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CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer.

Nature genetics

Griffith M, Spies NC, Krysiak K, McMichael JF, Coffman AC, Danos AM, Ainscough BJ, Ramirez CA, Rieke DT, Kujan L, Barnell EK, Wagner AH, Skidmore ZL, Wollam A, Liu CJ, Jones MR, Bilski RL, Lesurf R, Feng YY, Shah NM, Bonakdar M, Trani L, Matlock M, Ramu A, Campbell KM, Spies GC, Graubert AP, Gangavarapu K, Eldred JM, Larson DE, Walker JR, Good BM, Wu C, Su AI, Dienstmann R, Margolin AA, Tamborero D, Lopez-Bigas N, Jones SJ, Bose R, Spencer DH, Wartman LD, Wilson RK, Mardis ER, Griffith OL.
PMID: 28138153
Nat Genet. 2017 Jan 31;49(2):170-174. doi: 10.1038/ng.3774.

No abstract available.

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Griffith M, Spies NC, Krysiak K, et al. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat Genet. 2017;49(2):170-174doi: 10.1038/ng.3774.
Griffith, M., Spies, N. C., Krysiak, K., McMichael, J. F., Coffman, A. C., Danos, A. M., Ainscough, B. J., Ramirez, C. A., Rieke, D. T., Kujan, L., Barnell, E. K., Wagner, A. H., Skidmore, Z. L., Wollam, A., Liu, C. J., Jones, M. R., Bilski, R. L., Lesurf, R., Feng, Y. Y., Shah, N. M., Bonakdar, M., Trani, L., Matlock, M., Ramu, A., Campbell, K. M., Spies, G. C., Graubert, A. P., Gangavarapu, K., Eldred, J. M., Larson, D. E., Walker, J. R., Good, B. M., Wu, C., Su, A. I., Dienstmann, R., Margolin, A. A., Tamborero, D., Lopez-Bigas, N., Jones, S. J., Bose, R., Spencer, D. H., Wartman, L. D., Wilson, R. K., Mardis, E. R., & Griffith, O. L. (2017). CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nature genetics, 49(2), 170-174. https://doi.org/10.1038/ng.3774
Griffith, Malachi, et al. "CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer." Nature genetics vol. 49,2 (2017): 170-174. doi: https://doi.org/10.1038/ng.3774
Griffith M, Spies NC, Krysiak K, McMichael JF, Coffman AC, Danos AM, Ainscough BJ, Ramirez CA, Rieke DT, Kujan L, Barnell EK, Wagner AH, Skidmore ZL, Wollam A, Liu CJ, Jones MR, Bilski RL, Lesurf R, Feng YY, Shah NM, Bonakdar M, Trani L, Matlock M, Ramu A, Campbell KM, Spies GC, Graubert AP, Gangavarapu K, Eldred JM, Larson DE, Walker JR, Good BM, Wu C, Su AI, Dienstmann R, Margolin AA, Tamborero D, Lopez-Bigas N, Jones SJ, Bose R, Spencer DH, Wartman LD, Wilson RK, Mardis ER, Griffith OL. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat Genet. 2017 Jan 31;49(2):170-174. doi: 10.1038/ng.3774. PMID: 28138153; PMCID: PMC5367263.
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Semantic Data Integration and Knowledge Management to Represent Biological Network Associations.

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

Losko S, Heumann K.
PMID: 28849570
Methods Mol Biol. 2017;1613:403-423. doi: 10.1007/978-1-4939-7027-8_16.

The vast quantities of information generated by academic and industrial research groups are reflected in a rapidly growing body of scientific literature and exponentially expanding resources of formalized data, including experimental data, originating from a multitude of "-omics" platforms,...

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Losko S, Heumann K. Semantic Data Integration and Knowledge Management to Represent Biological Network Associations. Methods Mol Biol. 2017;1613:403-423doi: 10.1007/978-1-4939-7027-8_16.
Losko, S., & Heumann, K. (2017). Semantic Data Integration and Knowledge Management to Represent Biological Network Associations. Methods in molecular biology (Clifton, N.J.), 1613403-423. https://doi.org/10.1007/978-1-4939-7027-8_16
Losko, Sascha, and Heumann, Klaus. "Semantic Data Integration and Knowledge Management to Represent Biological Network Associations." Methods in molecular biology (Clifton, N.J.) vol. 1613 (2017): 403-423. doi: https://doi.org/10.1007/978-1-4939-7027-8_16
Losko S, Heumann K. Semantic Data Integration and Knowledge Management to Represent Biological Network Associations. Methods Mol Biol. 2017;1613:403-423. doi: 10.1007/978-1-4939-7027-8_16. PMID: 28849570.
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Climate information for public health: the role of the IRI climate data library in an integrated knowledge system.

