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Showing 25 to 36 of 64 entries
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Computational developments in microRNA-regulated protein-protein interactions.

BMC systems biology

Zhu W, Chen YP.
PMID: 24507415
BMC Syst Biol. 2014 Feb 10;8:14. doi: 10.1186/1752-0509-8-14.

Protein-protein interaction (PPI) is one of the most important functional components of a living cell. Recently, researchers have been interested in investigating the correlation between PPI and microRNA, which has been found to be a regulator at the post-transcriptional...

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Zhu W, Chen YP. Computational developments in microRNA-regulated protein-protein interactions. BMC Syst Biol. 2014;8:14doi: 10.1186/1752-0509-8-14.
Zhu, W., & Chen, Y. P. (2014). Computational developments in microRNA-regulated protein-protein interactions. BMC systems biology, 814. https://doi.org/10.1186/1752-0509-8-14
Zhu, Wei, and Chen, Yi-Ping Phoebe. "Computational developments in microRNA-regulated protein-protein interactions." BMC systems biology vol. 8 (2014): 14. doi: https://doi.org/10.1186/1752-0509-8-14
Zhu W, Chen YP. Computational developments in microRNA-regulated protein-protein interactions. BMC Syst Biol. 2014 Feb 10;8:14. doi: 10.1186/1752-0509-8-14. PMID: 24507415; PMCID: PMC3922185.
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Systems-Wide Approaches in Induced Pluripotent Stem Cell Models.

Annual review of pathology

Lau E, Paik DT, Wu JC.
PMID: 30379619
Annu Rev Pathol. 2019 Jan 24;14:395-419. doi: 10.1146/annurev-pathmechdis-012418-013046. Epub 2018 Oct 31.

Human induced pluripotent stem cells (iPSCs) provide a renewable supply of patient-specific and tissue-specific cells for cellular and molecular studies of disease mechanisms. Combined with advances in various omics technologies, iPSC models can be used to profile the expression...

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Lau E, Paik DT, Wu JC. Systems-Wide Approaches in Induced Pluripotent Stem Cell Models. Annu Rev Pathol. 2018;14:395-419doi: 10.1146/annurev-pathmechdis-012418-013046.
Lau, E., Paik, D. T., & Wu, J. C. (2019). Systems-Wide Approaches in Induced Pluripotent Stem Cell Models. Annual review of pathology, 14395-419. https://doi.org/10.1146/annurev-pathmechdis-012418-013046
Lau, Edward, et al. "Systems-Wide Approaches in Induced Pluripotent Stem Cell Models." Annual review of pathology vol. 14 (2019): 395-419. doi: https://doi.org/10.1146/annurev-pathmechdis-012418-013046
Lau E, Paik DT, Wu JC. Systems-Wide Approaches in Induced Pluripotent Stem Cell Models. Annu Rev Pathol. 2019 Jan 24;14:395-419. doi: 10.1146/annurev-pathmechdis-012418-013046. Epub 2018 Oct 31. PMID: 30379619; PMCID: PMC6450651.
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DDBJ update: the Genomic Expression Archive (GEA) for functional genomics data.

Nucleic acids research

Kodama Y, Mashima J, Kosuge T, Ogasawara O.
PMID: 30357349
Nucleic Acids Res. 2019 Jan 08;47:D69-D73. doi: 10.1093/nar/gky1002.

The Genomic Expression Archive (GEA) for functional genomics data from microarray and high-throughput sequencing experiments has been established at the DNA Data Bank of Japan (DDBJ) Center (https://www.ddbj.nig.ac.jp), which is a member of the International Nucleotide Sequence Database Collaboration...

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Kodama Y, Mashima J, Kosuge T, et al. DDBJ update: the Genomic Expression Archive (GEA) for functional genomics data. Nucleic Acids Res. 2019;47:D69-D73doi: 10.1093/nar/gky1002.
Kodama, Y., Mashima, J., Kosuge, T., & Ogasawara, O. (2019). DDBJ update: the Genomic Expression Archive (GEA) for functional genomics data. Nucleic acids research, 47D69-D73. https://doi.org/10.1093/nar/gky1002
Kodama, Yuichi, et al. "DDBJ update: the Genomic Expression Archive (GEA) for functional genomics data." Nucleic acids research vol. 47 (2019): D69-D73. doi: https://doi.org/10.1093/nar/gky1002
Kodama Y, Mashima J, Kosuge T, Ogasawara O. DDBJ update: the Genomic Expression Archive (GEA) for functional genomics data. Nucleic Acids Res. 2019 Jan 08;47:D69-D73. doi: 10.1093/nar/gky1002. PMID: 30357349; PMCID: PMC6323915.
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Integrated microRNA‑gene analysis of coronary artery disease based on miRNA and gene expression profiles.

