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Showing 1 to 9 of 9 entries
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Gene table: epilepsy (update).

European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society

Elmslie F.
PMID: 10817491
Eur J Paediatr Neurol. 2000;4(2):87-90. doi: 10.1053/ejpn.2000.0269.

No abstract available.

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Elmslie F. Gene table: epilepsy (update). Eur J Paediatr Neurol. 2000;4(2):87-90doi: 10.1053/ejpn.2000.0269.
Elmslie, F. (2000). Gene table: epilepsy (update). European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society, 4(2), 87-90. https://doi.org/10.1053/ejpn.2000.0269
Elmslie, F. "Gene table: epilepsy (update)." European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society vol. 4,2 (2000): 87-90. doi: https://doi.org/10.1053/ejpn.2000.0269
Elmslie F. Gene table: epilepsy (update). Eur J Paediatr Neurol. 2000;4(2):87-90. doi: 10.1053/ejpn.2000.0269. PMID: 10817491.
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Meta-Analysis of Gene Expression in Autism Spectrum Disorder.

Autism research : official journal of the International Society for Autism Research

Ch'ng C, Kwok W, Rogic S, Pavlidis P.
PMID: 25720351
Autism Res. 2015 Oct;8(5):593-608. doi: 10.1002/aur.1475. Epub 2015 Feb 26.

Autism spectrum disorders (ASD) are clinically heterogeneous and biologically complex. In general it remains unclear, what biological factors lead to changes in the brains of autistic individuals. A considerable number of transcriptome analyses have been performed in attempts to...

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Ch'ng C, Kwok W, Rogic S, et al. Meta-Analysis of Gene Expression in Autism Spectrum Disorder. Autism Res. 2015;8(5):593-608doi: 10.1002/aur.1475.
Ch'ng, C., Kwok, W., Rogic, S., & Pavlidis, P. (2015). Meta-Analysis of Gene Expression in Autism Spectrum Disorder. Autism research : official journal of the International Society for Autism Research, 8(5), 593-608. https://doi.org/10.1002/aur.1475
Ch'ng, Carolyn, et al. "Meta-Analysis of Gene Expression in Autism Spectrum Disorder." Autism research : official journal of the International Society for Autism Research vol. 8,5 (2015): 593-608. doi: https://doi.org/10.1002/aur.1475
Ch'ng C, Kwok W, Rogic S, Pavlidis P. Meta-Analysis of Gene Expression in Autism Spectrum Disorder. Autism Res. 2015 Oct;8(5):593-608. doi: 10.1002/aur.1475. Epub 2015 Feb 26. PMID: 25720351; PMCID: PMC4933311.
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Genetic and gene expression signatures in multiple sclerosis.

Multiple sclerosis (Houndmills, Basingstoke, England)

Patsopoulos NA, De Jager PL.
PMID: 31965883
Mult Scler. 2020 Apr;26(5):576-581. doi: 10.1177/1352458519898332. Epub 2020 Jan 22.

Multiple sclerosis (MS) exhibits a well-documented increased incidence in individuals with respective family history, that is, is a heritable disease. In the last decade, genome-wide association studies have enabled the agnostic interrogation of the whole genome at a large...

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Patsopoulos NA, De Jager PL. Genetic and gene expression signatures in multiple sclerosis. Mult Scler. 2020;26(5):576-581doi: 10.1177/1352458519898332.
Patsopoulos, N. A., & De Jager, P. L. (2020). Genetic and gene expression signatures in multiple sclerosis. Multiple sclerosis (Houndmills, Basingstoke, England), 26(5), 576-581. https://doi.org/10.1177/1352458519898332
Patsopoulos, Nikolaos A, and De Jager, Philip L. "Genetic and gene expression signatures in multiple sclerosis." Multiple sclerosis (Houndmills, Basingstoke, England) vol. 26,5 (2020): 576-581. doi: https://doi.org/10.1177/1352458519898332
Patsopoulos NA, De Jager PL. Genetic and gene expression signatures in multiple sclerosis. Mult Scler. 2020 Apr;26(5):576-581. doi: 10.1177/1352458519898332. Epub 2020 Jan 22. PMID: 31965883.
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Systems genetics - deciphering the complex disease with a systems approach.

Biochimica et biophysica acta

Cai Y, Huang T.
PMID: 27487032
Biochim Biophys Acta. 2016 Nov;1860(11):2611-2. doi: 10.1016/j.bbagen.2016.07.034. Epub 2016 Jul 31.

No abstract available.

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Cai Y, Huang T. Systems genetics - deciphering the complex disease with a systems approach. Biochim Biophys Acta. 2016;1860(11):2611-2doi: 10.1016/j.bbagen.2016.07.034.
Cai, Y., & Huang, T. (2016). Systems genetics - deciphering the complex disease with a systems approach. Biochimica et biophysica acta, 1860(11), 2611-2. https://doi.org/10.1016/j.bbagen.2016.07.034
Cai, Yudong, and Huang, Tao. "Systems genetics - deciphering the complex disease with a systems approach." Biochimica et biophysica acta vol. 1860,11 (2016): 2611-2. doi: https://doi.org/10.1016/j.bbagen.2016.07.034
Cai Y, Huang T. Systems genetics - deciphering the complex disease with a systems approach. Biochim Biophys Acta. 2016 Nov;1860(11):2611-2. doi: 10.1016/j.bbagen.2016.07.034. Epub 2016 Jul 31. PMID: 27487032.
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NICE Advice - Prolaris gene expression assay for assessing long-term risk of prostate cancer progression: © NICE (2016) Prolaris gene expression assay for assessing long-term risk of prostate cancer progression.

