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Showing 25 to 36 of 77 entries
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A weighted multipath measurement based on gene ontology for estimating gene products similarity.

Journal of computational biology : a journal of computational molecular cell biology

Liu L, Dai X, Wang H, Song W, Lu J.
PMID: 25229994
J Comput Biol. 2014 Dec;21(12):964-74. doi: 10.1089/cmb.2014.0143.

Many different methods have been proposed for calculating the semantic similarity of term pairs based on gene ontology (GO). Most existing methods are based on information content (IC), and the methods based on IC are used more commonly than...

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Liu L, Dai X, Wang H, et al. A weighted multipath measurement based on gene ontology for estimating gene products similarity. J Comput Biol. 2014;21(12):964-74doi: 10.1089/cmb.2014.0143.
Liu, L., Dai, X., Wang, H., Song, W., & Lu, J. (2014). A weighted multipath measurement based on gene ontology for estimating gene products similarity. Journal of computational biology : a journal of computational molecular cell biology, 21(12), 964-74. https://doi.org/10.1089/cmb.2014.0143
Liu, Lizhen, et al. "A weighted multipath measurement based on gene ontology for estimating gene products similarity." Journal of computational biology : a journal of computational molecular cell biology vol. 21,12 (2014): 964-74. doi: https://doi.org/10.1089/cmb.2014.0143
Liu L, Dai X, Wang H, Song W, Lu J. A weighted multipath measurement based on gene ontology for estimating gene products similarity. J Comput Biol. 2014 Dec;21(12):964-74. doi: 10.1089/cmb.2014.0143. PMID: 25229994; PMCID: PMC4253309.
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SDADB: a functional annotation database of protein structural domains.

Database : the journal of biological databases and curation

Zeng C, Zhan W, Deng L.
PMID: 29961821
Database (Oxford). 2018 Jan 01;2018. doi: 10.1093/database/bay064.

Annotating functional terms with individual domains is essential for understanding the functions of full-length proteins. We describe SDADB, a functional annotation database for structural domains. SDADB provides associations between gene ontology (GO) terms and SCOP domains calculated with an...

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Zeng C, Zhan W, Deng L. SDADB: a functional annotation database of protein structural domains. Database (Oxford). 2018;2018doi: 10.1093/database/bay064.
Zeng, C., Zhan, W., & Deng, L. (2018). SDADB: a functional annotation database of protein structural domains. Database : the journal of biological databases and curation, 2018. https://doi.org/10.1093/database/bay064
Zeng, Cheng, et al. "SDADB: a functional annotation database of protein structural domains." Database : the journal of biological databases and curation vol. 2018 (2018). doi: https://doi.org/10.1093/database/bay064
Zeng C, Zhan W, Deng L. SDADB: a functional annotation database of protein structural domains. Database (Oxford). 2018 Jan 01;2018. doi: 10.1093/database/bay064. PMID: 29961821; PMCID: PMC6025185.
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Predicting disease associations via biological network analysis.

BMC bioinformatics

Sun K, Gonçalves JP, Larminie C, Przulj N.
PMID: 25228247
BMC Bioinformatics. 2014 Sep 17;15:304. doi: 10.1186/1471-2105-15-304.

BACKGROUND: Understanding the relationship between diseases based on the underlying biological mechanisms is one of the greatest challenges in modern biology and medicine. Exploring disease-disease associations by using system-level biological data is expected to improve our current knowledge of...

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Sun K, Gonçalves JP, Larminie C, et al. Predicting disease associations via biological network analysis. BMC Bioinformatics. 2014;15:304doi: 10.1186/1471-2105-15-304.
Sun, K., Gonçalves, J. P., Larminie, C., & Przulj, N. (2014). Predicting disease associations via biological network analysis. BMC bioinformatics, 15304. https://doi.org/10.1186/1471-2105-15-304
Sun, Kai, et al. "Predicting disease associations via biological network analysis." BMC bioinformatics vol. 15 (2014): 304. doi: https://doi.org/10.1186/1471-2105-15-304
Sun K, Gonçalves JP, Larminie C, Przulj N. Predicting disease associations via biological network analysis. BMC Bioinformatics. 2014 Sep 17;15:304. doi: 10.1186/1471-2105-15-304. PMID: 25228247; PMCID: PMC4174675.
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SSIF: Subsumption-based Sub-term Inference Framework to audit Gene Ontology.

Bioinformatics (Oxford, England)

Abeysinghe R, Hinderer EW, Moseley HNB, Cui L.
PMID: 32065617
Bioinformatics. 2020 May 01;36(10):3207-3214. doi: 10.1093/bioinformatics/btaa106.

