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Showing 13 to 24 of 77 entries
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Potential molecular mechanism of ACE gene at different time points in STEMI patients based on genome-wide microarray dataset.

Lipids in health and disease

Guan YZ, Yin RX, Zheng PF, Deng GX, Liu CX, Wei BL.
PMID: 31647035
Lipids Health Dis. 2019 Oct 23;18(1):184. doi: 10.1186/s12944-019-1131-3.

BACKGROUND: This study aimed to investigate the angiotensin converting enzyme (ACE) co-expression genes and their pathways involved in ST-segment elevation myocardial infarction (STEMI) at different time points.METHODS: The array data set of GSE59867 was examined for the ACE co-expression...

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Guan YZ, Yin RX, Zheng PF, et al. Potential molecular mechanism of ACE gene at different time points in STEMI patients based on genome-wide microarray dataset. Lipids Health Dis. 2019;18(1):184doi: 10.1186/s12944-019-1131-3.
Guan, Y. Z., Yin, R. X., Zheng, P. F., Deng, G. X., Liu, C. X., & Wei, B. L. (2019). Potential molecular mechanism of ACE gene at different time points in STEMI patients based on genome-wide microarray dataset. Lipids in health and disease, 18(1), 184. https://doi.org/10.1186/s12944-019-1131-3
Guan, Yao-Zong, et al. "Potential molecular mechanism of ACE gene at different time points in STEMI patients based on genome-wide microarray dataset." Lipids in health and disease vol. 18,1 (2019): 184. doi: https://doi.org/10.1186/s12944-019-1131-3
Guan YZ, Yin RX, Zheng PF, Deng GX, Liu CX, Wei BL. Potential molecular mechanism of ACE gene at different time points in STEMI patients based on genome-wide microarray dataset. Lipids Health Dis. 2019 Oct 23;18(1):184. doi: 10.1186/s12944-019-1131-3. PMID: 31647035; PMCID: PMC6813054.
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FuncTree2: an interactive radial tree for functional hierarchies and omics data visualization.

Bioinformatics (Oxford, England)

Darzi Y, Yamate Y, Yamada T.
PMID: 31004476
Bioinformatics. 2019 Nov 01;35(21):4519-4521. doi: 10.1093/bioinformatics/btz245.

SUMMARY: Functional annotations and their hierarchical classification are widely used in omics workflows to build novel insight upon existing biological knowledge. Currently, a plethora of tools is available to explore omics datasets at the level of functional annotations, but...

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Darzi Y, Yamate Y, Yamada T. FuncTree2: an interactive radial tree for functional hierarchies and omics data visualization. Bioinformatics. 2019;35(21):4519-4521doi: 10.1093/bioinformatics/btz245.
Darzi, Y., Yamate, Y., & Yamada, T. (2019). FuncTree2: an interactive radial tree for functional hierarchies and omics data visualization. Bioinformatics (Oxford, England), 35(21), 4519-4521. https://doi.org/10.1093/bioinformatics/btz245
Darzi, Youssef, et al. "FuncTree2: an interactive radial tree for functional hierarchies and omics data visualization." Bioinformatics (Oxford, England) vol. 35,21 (2019): 4519-4521. doi: https://doi.org/10.1093/bioinformatics/btz245
Darzi Y, Yamate Y, Yamada T. FuncTree2: an interactive radial tree for functional hierarchies and omics data visualization. Bioinformatics. 2019 Nov 01;35(21):4519-4521. doi: 10.1093/bioinformatics/btz245. PMID: 31004476; PMCID: PMC6821421.
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Which methods are the most effective in enabling novice users to participate in ontology creation? A usability study.

Database : the journal of biological databases and curation

Zhang L, Yang X, Cota Z, Cui H, Ford B, Chen HL, Macklin JA, Reznicek A, Starr J.
PMID: 34156445
Database (Oxford). 2021 Jun 22;2021. doi: 10.1093/database/baab035.