Molecular medicine reports

Xu X, Li H.
PMID: 26936111
Mol Med Rep. 2016 Apr;13(4):3063-73. doi: 10.3892/mmr.2016.4936. Epub 2016 Feb 23.

The present study aimed to investigate the key genes and microRNAs (miRNA/miRs) associated with coronary artery disease (CAD) progression. The gene expression profile of GSE20680 and GSE12288, and the miRNA expression profile of GSE28858 were downloaded from the gene...

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Xu X, Li H. Integrated microRNA‑gene analysis of coronary artery disease based on miRNA and gene expression profiles. Mol Med Rep. 2016;13(4):3063-73doi: 10.3892/mmr.2016.4936.
Xu, X., & Li, H. (2016). Integrated microRNA‑gene analysis of coronary artery disease based on miRNA and gene expression profiles. Molecular medicine reports, 13(4), 3063-73. https://doi.org/10.3892/mmr.2016.4936
Xu, Xiangdong, and Li, Hongsong. "Integrated microRNA‑gene analysis of coronary artery disease based on miRNA and gene expression profiles." Molecular medicine reports vol. 13,4 (2016): 3063-73. doi: https://doi.org/10.3892/mmr.2016.4936
Xu X, Li H. Integrated microRNA‑gene analysis of coronary artery disease based on miRNA and gene expression profiles. Mol Med Rep. 2016 Apr;13(4):3063-73. doi: 10.3892/mmr.2016.4936. Epub 2016 Feb 23. PMID: 26936111; PMCID: PMC4805068.
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Rare variants regulate expression of nearby individual genes in multiple tissues.

PLoS genetics

Li J, Kong N, Han B, Sul JH.
PMID: 34061836
PLoS Genet. 2021 Jun 01;17(6):e1009596. doi: 10.1371/journal.pgen.1009596. eCollection 2021 Jun.

The rapid decrease in sequencing cost has enabled genetic studies to discover rare variants associated with complex diseases and traits. Once this association is identified, the next step is to understand the genetic mechanism of rare variants on how...

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Li J, Kong N, Han B, et al. Rare variants regulate expression of nearby individual genes in multiple tissues. PLoS Genet. 2021;17(6):e1009596doi: 10.1371/journal.pgen.1009596.
Li, J., Kong, N., Han, B., & Sul, J. H. (2021). Rare variants regulate expression of nearby individual genes in multiple tissues. PLoS genetics, 17(6), e1009596. https://doi.org/10.1371/journal.pgen.1009596
Li, Jiajin, et al. "Rare variants regulate expression of nearby individual genes in multiple tissues." PLoS genetics vol. 17,6 (2021): e1009596. doi: https://doi.org/10.1371/journal.pgen.1009596
Li J, Kong N, Han B, Sul JH. Rare variants regulate expression of nearby individual genes in multiple tissues. PLoS Genet. 2021 Jun 01;17(6):e1009596. doi: 10.1371/journal.pgen.1009596. eCollection 2021 Jun. PMID: 34061836; PMCID: PMC8195400.
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Genome-wide pQTL analysis of protein expression regulatory networks in the human liver.

BMC biology

He B, Shi J, Wang X, Jiang H, Zhu HJ.
PMID: 32778093
BMC Biol. 2020 Aug 10;18(1):97. doi: 10.1186/s12915-020-00830-3.

BACKGROUND: Previous expression quantitative trait loci (eQTL) studies have identified thousands of genetic variants to be associated with gene expression at the mRNA level in the human liver. However, protein expression often correlates poorly with mRNA levels. Thus, protein...

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He B, Shi J, Wang X, et al. Genome-wide pQTL analysis of protein expression regulatory networks in the human liver. BMC Biol. 2020;18(1):97doi: 10.1186/s12915-020-00830-3.
He, B., Shi, J., Wang, X., Jiang, H., & Zhu, H. J. (2020). Genome-wide pQTL analysis of protein expression regulatory networks in the human liver. BMC biology, 18(1), 97. https://doi.org/10.1186/s12915-020-00830-3
He, Bing, et al. "Genome-wide pQTL analysis of protein expression regulatory networks in the human liver." BMC biology vol. 18,1 (2020): 97. doi: https://doi.org/10.1186/s12915-020-00830-3
He B, Shi J, Wang X, Jiang H, Zhu HJ. Genome-wide pQTL analysis of protein expression regulatory networks in the human liver. BMC Biol. 2020 Aug 10;18(1):97. doi: 10.1186/s12915-020-00830-3. PMID: 32778093; PMCID: PMC7418398.
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First step toward gene expression data integration: transcriptomic data acquisition with COMMAND>_.

