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Showing 1 to 12 of 77 entries
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NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity.

Computational biology and chemistry

Lu C, Wang J, Zhang Z, Yang P, Yu G.
PMID: 27670689
Comput Biol Chem. 2016 Dec;65:203-211. doi: 10.1016/j.compbiolchem.2016.09.005. Epub 2016 Sep 13.

Gene Ontology (GO) provides GO annotations (GOA) that associate gene products with GO terms that summarize their cellular, molecular and functional aspects in the context of biological pathways. GO Consortium (GOC) resorts to various quality assurances to ensure the...

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Lu C, Wang J, Zhang Z, et al. NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity. Comput Biol Chem. 2016;65:203-211doi: 10.1016/j.compbiolchem.2016.09.005.
Lu, C., Wang, J., Zhang, Z., Yang, P., & Yu, G. (2016). NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity. Computational biology and chemistry, 65203-211. https://doi.org/10.1016/j.compbiolchem.2016.09.005
Lu, Chang, et al. "NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity." Computational biology and chemistry vol. 65 (2016): 203-211. doi: https://doi.org/10.1016/j.compbiolchem.2016.09.005
Lu C, Wang J, Zhang Z, Yang P, Yu G. NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity. Comput Biol Chem. 2016 Dec;65:203-211. doi: 10.1016/j.compbiolchem.2016.09.005. Epub 2016 Sep 13. PMID: 27670689.
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Pleurochrysome: A Web Database of Pleurochrysis Transcripts and Orthologs Among Heterogeneous Algae.

Plant & cell physiology

Yamamoto N, Kudo T, Fujiwara S, Takatsuka Y, Hirokawa Y, Tsuzuki M, Takano T, Kobayashi M, Suda K, Asamizu E, Yokoyama K, Shibata D, Tabata S, Yano K.
PMID: 26746174
Plant Cell Physiol. 2016 Jan;57(1):e6. doi: 10.1093/pcp/pcv195. Epub 2016 Jan 07.

Pleurochrysis is a coccolithophorid genus, which belongs to the Coccolithales in the Haptophyta. The genus has been used extensively for biological research, together with Emiliania in the Isochrysidales, to understand distinctive features between the two coccolithophorid-including orders. However, molecular...

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Yamamoto N, Kudo T, Fujiwara S, et al. Pleurochrysome: A Web Database of Pleurochrysis Transcripts and Orthologs Among Heterogeneous Algae. Plant Cell Physiol. 2016;57(1):e6doi: 10.1093/pcp/pcv195.
Yamamoto, N., Kudo, T., Fujiwara, S., Takatsuka, Y., Hirokawa, Y., Tsuzuki, M., Takano, T., Kobayashi, M., Suda, K., Asamizu, E., Yokoyama, K., Shibata, D., Tabata, S., & Yano, K. (2016). Pleurochrysome: A Web Database of Pleurochrysis Transcripts and Orthologs Among Heterogeneous Algae. Plant & cell physiology, 57(1), e6. https://doi.org/10.1093/pcp/pcv195
Yamamoto, Naoki, et al. "Pleurochrysome: A Web Database of Pleurochrysis Transcripts and Orthologs Among Heterogeneous Algae." Plant & cell physiology vol. 57,1 (2016): e6. doi: https://doi.org/10.1093/pcp/pcv195
Yamamoto N, Kudo T, Fujiwara S, Takatsuka Y, Hirokawa Y, Tsuzuki M, Takano T, Kobayashi M, Suda K, Asamizu E, Yokoyama K, Shibata D, Tabata S, Yano K. Pleurochrysome: A Web Database of Pleurochrysis Transcripts and Orthologs Among Heterogeneous Algae. Plant Cell Physiol. 2016 Jan;57(1):e6. doi: 10.1093/pcp/pcv195. Epub 2016 Jan 07. PMID: 26746174; PMCID: PMC4722176.
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Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database.

PloS one

Davis AP, Wiegers TC, King BL, Wiegers J, Grondin CJ, Sciaky D, Johnson RJ, Mattingly CJ.
PMID: 27171405
PLoS One. 2016 May 12;11(5):e0155530. doi: 10.1371/journal.pone.0155530. eCollection 2016.