MOTIVATION: The Gene Ontology (GO) is the unifying biological vocabulary for codifying, managing and sharing biological knowledge. Quality issues in GO, if not addressed, can cause misleading results or missed biological discoveries. Manual identification of potential quality issues in...

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Abeysinghe R, Hinderer EW, Moseley HNB, et al. SSIF: Subsumption-based Sub-term Inference Framework to audit Gene Ontology. Bioinformatics. 2020;36(10):3207-3214doi: 10.1093/bioinformatics/btaa106.
Abeysinghe, R., Hinderer, E. W., Moseley, H. N. B., & Cui, L. (2020). SSIF: Subsumption-based Sub-term Inference Framework to audit Gene Ontology. Bioinformatics (Oxford, England), 36(10), 3207-3214. https://doi.org/10.1093/bioinformatics/btaa106
Abeysinghe, Rashmie, et al. "SSIF: Subsumption-based Sub-term Inference Framework to audit Gene Ontology." Bioinformatics (Oxford, England) vol. 36,10 (2020): 3207-3214. doi: https://doi.org/10.1093/bioinformatics/btaa106
Abeysinghe R, Hinderer EW, Moseley HNB, Cui L. SSIF: Subsumption-based Sub-term Inference Framework to audit Gene Ontology. Bioinformatics. 2020 May 01;36(10):3207-3214. doi: 10.1093/bioinformatics/btaa106. PMID: 32065617; PMCID: PMC7214018.
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Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR.

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

Wieczorek S, Combes F, Borges H, Burger T.
PMID: 30852826
Methods Mol Biol. 2019;1959:225-246. doi: 10.1007/978-1-4939-9164-8_15.

ProStaR is a software tool dedicated to differential analysis in label-free quantitative proteomics. Practically, once biological samples have been analyzed by bottom-up mass spectrometry-based proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify...

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Wieczorek S, Combes F, Borges H, et al. Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR. Methods Mol Biol. 2019;1959:225-246doi: 10.1007/978-1-4939-9164-8_15.
Wieczorek, S., Combes, F., Borges, H., & Burger, T. (2019). Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR. Methods in molecular biology (Clifton, N.J.), 1959225-246. https://doi.org/10.1007/978-1-4939-9164-8_15
Wieczorek, Samuel, et al. "Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR." Methods in molecular biology (Clifton, N.J.) vol. 1959 (2019): 225-246. doi: https://doi.org/10.1007/978-1-4939-9164-8_15
Wieczorek S, Combes F, Borges H, Burger T. Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR. Methods Mol Biol. 2019;1959:225-246. doi: 10.1007/978-1-4939-9164-8_15. PMID: 30852826.
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Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance.

Scientific reports

Manjang K, Tripathi S, Yli-Harja O, Dehmer M, Emmert-Streib F.
PMID: 33028846
Sci Rep. 2020 Oct 07;10(1):16672. doi: 10.1038/s41598-020-73326-3.

Gene ontology (GO) is an eminent knowledge base frequently used for providing biological interpretations for the analysis of genes or gene sets from biological, medical and clinical problems. Unfortunately, the interpretation of such results is challenging due to the...

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Manjang K, Tripathi S, Yli-Harja O, et al. Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance. Sci Rep. 2020;10(1):16672doi: 10.1038/s41598-020-73326-3.
Manjang, K., Tripathi, S., Yli-Harja, O., Dehmer, M., & Emmert-Streib, F. (2020). Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance. Scientific reports, 10(1), 16672. https://doi.org/10.1038/s41598-020-73326-3
Manjang, Kalifa, et al. "Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance." Scientific reports vol. 10,1 (2020): 16672. doi: https://doi.org/10.1038/s41598-020-73326-3
Manjang K, Tripathi S, Yli-Harja O, Dehmer M, Emmert-Streib F. Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance. Sci Rep. 2020 Oct 07;10(1):16672. doi: 10.1038/s41598-020-73326-3. PMID: 33028846; PMCID: PMC7542435.
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Moving targets in drug discovery.

Scientific reports

Zdrazil B, Richter L, Brown N, Guha R.
PMID: 33214619
Sci Rep. 2020 Nov 19;10(1):20213. doi: 10.1038/s41598-020-77033-x.

Drug Discovery is a lengthy and costly process and has faced a period of declining productivity within the last two decades resulting in increasing importance of integrative data-driven approaches. In this paper, data mining and integration is leveraged to...