Producing findable, accessible, interoperable and reusable (FAIR) data cannot be accomplished solely by data curators in all disciplines. In biology, we have shown that phenotypic data curation is not only costly, but it is burdened with inter-curator variation. We...

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Zhang L, Yang X, Cota Z, et al. Which methods are the most effective in enabling novice users to participate in ontology creation? A usability study. Database (Oxford). 2021;2021doi: 10.1093/database/baab035.
Zhang, L., Yang, X., Cota, Z., Cui, H., Ford, B., Chen, H. L., Macklin, J. A., Reznicek, A., & Starr, J. (2021). Which methods are the most effective in enabling novice users to participate in ontology creation? A usability study. Database : the journal of biological databases and curation, 2021. https://doi.org/10.1093/database/baab035
Zhang, Limin, et al. "Which methods are the most effective in enabling novice users to participate in ontology creation? A usability study." Database : the journal of biological databases and curation vol. 2021 (2021). doi: https://doi.org/10.1093/database/baab035
Zhang L, Yang X, Cota Z, Cui H, Ford B, Chen HL, Macklin JA, Reznicek A, Starr J. Which methods are the most effective in enabling novice users to participate in ontology creation? A usability study. Database (Oxford). 2021 Jun 22;2021. doi: 10.1093/database/baab035. PMID: 34156445; PMCID: PMC8218699.
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FFPred 3: feature-based function prediction for all Gene Ontology domains.

Scientific reports

Cozzetto D, Minneci F, Currant H, Jones DT.
PMID: 27561554
Sci Rep. 2016 Aug 26;6:31865. doi: 10.1038/srep31865.

Predicting protein function has been a major goal of bioinformatics for several decades, and it has gained fresh momentum thanks to recent community-wide blind tests aimed at benchmarking available tools on a genomic scale. Sequence-based predictors, especially those performing...

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Cozzetto D, Minneci F, Currant H, et al. FFPred 3: feature-based function prediction for all Gene Ontology domains. Sci Rep. 2016;6:31865doi: 10.1038/srep31865.
Cozzetto, D., Minneci, F., Currant, H., & Jones, D. T. (2016). FFPred 3: feature-based function prediction for all Gene Ontology domains. Scientific reports, 631865. https://doi.org/10.1038/srep31865
Cozzetto, Domenico, et al. "FFPred 3: feature-based function prediction for all Gene Ontology domains." Scientific reports vol. 6 (2016): 31865. doi: https://doi.org/10.1038/srep31865
Cozzetto D, Minneci F, Currant H, Jones DT. FFPred 3: feature-based function prediction for all Gene Ontology domains. Sci Rep. 2016 Aug 26;6:31865. doi: 10.1038/srep31865. PMID: 27561554; PMCID: PMC4999993.
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Construction of the gene expression subgroups of patients with coronary artery disease through bioinformatics approach.

Mathematical biosciences and engineering : MBE

Zhang B, Zeng K, Li R, Jiang H, Gao M, Zhang L, Li J, Guan R, Liu Y, Qiang Y, Yang Y.
PMID: 34814316
Math Biosci Eng. 2021 Oct 09;18(6):8622-8640. doi: 10.3934/mbe.2021427.

Coronary artery disease (CAD) is a heterogeneous disease that has placed a heavy burden on public health due to its considerable morbidity, mortality and high costs. Better understanding of the genetic drivers and gene expression clustering behind CAD will...