Geospatial health

del Corral J, Blumenthal MB, Mantilla G, Ceccato P, Connor SJ, Thomson MC.
PMID: 23032279
Geospat Health. 2012 Sep;6(3):S15-24. doi: 10.4081/gh.2012.118.

Public health professionals are increasingly concerned about the potential impact of climate variability and change on health outcomes. Protecting public health from the vagaries of climate requires new working relationships between the public health sector and the providers of...

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del Corral J, Blumenthal MB, Mantilla G, et al. Climate information for public health: the role of the IRI climate data library in an integrated knowledge system. Geospat Health. 2012;6(3):S15-24doi: 10.4081/gh.2012.118.
del Corral, J., Blumenthal, M. B., Mantilla, G., Ceccato, P., Connor, S. J., & Thomson, M. C. (2012). Climate information for public health: the role of the IRI climate data library in an integrated knowledge system. Geospatial health, 6(3), S15-24. https://doi.org/10.4081/gh.2012.118
del Corral, John, et al. "Climate information for public health: the role of the IRI climate data library in an integrated knowledge system." Geospatial health vol. 6,3 (2012): S15-24. doi: https://doi.org/10.4081/gh.2012.118
del Corral J, Blumenthal MB, Mantilla G, Ceccato P, Connor SJ, Thomson MC. Climate information for public health: the role of the IRI climate data library in an integrated knowledge system. Geospat Health. 2012 Sep;6(3):S15-24. doi: 10.4081/gh.2012.118. PMID: 23032279.
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Network-Based Approaches for Multi-omics Integration.

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

Zhou G, Li S, Xia J.
PMID: 31953831
Methods Mol Biol. 2020;2104:469-487. doi: 10.1007/978-1-0716-0239-3_23.

Network-based approach is rapidly emerging as a promising strategy to integrate and interpret different -omics datasets, including metabolomics. The first section of this chapter introduces the current progresses and main concepts in multi-omics integration. The second section provides an...

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Zhou G, Li S, Xia J. Network-Based Approaches for Multi-omics Integration. Methods Mol Biol. 2020;2104:469-487doi: 10.1007/978-1-0716-0239-3_23.
Zhou, G., Li, S., & Xia, J. (2020). Network-Based Approaches for Multi-omics Integration. Methods in molecular biology (Clifton, N.J.), 2104469-487. https://doi.org/10.1007/978-1-0716-0239-3_23
Zhou, Guangyan, et al. "Network-Based Approaches for Multi-omics Integration." Methods in molecular biology (Clifton, N.J.) vol. 2104 (2020): 469-487. doi: https://doi.org/10.1007/978-1-0716-0239-3_23
Zhou G, Li S, Xia J. Network-Based Approaches for Multi-omics Integration. Methods Mol Biol. 2020;2104:469-487. doi: 10.1007/978-1-0716-0239-3_23. PMID: 31953831.
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ROBOKOP KG and KGB: Integrated Knowledge Graphs from Federated Sources.

Journal of chemical information and modeling

Bizon C, Cox S, Balhoff J, Kebede Y, Wang P, Morton K, Fecho K, Tropsha A.
PMID: 31769676
J Chem Inf Model. 2019 Dec 23;59(12):4968-4973. doi: 10.1021/acs.jcim.9b00683. Epub 2019 Dec 12.

A proliferation of data sources has led to the notional existence of an implicit Knowledge Graph (KG) that contains vast amounts of biological knowledge contributed by distributed Application Programming Interfaces (APIs). However, challenges arise when integrating data across multiple...