Strategies for discovering common molecular events among disparate diseases hold promise for improving understanding of disease etiology and expanding treatment options. One technique is to leverage curated datasets found in the public domain. The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/)...

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Davis AP, Wiegers TC, King BL, et al. Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database. PLoS One. 2016;11(5):e0155530doi: 10.1371/journal.pone.0155530.
Davis, A. P., Wiegers, T. C., King, B. L., Wiegers, J., Grondin, C. J., Sciaky, D., Johnson, R. J., & Mattingly, C. J. (2016). Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database. PloS one, 11(5), e0155530. https://doi.org/10.1371/journal.pone.0155530
Davis, Allan Peter, et al. "Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database." PloS one vol. 11,5 (2016): e0155530. doi: https://doi.org/10.1371/journal.pone.0155530
Davis AP, Wiegers TC, King BL, Wiegers J, Grondin CJ, Sciaky D, Johnson RJ, Mattingly CJ. Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database. PLoS One. 2016 May 12;11(5):e0155530. doi: 10.1371/journal.pone.0155530. eCollection 2016. PMID: 27171405; PMCID: PMC4865041.
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A disease similarity matrix based on the uniqueness of shared genes.

BMC medical genomics

Carson MB, Liu C, Lu Y, Jia C, Lu H.
PMID: 28589854
BMC Med Genomics. 2017 May 24;10:26. doi: 10.1186/s12920-017-0265-2.

BACKGROUND: Complex diseases involve many genes, and these genes are often associated with several different illnesses. Disease similarity measurement can be based on shared genotype or phenotype. Quantifying relationships between genes can reveal previously unknown connections and form a...

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Carson MB, Liu C, Lu Y, et al. A disease similarity matrix based on the uniqueness of shared genes. BMC Med Genomics. 2017;10:26doi: 10.1186/s12920-017-0265-2.
Carson, M. B., Liu, C., Lu, Y., Jia, C., & Lu, H. (2017). A disease similarity matrix based on the uniqueness of shared genes. BMC medical genomics, 1026. https://doi.org/10.1186/s12920-017-0265-2
Carson, Matthew B, et al. "A disease similarity matrix based on the uniqueness of shared genes." BMC medical genomics vol. 10 (2017): 26. doi: https://doi.org/10.1186/s12920-017-0265-2
Carson MB, Liu C, Lu Y, Jia C, Lu H. A disease similarity matrix based on the uniqueness of shared genes. BMC Med Genomics. 2017 May 24;10:26. doi: 10.1186/s12920-017-0265-2. PMID: 28589854; PMCID: PMC5461528.
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Integrative information theoretic network analysis for genome-wide association study of aspirin exacerbated respiratory disease in Korean population.

BMC medical genomics

Wang S, Jeong HH, Kim D, Wee K, Park HS, Kim SH, Sohn KA.
PMID: 28589859
BMC Med Genomics. 2017 May 24;10:31. doi: 10.1186/s12920-017-0266-1.

BACKGROUND: Aspirin Exacerbated Respiratory Disease (AERD) is a chronic medical condition that encompasses asthma, nasal polyposis, and hypersensitivity to aspirin and other non-steroidal anti-inflammatory drugs. Several previous studies have shown that part of the genetic effects of the disease...

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Wang S, Jeong HH, Kim D, et al. Integrative information theoretic network analysis for genome-wide association study of aspirin exacerbated respiratory disease in Korean population. BMC Med Genomics. 2017;10:31doi: 10.1186/s12920-017-0266-1.
Wang, S., Jeong, H. H., Kim, D., Wee, K., Park, H. S., Kim, S. H., & Sohn, K. A. (2017). Integrative information theoretic network analysis for genome-wide association study of aspirin exacerbated respiratory disease in Korean population. BMC medical genomics, 1031. https://doi.org/10.1186/s12920-017-0266-1
Wang, Sehee, et al. "Integrative information theoretic network analysis for genome-wide association study of aspirin exacerbated respiratory disease in Korean population." BMC medical genomics vol. 10 (2017): 31. doi: https://doi.org/10.1186/s12920-017-0266-1
Wang S, Jeong HH, Kim D, Wee K, Park HS, Kim SH, Sohn KA. Integrative information theoretic network analysis for genome-wide association study of aspirin exacerbated respiratory disease in Korean population. BMC Med Genomics. 2017 May 24;10:31. doi: 10.1186/s12920-017-0266-1. PMID: 28589859; PMCID: PMC5461529.
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FunRich: An open access standalone functional enrichment and interaction network analysis tool.