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Zhang B, Zeng K, Li R, et al. Construction of the gene expression subgroups of patients with coronary artery disease through bioinformatics approach. Math Biosci Eng. 2021;18(6):8622-8640doi: 10.3934/mbe.2021427.
Zhang, B., Zeng, K., Li, R., Jiang, H., Gao, M., Zhang, L., Li, J., Guan, R., Liu, Y., Qiang, Y., & Yang, Y. (2021). Construction of the gene expression subgroups of patients with coronary artery disease through bioinformatics approach. Mathematical biosciences and engineering : MBE, 18(6), 8622-8640. https://doi.org/10.3934/mbe.2021427
Zhang, Bin, et al. "Construction of the gene expression subgroups of patients with coronary artery disease through bioinformatics approach." Mathematical biosciences and engineering : MBE vol. 18,6 (2021): 8622-8640. doi: https://doi.org/10.3934/mbe.2021427
Zhang B, Zeng K, Li R, Jiang H, Gao M, Zhang L, Li J, Guan R, Liu Y, Qiang Y, Yang Y. Construction of the gene expression subgroups of patients with coronary artery disease through bioinformatics approach. Math Biosci Eng. 2021 Oct 09;18(6):8622-8640. doi: 10.3934/mbe.2021427. PMID: 34814316.
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Key genes associated with non-alcoholic fatty liver disease and acute myocardial infarction.

Medical science monitor : international medical journal of experimental and clinical research

Dai W, Sun Y, Jiang Z, Du K, Xia N, Zhong G.
PMID: 32594092
Med Sci Monit. 2020 Jun 28;26:e922492. doi: 10.12659/MSM.922492.

BACKGROUND With the increasing research on non-alcoholic fatty liver (NAFLD) and acute myocardial infarction (AMI), many evidences show a tight correlation between NAFLD and AMI, however the underlying pathophysiology is still not clear. This study was to identify the...

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Dai W, Sun Y, Jiang Z, et al. Key genes associated with non-alcoholic fatty liver disease and acute myocardial infarction. Med Sci Monit. 2020;26:e922492doi: 10.12659/MSM.922492.
Dai, W., Sun, Y., Jiang, Z., Du, K., Xia, N., & Zhong, G. (2020). Key genes associated with non-alcoholic fatty liver disease and acute myocardial infarction. Medical science monitor : international medical journal of experimental and clinical research, 26e922492. https://doi.org/10.12659/MSM.922492
Dai, Weiran, et al. "Key genes associated with non-alcoholic fatty liver disease and acute myocardial infarction." Medical science monitor : international medical journal of experimental and clinical research vol. 26 (2020): e922492. doi: https://doi.org/10.12659/MSM.922492
Dai W, Sun Y, Jiang Z, Du K, Xia N, Zhong G. Key genes associated with non-alcoholic fatty liver disease and acute myocardial infarction. Med Sci Monit. 2020 Jun 28;26:e922492. doi: 10.12659/MSM.922492. PMID: 32594092; PMCID: PMC7341693.
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pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information.

Bioinformatics (Oxford, England)

Cheng X, Xiao X, Chou KC.
PMID: 29106451
Bioinformatics. 2018 May 01;34(9):1448-1456. doi: 10.1093/bioinformatics/btx711.

MOTIVATION: For in-depth understanding the functions of proteins in a cell, the knowledge of their subcellular localization is indispensable. The current study is focused on human protein subcellular location prediction based on the sequence information alone. Although considerable efforts...

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Cheng X, Xiao X, Chou KC. pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics. 2018;34(9):1448-1456doi: 10.1093/bioinformatics/btx711.
Cheng, X., Xiao, X., & Chou, K. C. (2018). pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics (Oxford, England), 34(9), 1448-1456. https://doi.org/10.1093/bioinformatics/btx711
Cheng, Xiang, et al. "pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information." Bioinformatics (Oxford, England) vol. 34,9 (2018): 1448-1456. doi: https://doi.org/10.1093/bioinformatics/btx711
Cheng X, Xiao X, Chou KC. pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics. 2018 May 01;34(9):1448-1456. doi: 10.1093/bioinformatics/btx711. PMID: 29106451.
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Genomes OnLine database (GOLD) v.7: updates and new features.