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Bizon C, Cox S, Balhoff J, et al. ROBOKOP KG and KGB: Integrated Knowledge Graphs from Federated Sources. J Chem Inf Model. 2019;59(12):4968-4973doi: 10.1021/acs.jcim.9b00683.
Bizon, C., Cox, S., Balhoff, J., Kebede, Y., Wang, P., Morton, K., Fecho, K., & Tropsha, A. (2019). ROBOKOP KG and KGB: Integrated Knowledge Graphs from Federated Sources. Journal of chemical information and modeling, 59(12), 4968-4973. https://doi.org/10.1021/acs.jcim.9b00683
Bizon, Chris, et al. "ROBOKOP KG and KGB: Integrated Knowledge Graphs from Federated Sources." Journal of chemical information and modeling vol. 59,12 (2019): 4968-4973. doi: https://doi.org/10.1021/acs.jcim.9b00683
Bizon C, Cox S, Balhoff J, Kebede Y, Wang P, Morton K, Fecho K, Tropsha A. ROBOKOP KG and KGB: Integrated Knowledge Graphs from Federated Sources. J Chem Inf Model. 2019 Dec 23;59(12):4968-4973. doi: 10.1021/acs.jcim.9b00683. Epub 2019 Dec 12. PMID: 31769676.
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Unknown Disease Outbreaks Detection: A Pilot Study on Feature-Based Knowledge Representation and Reasoning Model.

Frontiers in public health

Feng R, Hu Q, Jiang Y.
PMID: 34055732
Front Public Health. 2021 May 13;9:683855. doi: 10.3389/fpubh.2021.683855. eCollection 2021.

No abstract available.

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Feng R, Hu Q, Jiang Y. Unknown Disease Outbreaks Detection: A Pilot Study on Feature-Based Knowledge Representation and Reasoning Model. Front Public Health. 2021;9:683855doi: 10.3389/fpubh.2021.683855.
Feng, R., Hu, Q., & Jiang, Y. (2021). Unknown Disease Outbreaks Detection: A Pilot Study on Feature-Based Knowledge Representation and Reasoning Model. Frontiers in public health, 9683855. https://doi.org/10.3389/fpubh.2021.683855
Feng, Rui, et al. "Unknown Disease Outbreaks Detection: A Pilot Study on Feature-Based Knowledge Representation and Reasoning Model." Frontiers in public health vol. 9 (2021): 683855. doi: https://doi.org/10.3389/fpubh.2021.683855
Feng R, Hu Q, Jiang Y. Unknown Disease Outbreaks Detection: A Pilot Study on Feature-Based Knowledge Representation and Reasoning Model. Front Public Health. 2021 May 13;9:683855. doi: 10.3389/fpubh.2021.683855. eCollection 2021. PMID: 34055732; PMCID: PMC8155365.
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The Age of Hubris.

The British journal of general practice : the journal of the Royal College of General Practitioners

Miller S.
PMID: 32217597
Br J Gen Pract. 2020 Mar 26;70(693):194. doi: 10.3399/bjgp20X709217. Print 2020 Apr.

No abstract available.

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Miller S. The Age of Hubris. Br J Gen Pract. 2020;70(693):194doi: 10.3399/bjgp20X709217.
Miller, S. (2020). The Age of Hubris. The British journal of general practice : the journal of the Royal College of General Practitioners, 70(693), 194. https://doi.org/10.3399/bjgp20X709217
Miller, Saul. "The Age of Hubris." The British journal of general practice : the journal of the Royal College of General Practitioners vol. 70,693 (2020): 194. doi: https://doi.org/10.3399/bjgp20X709217
Miller S. The Age of Hubris. Br J Gen Pract. 2020 Mar 26;70(693):194. doi: 10.3399/bjgp20X709217. Print 2020 Apr. PMID: 32217597; PMCID: PMC7098520.
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Biomedical Knowledge Graphs Construction From Conditional Statements.

IEEE/ACM transactions on computational biology and bioinformatics

Jiang T, Zeng Q, Zhao T, Qin B, Liu T, Chawla NV, Jiang M.
PMID: 32167907
IEEE/ACM Trans Comput Biol Bioinform. 2021 May-Jun;18(3):823-835. doi: 10.1109/TCBB.2020.2979959. Epub 2021 Jun 03.

Conditions play an essential role in biomedical statements. However, existing biomedical knowledge graphs (BioKGs) only focus on factual knowledge, organized as a flat relational network of biomedical concepts. These BioKGs ignore the conditions of the facts being valid, which...