Proteomics

Pathan M, Keerthikumar S, Ang CS, Gangoda L, Quek CY, Williamson NA, Mouradov D, Sieber OM, Simpson RJ, Salim A, Bacic A, Hill AF, Stroud DA, Ryan MT, Agbinya JI, Mariadason JM, Burgess AW, Mathivanan S.
PMID: 25921073
Proteomics. 2015 Aug;15(15):2597-601. doi: 10.1002/pmic.201400515. Epub 2015 Jun 17.

As high-throughput techniques including proteomics become more accessible to individual laboratories, there is an urgent need for a user-friendly bioinformatics analysis system. Here, we describe FunRich, an open access, standalone functional enrichment and network analysis tool. FunRich is designed...

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Pathan M, Keerthikumar S, Ang CS, et al. FunRich: An open access standalone functional enrichment and interaction network analysis tool. Proteomics. 2015;15(15):2597-601doi: 10.1002/pmic.201400515.
Pathan, M., Keerthikumar, S., Ang, C. S., Gangoda, L., Quek, C. Y., Williamson, N. A., Mouradov, D., Sieber, O. M., Simpson, R. J., Salim, A., Bacic, A., Hill, A. F., Stroud, D. A., Ryan, M. T., Agbinya, J. I., Mariadason, J. M., Burgess, A. W., & Mathivanan, S. (2015). FunRich: An open access standalone functional enrichment and interaction network analysis tool. Proteomics, 15(15), 2597-601. https://doi.org/10.1002/pmic.201400515
Pathan, Mohashin, et al. "FunRich: An open access standalone functional enrichment and interaction network analysis tool." Proteomics vol. 15,15 (2015): 2597-601. doi: https://doi.org/10.1002/pmic.201400515
Pathan M, Keerthikumar S, Ang CS, Gangoda L, Quek CY, Williamson NA, Mouradov D, Sieber OM, Simpson RJ, Salim A, Bacic A, Hill AF, Stroud DA, Ryan MT, Agbinya JI, Mariadason JM, Burgess AW, Mathivanan S. FunRich: An open access standalone functional enrichment and interaction network analysis tool. Proteomics. 2015 Aug;15(15):2597-601. doi: 10.1002/pmic.201400515. Epub 2015 Jun 17. PMID: 25921073.
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OntoPIN: an ontology-annotated PPI database.

Interdisciplinary sciences, computational life sciences

Guzzi PH, Veltri P, Cannataro M.
PMID: 24307410
Interdiscip Sci. 2013 Sep;5(3):187-95. doi: 10.1007/s12539-013-0173-x. Epub 2013 Dec 04.

Protein-protein interaction (PPI) data stored in publicly available databases are queried by the use of simple query interfaces allowing only key-based queries. A typical query on such databases is based on the use of protein identifiers and enables the...

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Guzzi PH, Veltri P, Cannataro M. OntoPIN: an ontology-annotated PPI database. Interdiscip Sci. 2013;5(3):187-95doi: 10.1007/s12539-013-0173-x.
Guzzi, P. H., Veltri, P., & Cannataro, M. (2013). OntoPIN: an ontology-annotated PPI database. Interdisciplinary sciences, computational life sciences, 5(3), 187-95. https://doi.org/10.1007/s12539-013-0173-x
Guzzi, Pietro Hiram, et al. "OntoPIN: an ontology-annotated PPI database." Interdisciplinary sciences, computational life sciences vol. 5,3 (2013): 187-95. doi: https://doi.org/10.1007/s12539-013-0173-x
Guzzi PH, Veltri P, Cannataro M. OntoPIN: an ontology-annotated PPI database. Interdiscip Sci. 2013 Sep;5(3):187-95. doi: 10.1007/s12539-013-0173-x. Epub 2013 Dec 04. PMID: 24307410.
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GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data.