BMC bioinformatics

Moretto M, Sonego P, Villaseñor-Altamirano AB, Engelen K.
PMID: 30691411
BMC Bioinformatics. 2019 Jan 28;20(1):54. doi: 10.1186/s12859-019-2643-6.

BACKGROUND: Exploring cellular responses to stimuli using extensive gene expression profiles has become a routine procedure performed on a daily basis. Raw and processed data from these studies are available on public databases but the opportunity to fully exploit...

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Moretto M, Sonego P, Villaseñor-Altamirano AB, et al. First step toward gene expression data integration: transcriptomic data acquisition with COMMAND>_. BMC Bioinformatics. 2019;20(1):54doi: 10.1186/s12859-019-2643-6.
Moretto, M., Sonego, P., Villaseñor-Altamirano, A. B., & Engelen, K. (2019). First step toward gene expression data integration: transcriptomic data acquisition with COMMAND>_. BMC bioinformatics, 20(1), 54. https://doi.org/10.1186/s12859-019-2643-6
Moretto, Marco, et al. "First step toward gene expression data integration: transcriptomic data acquisition with COMMAND>_." BMC bioinformatics vol. 20,1 (2019): 54. doi: https://doi.org/10.1186/s12859-019-2643-6
Moretto M, Sonego P, Villaseñor-Altamirano AB, Engelen K. First step toward gene expression data integration: transcriptomic data acquisition with COMMAND>_. BMC Bioinformatics. 2019 Jan 28;20(1):54. doi: 10.1186/s12859-019-2643-6. PMID: 30691411; PMCID: PMC6348648.
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miRgo: integrating various off-the-shelf tools for identification of microRNA-target interactions by heterogeneous features and a novel evaluation indicator.

Scientific reports

Chu YW, Chang KP, Chen CW, Liang YT, Soh ZT, Hsieh LC.
PMID: 32001758
Sci Rep. 2020 Jan 30;10(1):1466. doi: 10.1038/s41598-020-58336-5.

MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression and biological processes through binding to messenger RNAs. Predicting the relationship between miRNAs and their targets is crucial for research and clinical applications. Many tools have been developed to...

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Chu YW, Chang KP, Chen CW, et al. miRgo: integrating various off-the-shelf tools for identification of microRNA-target interactions by heterogeneous features and a novel evaluation indicator. Sci Rep. 2020;10(1):1466doi: 10.1038/s41598-020-58336-5.
Chu, Y. W., Chang, K. P., Chen, C. W., Liang, Y. T., Soh, Z. T., & Hsieh, L. C. (2020). miRgo: integrating various off-the-shelf tools for identification of microRNA-target interactions by heterogeneous features and a novel evaluation indicator. Scientific reports, 10(1), 1466. https://doi.org/10.1038/s41598-020-58336-5
Chu, Yen-Wei, et al. "miRgo: integrating various off-the-shelf tools for identification of microRNA-target interactions by heterogeneous features and a novel evaluation indicator." Scientific reports vol. 10,1 (2020): 1466. doi: https://doi.org/10.1038/s41598-020-58336-5
Chu YW, Chang KP, Chen CW, Liang YT, Soh ZT, Hsieh LC. miRgo: integrating various off-the-shelf tools for identification of microRNA-target interactions by heterogeneous features and a novel evaluation indicator. Sci Rep. 2020 Jan 30;10(1):1466. doi: 10.1038/s41598-020-58336-5. PMID: 32001758; PMCID: PMC6992741.
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Integrative computational epigenomics to build data-driven gene regulation hypotheses.

GigaScience

Chen T, Tyagi S.
PMID: 32543653
Gigascience. 2020 Jun 01;9(6). doi: 10.1093/gigascience/giaa064.

BACKGROUND: Diseases are complex phenotypes often arising as an emergent property of a non-linear network of genetic and epigenetic interactions. To translate this resulting state into a causal relationship with a subset of regulatory features, many experiments deploy an...