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Jiang T, Zeng Q, Zhao T, et al. Biomedical Knowledge Graphs Construction From Conditional Statements. IEEE/ACM Trans Comput Biol Bioinform. 2021;18(3):823-835doi: 10.1109/TCBB.2020.2979959.
Jiang, T., Zeng, Q., Zhao, T., Qin, B., Liu, T., Chawla, N. V., & Jiang, M. (2021). Biomedical Knowledge Graphs Construction From Conditional Statements. IEEE/ACM transactions on computational biology and bioinformatics, 18(3), 823-835. https://doi.org/10.1109/TCBB.2020.2979959
Jiang, Tianwen, et al. "Biomedical Knowledge Graphs Construction From Conditional Statements." IEEE/ACM transactions on computational biology and bioinformatics vol. 18,3 (2021): 823-835. doi: https://doi.org/10.1109/TCBB.2020.2979959
Jiang T, Zeng Q, Zhao T, Qin B, Liu T, Chawla NV, Jiang M. Biomedical Knowledge Graphs Construction From Conditional Statements. IEEE/ACM Trans Comput Biol Bioinform. 2021 May-Jun;18(3):823-835. doi: 10.1109/TCBB.2020.2979959. Epub 2021 Jun 03. PMID: 32167907.
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Evidence-Based Health Intelligence with Globally Localized Epidemic Knowledge Base: Merging Pathological Data, Socio-Environmental Data and Intervention Knowledge Data.

Studies in health technology and informatics

Shin EK, Lee JH.
PMID: 32604589
Stud Health Technol Inform. 2020 Jun 26;272:17-20. doi: 10.3233/SHTI200482.

The increased prevalence and frequency of infectious diseases are alarming with respect to the disproportionate fatalities across different regions, socio-economic conditions, and demographic groups. Combining pathological data, socio-environmental data, and extracted knowledge from white papers, we proposed a Globally...

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Shin EK, Lee JH. Evidence-Based Health Intelligence with Globally Localized Epidemic Knowledge Base: Merging Pathological Data, Socio-Environmental Data and Intervention Knowledge Data. Stud Health Technol Inform. 2020;272:17-20doi: 10.3233/SHTI200482.
Shin, E. K., & Lee, J. H. (2020). Evidence-Based Health Intelligence with Globally Localized Epidemic Knowledge Base: Merging Pathological Data, Socio-Environmental Data and Intervention Knowledge Data. Studies in health technology and informatics, 27217-20. https://doi.org/10.3233/SHTI200482
Shin, Eun Kyong, and Lee, Jong Hun. "Evidence-Based Health Intelligence with Globally Localized Epidemic Knowledge Base: Merging Pathological Data, Socio-Environmental Data and Intervention Knowledge Data." Studies in health technology and informatics vol. 272 (2020): 17-20. doi: https://doi.org/10.3233/SHTI200482
Shin EK, Lee JH. Evidence-Based Health Intelligence with Globally Localized Epidemic Knowledge Base: Merging Pathological Data, Socio-Environmental Data and Intervention Knowledge Data. Stud Health Technol Inform. 2020 Jun 26;272:17-20. doi: 10.3233/SHTI200482. PMID: 32604589.
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Ophthatome™: an integrated knowledgebase of ophthalmic diseases for translating vision research into the clinic.

BMC ophthalmology

Raj P, Tejwani S, Sudha D, Muthu Narayanan B, Thangapandi C, Das S, Somasekar J, Mangalapudi S, Kumar D, Pindipappanahalli N, Shetty R, Ghosh A, Kumaramanickavel G, Chaudhuri A, Soumittra N.
PMID: 33172432
BMC Ophthalmol. 2020 Nov 10;20(1):442. doi: 10.1186/s12886-020-01705-5.

BACKGROUND: Medical big data analytics has revolutionized the human healthcare system by introducing processes that facilitate rationale clinical decision making, predictive or prognostic modelling of the disease progression and management, disease surveillance, overall impact on public health and research....