Nucleic acids research

Mukherjee S, Stamatis D, Bertsch J, Ovchinnikova G, Katta HY, Mojica A, Chen IA, Kyrpides NC, Reddy T.
PMID: 30357420
Nucleic Acids Res. 2019 Jan 08;47:D649-D659. doi: 10.1093/nar/gky977.

The Genomes Online Database (GOLD) (https://gold.jgi.doe.gov) is an open online resource, which maintains an up-to-date catalog of genome and metagenome projects in the context of a comprehensive list of associated metadata. Information in GOLD is organized into four levels:...

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Mukherjee S, Stamatis D, Bertsch J, et al. Genomes OnLine database (GOLD) v.7: updates and new features. Nucleic Acids Res. 2019;47:D649-D659doi: 10.1093/nar/gky977.
Mukherjee, S., Stamatis, D., Bertsch, J., Ovchinnikova, G., Katta, H. Y., Mojica, A., Chen, I. A., Kyrpides, N. C., & Reddy, T. (2019). Genomes OnLine database (GOLD) v.7: updates and new features. Nucleic acids research, 47D649-D659. https://doi.org/10.1093/nar/gky977
Mukherjee, Supratim, et al. "Genomes OnLine database (GOLD) v.7: updates and new features." Nucleic acids research vol. 47 (2019): D649-D659. doi: https://doi.org/10.1093/nar/gky977
Mukherjee S, Stamatis D, Bertsch J, Ovchinnikova G, Katta HY, Mojica A, Chen IA, Kyrpides NC, Reddy T. Genomes OnLine database (GOLD) v.7: updates and new features. Nucleic Acids Res. 2019 Jan 08;47:D649-D659. doi: 10.1093/nar/gky977. PMID: 30357420; PMCID: PMC6323969.
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Gene fingerprint model for literature based detection of the associations among complex diseases: a case study of COPD.

BMC medical informatics and decision making

Chen G, Jia Y, Zhu L, Li P, Zhang L, Tao C, Jim Zheng W.
PMID: 30700303
BMC Med Inform Decis Mak. 2019 Jan 31;19:20. doi: 10.1186/s12911-019-0738-7.

BACKGROUND: Disease comorbidity is very common and has significant impact on disease treatment. Revealing the associations among diseases may help to understand the mechanisms of diseases, improve the prevention and treatment of diseases, and support the discovery of new...

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Chen G, Jia Y, Zhu L, et al. Gene fingerprint model for literature based detection of the associations among complex diseases: a case study of COPD. BMC Med Inform Decis Mak. 2019;19:20doi: 10.1186/s12911-019-0738-7.
Chen, G., Jia, Y., Zhu, L., Li, P., Zhang, L., Tao, C., & Jim Zheng, W. (2019). Gene fingerprint model for literature based detection of the associations among complex diseases: a case study of COPD. BMC medical informatics and decision making, 1920. https://doi.org/10.1186/s12911-019-0738-7
Chen, Guocai, et al. "Gene fingerprint model for literature based detection of the associations among complex diseases: a case study of COPD." BMC medical informatics and decision making vol. 19 (2019): 20. doi: https://doi.org/10.1186/s12911-019-0738-7
Chen G, Jia Y, Zhu L, Li P, Zhang L, Tao C, Jim Zheng W. Gene fingerprint model for literature based detection of the associations among complex diseases: a case study of COPD. BMC Med Inform Decis Mak. 2019 Jan 31;19:20. doi: 10.1186/s12911-019-0738-7. PMID: 30700303; PMCID: PMC6354331.
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PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology.

PloS one

Gioutlakis A, Klapa MI, Moschonas NK.
PMID: 29023571
PLoS One. 2017 Oct 12;12(10):e0186039. doi: 10.1371/journal.pone.0186039. eCollection 2017.

It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple heterogeneous primary datasets, presenting the PPIs at various genetic...