Scientific reports

Araujo FA, Barh D, Silva A, Guimarães L, Ramos RTJ.
PMID: 29379090
Sci Rep. 2018 Jan 29;8(1):1794. doi: 10.1038/s41598-018-20211-9.

Downstream analysis of genomic and transcriptomic sequence data is often executed by functional annotation that can be performed by various bioinformatics tools and biological databases. However, a full fast integrated tool is not available for such analysis. Besides, the...

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Araujo FA, Barh D, Silva A, et al. GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data. Sci Rep. 2018;8(1):1794doi: 10.1038/s41598-018-20211-9.
Araujo, F. A., Barh, D., Silva, A., Guimarães, L., & Ramos, R. T. J. (2018). GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data. Scientific reports, 8(1), 1794. https://doi.org/10.1038/s41598-018-20211-9
Araujo, Fabricio Almeida, et al. "GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data." Scientific reports vol. 8,1 (2018): 1794. doi: https://doi.org/10.1038/s41598-018-20211-9
Araujo FA, Barh D, Silva A, Guimarães L, Ramos RTJ. GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data. Sci Rep. 2018 Jan 29;8(1):1794. doi: 10.1038/s41598-018-20211-9. PMID: 29379090; PMCID: PMC5789007.
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WEGO 2.0: a web tool for analyzing and plotting GO annotations, 2018 update.

Nucleic acids research

Ye J, Zhang Y, Cui H, Liu J, Wu Y, Cheng Y, Xu H, Huang X, Li S, Zhou A, Zhang X, Bolund L, Chen Q, Wang J, Yang H, Fang L, Shi C.
PMID: 29788377
Nucleic Acids Res. 2018 Jul 02;46:W71-W75. doi: 10.1093/nar/gky400.

WEGO (Web Gene Ontology Annotation Plot), created in 2006, is a simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results. Owing largely to the rapid development of high-throughput sequencing and the increasing acceptance of...

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Ye J, Zhang Y, Cui H, et al. WEGO 2.0: a web tool for analyzing and plotting GO annotations, 2018 update. Nucleic Acids Res. 2018;46:W71-W75doi: 10.1093/nar/gky400.
Ye, J., Zhang, Y., Cui, H., Liu, J., Wu, Y., Cheng, Y., Xu, H., Huang, X., Li, S., Zhou, A., Zhang, X., Bolund, L., Chen, Q., Wang, J., Yang, H., Fang, L., & Shi, C. (2018). WEGO 2.0: a web tool for analyzing and plotting GO annotations, 2018 update. Nucleic acids research, 46W71-W75. https://doi.org/10.1093/nar/gky400
Ye, Jia, et al. "WEGO 2.0: a web tool for analyzing and plotting GO annotations, 2018 update." Nucleic acids research vol. 46 (2018): W71-W75. doi: https://doi.org/10.1093/nar/gky400
Ye J, Zhang Y, Cui H, Liu J, Wu Y, Cheng Y, Xu H, Huang X, Li S, Zhou A, Zhang X, Bolund L, Chen Q, Wang J, Yang H, Fang L, Shi C. WEGO 2.0: a web tool for analyzing and plotting GO annotations, 2018 update. Nucleic Acids Res. 2018 Jul 02;46:W71-W75. doi: 10.1093/nar/gky400. PMID: 29788377; PMCID: PMC6030983.
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Annotating activation/inhibition relationships to protein-protein interactions using gene ontology relations.

BMC systems biology

Yim S, Yu H, Jang D, Lee D.
PMID: 29671402
BMC Syst Biol. 2018 Apr 11;12:9. doi: 10.1186/s12918-018-0535-4.

BACKGROUND: Signaling pathways can be reconstructed by identifying 'effect types' (i.e. activation/inhibition) of protein-protein interactions (PPIs). Effect types are composed of 'directions' (i.e. upstream/downstream) and 'signs' (i.e. positive/negative), thereby requiring directions as well as signs of PPIs to predict...