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Zdrazil B, Richter L, Brown N, et al. Moving targets in drug discovery. Sci Rep. 2020;10(1):20213doi: 10.1038/s41598-020-77033-x.
Zdrazil, B., Richter, L., Brown, N., & Guha, R. (2020). Moving targets in drug discovery. Scientific reports, 10(1), 20213. https://doi.org/10.1038/s41598-020-77033-x
Zdrazil, Barbara, et al. "Moving targets in drug discovery." Scientific reports vol. 10,1 (2020): 20213. doi: https://doi.org/10.1038/s41598-020-77033-x
Zdrazil B, Richter L, Brown N, Guha R. Moving targets in drug discovery. Sci Rep. 2020 Nov 19;10(1):20213. doi: 10.1038/s41598-020-77033-x. PMID: 33214619; PMCID: PMC7677539.
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[Intervention mechanism of Qingwen Baidu Yin on cytokine storm based on network pharmacology].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica

Zhang FR, Zhu N, Li ZY, Tang SH.
PMID: 32489026
Zhongguo Zhong Yao Za Zhi. 2020 Apr;45(7):1499-1508. doi: 10.19540/j.cnki.cjcmm.20200305.401.

The aim of this paper was to explore the intervention mechanism of Qingwen Baidu Yin in cytokine storm based on network pharmacology. TCMSP and TCMIP V2.0 server were used to predict all chemical components and action targets of Qingwen...

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Zhang FR, Zhu N, Li ZY, et al. [Intervention mechanism of Qingwen Baidu Yin on cytokine storm based on network pharmacology]. Zhongguo Zhong Yao Za Zhi. 2020;45(7):1499-1508doi: 10.19540/j.cnki.cjcmm.20200305.401.
Zhang, F. R., Zhu, N., Li, Z. Y., & Tang, S. H. (2020). [Intervention mechanism of Qingwen Baidu Yin on cytokine storm based on network pharmacology]. Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica, 45(7), 1499-1508. https://doi.org/10.19540/j.cnki.cjcmm.20200305.401
Zhang, Feng-Rong, et al. "[Intervention mechanism of Qingwen Baidu Yin on cytokine storm based on network pharmacology]." Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica vol. 45,7 (2020): 1499-1508. doi: https://doi.org/10.19540/j.cnki.cjcmm.20200305.401
Zhang FR, Zhu N, Li ZY, Tang SH. [Intervention mechanism of Qingwen Baidu Yin on cytokine storm based on network pharmacology]. Zhongguo Zhong Yao Za Zhi. 2020 Apr;45(7):1499-1508. doi: 10.19540/j.cnki.cjcmm.20200305.401. Chinese. PMID: 32489026.
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A method for increasing expressivity of Gene Ontology annotations using a compositional approach.

BMC bioinformatics

Huntley RP, Harris MA, Alam-Faruque Y, Blake JA, Carbon S, Dietze H, Dimmer EC, Foulger RE, Hill DP, Khodiyar VK, Lock A, Lomax J, Lovering RC, Mutowo-Meullenet P, Sawford T, Van Auken K, Wood V, Mungall CJ.
PMID: 24885854
BMC Bioinformatics. 2014 May 21;15:155. doi: 10.1186/1471-2105-15-155.

BACKGROUND: The Gene Ontology project integrates data about the function of gene products across a diverse range of organisms, allowing the transfer of knowledge from model organisms to humans, and enabling computational analyses for interpretation of high-throughput experimental and...

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Huntley RP, Harris MA, Alam-Faruque Y, et al. A method for increasing expressivity of Gene Ontology annotations using a compositional approach. BMC Bioinformatics. 2014;15:155doi: 10.1186/1471-2105-15-155.
Huntley, R. P., Harris, M. A., Alam-Faruque, Y., Blake, J. A., Carbon, S., Dietze, H., Dimmer, E. C., Foulger, R. E., Hill, D. P., Khodiyar, V. K., Lock, A., Lomax, J., Lovering, R. C., Mutowo-Meullenet, P., Sawford, T., Van Auken, K., Wood, V., & Mungall, C. J. (2014). A method for increasing expressivity of Gene Ontology annotations using a compositional approach. BMC bioinformatics, 15155. https://doi.org/10.1186/1471-2105-15-155
Huntley, Rachael P, et al. "A method for increasing expressivity of Gene Ontology annotations using a compositional approach." BMC bioinformatics vol. 15 (2014): 155. doi: https://doi.org/10.1186/1471-2105-15-155
Huntley RP, Harris MA, Alam-Faruque Y, Blake JA, Carbon S, Dietze H, Dimmer EC, Foulger RE, Hill DP, Khodiyar VK, Lock A, Lomax J, Lovering RC, Mutowo-Meullenet P, Sawford T, Van Auken K, Wood V, Mungall CJ. A method for increasing expressivity of Gene Ontology annotations using a compositional approach. BMC Bioinformatics. 2014 May 21;15:155. doi: 10.1186/1471-2105-15-155. PMID: 24885854; PMCID: PMC4039540.
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Identification of key genes in allergic rhinitis by bioinformatics analysis.