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Gioutlakis A, Klapa MI, Moschonas NK. PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology. PLoS One. 2017;12(10):e0186039doi: 10.1371/journal.pone.0186039.
Gioutlakis, A., Klapa, M. I., & Moschonas, N. K. (2017). PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology. PloS one, 12(10), e0186039. https://doi.org/10.1371/journal.pone.0186039
Gioutlakis, Aris, et al. "PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology." PloS one vol. 12,10 (2017): e0186039. doi: https://doi.org/10.1371/journal.pone.0186039
Gioutlakis A, Klapa MI, Moschonas NK. PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology. PLoS One. 2017 Oct 12;12(10):e0186039. doi: 10.1371/journal.pone.0186039. eCollection 2017. PMID: 29023571; PMCID: PMC5638325.
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Data- and expert-driven rule induction and filtering framework for functional interpretation and description of gene sets.

Journal of biomedical semantics

Gruca A, Sikora M.
PMID: 28651634
J Biomed Semantics. 2017 Jun 26;8(1):23. doi: 10.1186/s13326-017-0129-x.

BACKGROUND: High-throughput methods in molecular biology provided researchers with abundance of experimental data that need to be interpreted in order to understand the experimental results. Manual methods of functional gene/protein group interpretation are expensive and time-consuming; therefore, there is...

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Gruca A, Sikora M. Data- and expert-driven rule induction and filtering framework for functional interpretation and description of gene sets. J Biomed Semantics. 2017;8(1):23doi: 10.1186/s13326-017-0129-x.
Gruca, A., & Sikora, M. (2017). Data- and expert-driven rule induction and filtering framework for functional interpretation and description of gene sets. Journal of biomedical semantics, 8(1), 23. https://doi.org/10.1186/s13326-017-0129-x
Gruca, Aleksandra, and Sikora, Marek. "Data- and expert-driven rule induction and filtering framework for functional interpretation and description of gene sets." Journal of biomedical semantics vol. 8,1 (2017): 23. doi: https://doi.org/10.1186/s13326-017-0129-x
Gruca A, Sikora M. Data- and expert-driven rule induction and filtering framework for functional interpretation and description of gene sets. J Biomed Semantics. 2017 Jun 26;8(1):23. doi: 10.1186/s13326-017-0129-x. PMID: 28651634; PMCID: PMC5483958.
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GO annotation in InterPro: why stability does not indicate accuracy in a sea of changing annotations.

Database : the journal of biological databases and curation

Sangrador-Vegas A, Mitchell AL, Chang HY, Yong SY, Finn RD.
PMID: 26994912
Database (Oxford). 2016 Mar 19;2016. doi: 10.1093/database/baw027. Print 2016.

The removal of annotation from biological databases is often perceived as an indicator of erroneous annotation. As a corollary, annotation stability is considered to be a measure of reliability. However, diverse data-driven events can affect the stability of annotations...

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Sangrador-Vegas A, Mitchell AL, Chang HY, et al. GO annotation in InterPro: why stability does not indicate accuracy in a sea of changing annotations. Database (Oxford). 2016;2016doi: 10.1093/database/baw027.
Sangrador-Vegas, A., Mitchell, A. L., Chang, H. Y., Yong, S. Y., & Finn, R. D. (2016). GO annotation in InterPro: why stability does not indicate accuracy in a sea of changing annotations. Database : the journal of biological databases and curation, 2016. https://doi.org/10.1093/database/baw027
Sangrador-Vegas, Amaia, et al. "GO annotation in InterPro: why stability does not indicate accuracy in a sea of changing annotations." Database : the journal of biological databases and curation vol. 2016 (2016). doi: https://doi.org/10.1093/database/baw027
Sangrador-Vegas A, Mitchell AL, Chang HY, Yong SY, Finn RD. GO annotation in InterPro: why stability does not indicate accuracy in a sea of changing annotations. Database (Oxford). 2016 Mar 19;2016. doi: 10.1093/database/baw027. Print 2016. PMID: 26994912; PMCID: PMC4799721.
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Showing 13 to 24 of 77 entries