo G, Bugatti PH, Saito PTM. Active semi-supervised learning for biological data classification. PLoS One. 2020;15(8):e0237428doi: 10.1371/journal.pone.0237428.
Camargo, G., Bugatti, P. H., & Saito, P. T. M. (2020). Active semi-supervised learning for biological data classification. PloS one, 15(8), e0237428. https://doi.org/10.1371/journal.pone.0237428
Camargo, Guilherme, et al. "Active semi-supervised learning for biological data classification." PloS one vol. 15,8 (2020): e0237428. doi: https://doi.org/10.1371/journal.pone.0237428
Camargo G, Bugatti PH, Saito PTM. Active semi-supervised learning for biological data classification. PLoS One. 2020 Aug 19;15(8):e0237428. doi: 10.1371/journal.pone.0237428. eCollection 2020. PMID: 32813738; PMCID: PMC7437865.
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Mobile application for the evaluation and planning of nursing workload in the intensive care unit.

International journal of medical informatics

da Silva R, Baptista A, Serra RL, Magalhães DSF.
PMID: 32179255
Int J Med Inform. 2020 May;137:104120. doi: 10.1016/j.ijmedinf.2020.104120. Epub 2020 Mar 09.

OBJECTIVE: In this study, we present an application type software which employs the Nursing Activities Score (NAS), a management tool for measuring nursing workload prospectively.METHOD: The system was developed in two modules: WEB (controlled from an Internet browser) for...

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da Silva R, Baptista A, Serra RL, et al. Mobile application for the evaluation and planning of nursing workload in the intensive care unit. Int J Med Inform. 2020;137:104120doi: 10.1016/j.ijmedinf.2020.104120.
da Silva, R., Baptista, A., Serra, R. L., & Magalhães, D. S. F. (2020). Mobile application for the evaluation and planning of nursing workload in the intensive care unit. International journal of medical informatics, 137104120. https://doi.org/10.1016/j.ijmedinf.2020.104120
da Silva, Rosilene, et al. "Mobile application for the evaluation and planning of nursing workload in the intensive care unit." International journal of medical informatics vol. 137 (2020): 104120. doi: https://doi.org/10.1016/j.ijmedinf.2020.104120
da Silva R, Baptista A, Serra RL, Magalhães DSF. Mobile application for the evaluation and planning of nursing workload in the intensive care unit. Int J Med Inform. 2020 May;137:104120. doi: 10.1016/j.ijmedinf.2020.104120. Epub 2020 Mar 09. PMID: 32179255.
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Showing 1 to 12 of 71 entries