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Yim S, Yu H, Jang D, et al. Annotating activation/inhibition relationships to protein-protein interactions using gene ontology relations. BMC Syst Biol. 2018;12:9doi: 10.1186/s12918-018-0535-4.
Yim, S., Yu, H., Jang, D., & Lee, D. (2018). Annotating activation/inhibition relationships to protein-protein interactions using gene ontology relations. BMC systems biology, 129. https://doi.org/10.1186/s12918-018-0535-4
Yim, Soorin, et al. "Annotating activation/inhibition relationships to protein-protein interactions using gene ontology relations." BMC systems biology vol. 12 (2018): 9. doi: https://doi.org/10.1186/s12918-018-0535-4
Yim S, Yu H, Jang D, Lee D. Annotating activation/inhibition relationships to protein-protein interactions using gene ontology relations. BMC Syst Biol. 2018 Apr 11;12:9. doi: 10.1186/s12918-018-0535-4. PMID: 29671402; PMCID: PMC5907154.
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A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies.

IEEE/ACM transactions on computational biology and bioinformatics

Hashemikhabir S, Xia R, Xiang Y, Janga SC.
PMID: 26394433
IEEE/ACM Trans Comput Biol Bioinform. 2018 Jul-Aug;15(4):1259-1269. doi: 10.1109/TCBB.2015.2480056. Epub 2015 Sep 18.

Although some methods are proposed for automatic ontology generation, none of them address the issue of integrating large-scale heterogeneous biomedical ontologies. We propose a novel approach for integrating various types of ontologies efficiently and apply it to integrate International...

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Hashemikhabir S, Xia R, Xiang Y, et al. A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies. IEEE/ACM Trans Comput Biol Bioinform. 2015;15(4):1259-1269doi: 10.1109/TCBB.2015.2480056.
Hashemikhabir, S., Xia, R., Xiang, Y., & Janga, S. C. (2018). A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies. IEEE/ACM transactions on computational biology and bioinformatics, 15(4), 1259-1269. https://doi.org/10.1109/TCBB.2015.2480056
Hashemikhabir, Seyedsasan, et al. "A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies." IEEE/ACM transactions on computational biology and bioinformatics vol. 15,4 (2018): 1259-1269. doi: https://doi.org/10.1109/TCBB.2015.2480056
Hashemikhabir S, Xia R, Xiang Y, Janga SC. A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies. IEEE/ACM Trans Comput Biol Bioinform. 2018 Jul-Aug;15(4):1259-1269. doi: 10.1109/TCBB.2015.2480056. Epub 2015 Sep 18. PMID: 26394433.
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Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis.

International ophthalmology

Sun H, Cheng Y, Yan Z, Liu X, Zhang J.
PMID: 31953631
Int Ophthalmol. 2020 Feb;40(2):269-279. doi: 10.1007/s10792-019-01158-w. Epub 2020 Jan 17.

PURPOSE: Diabetic retinopathy (DR) especially proliferative diabetic retinopathy (PDR) is a serious eye disease. We aimed to identify key pathway and hub genes associated with PDR by analyzing the expression of retinal fibrovascular tissue in PDR patients.METHODS: First raw...

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Sun H, Cheng Y, Yan Z, et al. Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis. Int Ophthalmol. 2020;40(2):269-279doi: 10.1007/s10792-019-01158-w.
Sun, H., Cheng, Y., Yan, Z., Liu, X., & Zhang, J. (2020). Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis. International ophthalmology, 40(2), 269-279. https://doi.org/10.1007/s10792-019-01158-w
Sun, Haiyan, et al. "Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis." International ophthalmology vol. 40,2 (2020): 269-279. doi: https://doi.org/10.1007/s10792-019-01158-w
Sun H, Cheng Y, Yan Z, Liu X, Zhang J. Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis. Int Ophthalmol. 2020 Feb;40(2):269-279. doi: 10.1007/s10792-019-01158-w. Epub 2020 Jan 17. PMID: 31953631.
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Showing 1 to 12 of 77 entries