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Raj P, Tejwani S, Sudha D, et al. Ophthatome™: an integrated knowledgebase of ophthalmic diseases for translating vision research into the clinic. BMC Ophthalmol. 2020;20(1):442doi: 10.1186/s12886-020-01705-5.
Raj, P., Tejwani, S., Sudha, D., Muthu Narayanan, B., Thangapandi, C., Das, S., Somasekar, J., Mangalapudi, S., Kumar, D., Pindipappanahalli, N., Shetty, R., Ghosh, A., Kumaramanickavel, G., Chaudhuri, A., & Soumittra, N. (2020). Ophthatome™: an integrated knowledgebase of ophthalmic diseases for translating vision research into the clinic. BMC ophthalmology, 20(1), 442. https://doi.org/10.1186/s12886-020-01705-5
Raj, Praveen, et al. "Ophthatome™: an integrated knowledgebase of ophthalmic diseases for translating vision research into the clinic." BMC ophthalmology vol. 20,1 (2020): 442. doi: https://doi.org/10.1186/s12886-020-01705-5
Raj P, Tejwani S, Sudha D, Muthu Narayanan B, Thangapandi C, Das S, Somasekar J, Mangalapudi S, Kumar D, Pindipappanahalli N, Shetty R, Ghosh A, Kumaramanickavel G, Chaudhuri A, Soumittra N. Ophthatome™: an integrated knowledgebase of ophthalmic diseases for translating vision research into the clinic. BMC Ophthalmol. 2020 Nov 10;20(1):442. doi: 10.1186/s12886-020-01705-5. PMID: 33172432; PMCID: PMC7656766.
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Unsupervised cross-domain named entity recognition using entity-aware adversarial training.

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

Peng Q, Zheng C, Cai Y, Wang T, Xie H, Li Q.
PMID: 33631608
Neural Netw. 2021 Jun;138:68-77. doi: 10.1016/j.neunet.2020.12.027. Epub 2020 Dec 31.

The success of neural network based methods in named entity recognition (NER) is heavily relied on abundant manual labeled data. However, these NER methods are unavailable when the data is fully-unlabeled in a new domain. To address the problem,...

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Peng Q, Zheng C, Cai Y, et al. Unsupervised cross-domain named entity recognition using entity-aware adversarial training. Neural Netw. 2020;138:68-77doi: 10.1016/j.neunet.2020.12.027.
Peng, Q., Zheng, C., Cai, Y., Wang, T., Xie, H., & Li, Q. (2021). Unsupervised cross-domain named entity recognition using entity-aware adversarial training. Neural networks : the official journal of the International Neural Network Society, 13868-77. https://doi.org/10.1016/j.neunet.2020.12.027
Peng, Qi, et al. "Unsupervised cross-domain named entity recognition using entity-aware adversarial training." Neural networks : the official journal of the International Neural Network Society vol. 138 (2021): 68-77. doi: https://doi.org/10.1016/j.neunet.2020.12.027
Peng Q, Zheng C, Cai Y, Wang T, Xie H, Li Q. Unsupervised cross-domain named entity recognition using entity-aware adversarial training. Neural Netw. 2021 Jun;138:68-77. doi: 10.1016/j.neunet.2020.12.027. Epub 2020 Dec 31. PMID: 33631608.
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SequencEnG: an interactive knowledge base of sequencing techniques.

Bioinformatics (Oxford, England)

Zhang Y, Manjunath M, Kim Y, Heintz J, Song JS.
PMID: 30202870
Bioinformatics. 2019 Apr 15;35(8):1438-1440. doi: 10.1093/bioinformatics/bty794.

SUMMARY: Next-generation sequencing (NGS) techniques are revolutionizing biomedical research by providing powerful methods for generating genomic and epigenomic profiles. The rapid progress is posing an acute challenge to students and researchers to stay acquainted with the numerous available methods....

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Zhang Y, Manjunath M, Kim Y, et al. SequencEnG: an interactive knowledge base of sequencing techniques. Bioinformatics. 2019;35(8):1438-1440doi: 10.1093/bioinformatics/bty794.
Zhang, Y., Manjunath, M., Kim, Y., Heintz, J., & Song, J. S. (2019). SequencEnG: an interactive knowledge base of sequencing techniques. Bioinformatics (Oxford, England), 35(8), 1438-1440. https://doi.org/10.1093/bioinformatics/bty794
Zhang, Yi, et al. "SequencEnG: an interactive knowledge base of sequencing techniques." Bioinformatics (Oxford, England) vol. 35,8 (2019): 1438-1440. doi: https://doi.org/10.1093/bioinformatics/bty794
Zhang Y, Manjunath M, Kim Y, Heintz J, Song JS. SequencEnG: an interactive knowledge base of sequencing techniques. Bioinformatics. 2019 Apr 15;35(8):1438-1440. doi: 10.1093/bioinformatics/bty794. PMID: 30202870; PMCID: PMC6478014.
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Showing 13 to 24 of 97 entries