The Journal of international medical research

Zhang Y, Huang Y, Chen WX, Xu ZM.
PMID: 34334005
J Int Med Res. 2021 Jul;49(7):3000605211029521. doi: 10.1177/03000605211029521.

OBJECTIVE: This study aimed to explore the potential molecular mechanism of allergic rhinitis (AR) and identify gene signatures by analyzing microarray data using bioinformatics methods.METHODS: The dataset GSE19187 was used to screen differentially expressed genes (DEGs) between samples from...

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Zhang Y, Huang Y, Chen WX, et al. Identification of key genes in allergic rhinitis by bioinformatics analysis. J Int Med Res. 2021;49(7):3000605211029521doi: 10.1177/03000605211029521.
Zhang, Y., Huang, Y., Chen, W. X., & Xu, Z. M. (2021). Identification of key genes in allergic rhinitis by bioinformatics analysis. The Journal of international medical research, 49(7), 3000605211029521. https://doi.org/10.1177/03000605211029521
Zhang, Yunfei, et al. "Identification of key genes in allergic rhinitis by bioinformatics analysis." The Journal of international medical research vol. 49,7 (2021): 3000605211029521. doi: https://doi.org/10.1177/03000605211029521
Zhang Y, Huang Y, Chen WX, Xu ZM. Identification of key genes in allergic rhinitis by bioinformatics analysis. J Int Med Res. 2021 Jul;49(7):3000605211029521. doi: 10.1177/03000605211029521. PMID: 34334005; PMCID: PMC8326637.
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MathIOmica: An Integrative Platform for Dynamic Omics.

Scientific reports

Mias GI, Yusufaly T, Roushangar R, Brooks LR, Singh VV, Christou C.
PMID: 27883025
Sci Rep. 2016 Nov 24;6:37237. doi: 10.1038/srep37237.

Multiple omics data are rapidly becoming available, necessitating the use of new methods to integrate different technologies and interpret the results arising from multimodal assaying. The MathIOmica package for Mathematica provides one of the first extensive introductions to the...

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Mias GI, Yusufaly T, Roushangar R, et al. MathIOmica: An Integrative Platform for Dynamic Omics. Sci Rep. 2016;6:37237doi: 10.1038/srep37237.
Mias, G. I., Yusufaly, T., Roushangar, R., Brooks, L. R., Singh, V. V., & Christou, C. (2016). MathIOmica: An Integrative Platform for Dynamic Omics. Scientific reports, 637237. https://doi.org/10.1038/srep37237
Mias, George I, et al. "MathIOmica: An Integrative Platform for Dynamic Omics." Scientific reports vol. 6 (2016): 37237. doi: https://doi.org/10.1038/srep37237
Mias GI, Yusufaly T, Roushangar R, Brooks LR, Singh VV, Christou C. MathIOmica: An Integrative Platform for Dynamic Omics. Sci Rep. 2016 Nov 24;6:37237. doi: 10.1038/srep37237. PMID: 27883025; PMCID: PMC5121649.
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Physiological responses and transcriptome analysis of Spirodela polyrhiza under red, blue, and white light.

Planta

Zhong Y, Wang L, Ma Z, Du X.
PMID: 34855030
Planta. 2021 Dec 02;255(1):11. doi: 10.1007/s00425-021-03764-4.

MAIN CONCLUSION: Red light (RL) accelerated starch accumulation in S. polyrhiza, but higher protein content under blue light (BL) was associated with the upregulation of most DEGs enriched for specific GO terms and KEGG pathways. Red light (RL) and...

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Zhong Y, Wang L, Ma Z, et al. Physiological responses and transcriptome analysis of Spirodela polyrhiza under red, blue, and white light. Planta. 2021;255(1):11doi: 10.1007/s00425-021-03764-4.
Zhong, Y., Wang, L., Ma, Z., & Du, X. (2021). Physiological responses and transcriptome analysis of Spirodela polyrhiza under red, blue, and white light. Planta, 255(1), 11. https://doi.org/10.1007/s00425-021-03764-4
Zhong, Yu, et al. "Physiological responses and transcriptome analysis of Spirodela polyrhiza under red, blue, and white light." Planta vol. 255,1 (2021): 11. doi: https://doi.org/10.1007/s00425-021-03764-4
Zhong Y, Wang L, Ma Z, Du X. Physiological responses and transcriptome analysis of Spirodela polyrhiza under red, blue, and white light. Planta. 2021 Dec 02;255(1):11. doi: 10.1007/s00425-021-03764-4. PMID: 34855030.
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Showing 25 to 36 of 77 entries