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Chen T, Tyagi S. Integrative computational epigenomics to build data-driven gene regulation hypotheses. Gigascience. 2020;9(6)doi: 10.1093/gigascience/giaa064.
Chen, T., & Tyagi, S. (2020). Integrative computational epigenomics to build data-driven gene regulation hypotheses. GigaScience, 9(6), . https://doi.org/10.1093/gigascience/giaa064
Chen, Tyrone, and Tyagi, Sonika. "Integrative computational epigenomics to build data-driven gene regulation hypotheses." GigaScience vol. 9,6 (2020). doi: https://doi.org/10.1093/gigascience/giaa064
Chen T, Tyagi S. Integrative computational epigenomics to build data-driven gene regulation hypotheses. Gigascience. 2020 Jun 01;9(6). doi: 10.1093/gigascience/giaa064. PMID: 32543653; PMCID: PMC7297091.
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The beclin 1 interactome: Modification and roles in the pathology of autophagy-related disorders.

Biochimie

Kaur S, Changotra H.
PMID: 32428566
Biochimie. 2020 Aug;175:34-49. doi: 10.1016/j.biochi.2020.04.025. Epub 2020 May 16.

Beclin 1 a yeast Atg6/VPS30 orthologue has a significant role in autophagy process (Macroautophagy) and protein sorting. The function of beclin 1 depends on the interaction with several autophagy-related genes (Atgs) and other proteins during the autophagy process. The...

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Kaur S, Changotra H. The beclin 1 interactome: Modification and roles in the pathology of autophagy-related disorders. Biochimie. 2020;175:34-49doi: 10.1016/j.biochi.2020.04.025.
Kaur, S., & Changotra, H. (2020). The beclin 1 interactome: Modification and roles in the pathology of autophagy-related disorders. Biochimie, 17534-49. https://doi.org/10.1016/j.biochi.2020.04.025
Kaur, Sargeet, and Changotra, Harish. "The beclin 1 interactome: Modification and roles in the pathology of autophagy-related disorders." Biochimie vol. 175 (2020): 34-49. doi: https://doi.org/10.1016/j.biochi.2020.04.025
Kaur S, Changotra H. The beclin 1 interactome: Modification and roles in the pathology of autophagy-related disorders. Biochimie. 2020 Aug;175:34-49. doi: 10.1016/j.biochi.2020.04.025. Epub 2020 May 16. PMID: 32428566.
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MicroRNA: the newest player in gene expression regulation.

Aviation, space, and environmental medicine

Kupfer DM, Burian D.
PMID: 18500056
Aviat Space Environ Med. 2008 May;79(5):550-1.

No abstract available.

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Kupfer DM, Burian D. MicroRNA: the newest player in gene expression regulation. Aviat Space Environ Med. 2008;79(5):550-1
Kupfer, D. M., & Burian, D. (2008). MicroRNA: the newest player in gene expression regulation. Aviation, space, and environmental medicine, 79(5), 550-1.
Kupfer, Doris M, and Burian, Dennis. "MicroRNA: the newest player in gene expression regulation." Aviation, space, and environmental medicine vol. 79,5 (2008): 550-1.
Kupfer DM, Burian D. MicroRNA: the newest player in gene expression regulation. Aviat Space Environ Med. 2008 May;79(5):550-1. PMID: 18500056.
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Taking Data Science to Heart: Next Scale of Gene Regulation.

Current cardiology reports

Chapski DJ, Vondriska TM.
PMID: 33721129
Curr Cardiol Rep. 2021 Mar 15;23(5):46. doi: 10.1007/s11886-021-01467-6.

PURPOSE OF REVIEW: Technical advances have facilitated high-throughput measurements of the genome in the context of cardiovascular biology. These techniques bring a deluge of gargantuan datasets, which in turn present two fundamentally new opportunities for innovation-data processing and knowledge...

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Chapski DJ, Vondriska TM. Taking Data Science to Heart: Next Scale of Gene Regulation. Curr Cardiol Rep. 2021;23(5):46doi: 10.1007/s11886-021-01467-6.
Chapski, D. J., & Vondriska, T. M. (2021). Taking Data Science to Heart: Next Scale of Gene Regulation. Current cardiology reports, 23(5), 46. https://doi.org/10.1007/s11886-021-01467-6
Chapski, Douglas J, and Vondriska, Thomas M. "Taking Data Science to Heart: Next Scale of Gene Regulation." Current cardiology reports vol. 23,5 (2021): 46. doi: https://doi.org/10.1007/s11886-021-01467-6
Chapski DJ, Vondriska TM. Taking Data Science to Heart: Next Scale of Gene Regulation. Curr Cardiol Rep. 2021 Mar 15;23(5):46. doi: 10.1007/s11886-021-01467-6. PMID: 33721129; PMCID: PMC8142693.
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Showing 25 to 36 of 64 entries