BJU international

[No authors listed]
PMID: 30134062
BJU Int. 2018 Aug;122(2):173-180. doi: 10.1111/bju.14452.

No abstract available.

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NICE Advice - Prolaris gene expression assay for assessing long-term risk of prostate cancer progression: © NICE (2016) Prolaris gene expression assay for assessing long-term risk of prostate cancer progression. BJU Int. 2018;122(2):173-180doi: 10.1111/bju.14452.
(2018). NICE Advice - Prolaris gene expression assay for assessing long-term risk of prostate cancer progression: © NICE (2016) Prolaris gene expression assay for assessing long-term risk of prostate cancer progression. BJU international, 122(2), 173-180. https://doi.org/10.1111/bju.14452
"NICE Advice - Prolaris gene expression assay for assessing long-term risk of prostate cancer progression: © NICE (2016) Prolaris gene expression assay for assessing long-term risk of prostate cancer progression." BJU international vol. 122,2 (2018): 173-180. doi: https://doi.org/10.1111/bju.14452
NICE Advice - Prolaris gene expression assay for assessing long-term risk of prostate cancer progression: © NICE (2016) Prolaris gene expression assay for assessing long-term risk of prostate cancer progression. BJU Int. 2018 Aug;122(2):173-180. doi: 10.1111/bju.14452. PMID: 30134062.
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Short paired-end reads trump long single-end reads for expression analysis.

BMC bioinformatics

Freedman AH, Gaspar JM, Sackton TB.
PMID: 32306895
BMC Bioinformatics. 2020 Apr 19;21(1):149. doi: 10.1186/s12859-020-3484-z.

BACKGROUND: Typical experimental design advice for expression analyses using RNA-seq generally assumes that single-end reads provide robust gene-level expression estimates in a cost-effective manner, and that the additional benefits obtained from paired-end sequencing are not worth the additional cost....

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Freedman AH, Gaspar JM, Sackton TB. Short paired-end reads trump long single-end reads for expression analysis. BMC Bioinformatics. 2020;21(1):149doi: 10.1186/s12859-020-3484-z.
Freedman, A. H., Gaspar, J. M., & Sackton, T. B. (2020). Short paired-end reads trump long single-end reads for expression analysis. BMC bioinformatics, 21(1), 149. https://doi.org/10.1186/s12859-020-3484-z
Freedman, Adam H, et al. "Short paired-end reads trump long single-end reads for expression analysis." BMC bioinformatics vol. 21,1 (2020): 149. doi: https://doi.org/10.1186/s12859-020-3484-z
Freedman AH, Gaspar JM, Sackton TB. Short paired-end reads trump long single-end reads for expression analysis. BMC Bioinformatics. 2020 Apr 19;21(1):149. doi: 10.1186/s12859-020-3484-z. PMID: 32306895; PMCID: PMC7168855.
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Integrating predicted transcriptome from multiple tissues improves association detection.

PLoS genetics

Barbeira AN, Pividori M, Zheng J, Wheeler HE, Nicolae DL, Im HK.
PMID: 30668570
PLoS Genet. 2019 Jan 22;15(1):e1007889. doi: 10.1371/journal.pgen.1007889. eCollection 2019 Jan.

Integration of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is needed to improve our understanding of the biological mechanisms underlying GWAS hits, and our ability to identify therapeutic targets. Gene-level association methods such as PrediXcan...

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Barbeira AN, Pividori M, Zheng J, et al. Integrating predicted transcriptome from multiple tissues improves association detection. PLoS Genet. 2019;15(1):e1007889doi: 10.1371/journal.pgen.1007889.
Barbeira, A. N., Pividori, M., Zheng, J., Wheeler, H. E., Nicolae, D. L., & Im, H. K. (2019). Integrating predicted transcriptome from multiple tissues improves association detection. PLoS genetics, 15(1), e1007889. https://doi.org/10.1371/journal.pgen.1007889
Barbeira, Alvaro N, et al. "Integrating predicted transcriptome from multiple tissues improves association detection." PLoS genetics vol. 15,1 (2019): e1007889. doi: https://doi.org/10.1371/journal.pgen.1007889
Barbeira AN, Pividori M, Zheng J, Wheeler HE, Nicolae DL, Im HK. Integrating predicted transcriptome from multiple tissues improves association detection. PLoS Genet. 2019 Jan 22;15(1):e1007889. doi: 10.1371/journal.pgen.1007889. eCollection 2019 Jan. PMID: 30668570; PMCID: PMC6358100.
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NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis.

Nucleic acids research

Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J.
PMID: 30931480
Nucleic Acids Res. 2019 Jul 02;47:W234-W241. doi: 10.1093/nar/gkz240.

The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein...

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Zhou G, Soufan O, Ewald J, et al. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019;47:W234-W241doi: 10.1093/nar/gkz240.
Zhou, G., Soufan, O., Ewald, J., Hancock, R. E. W., Basu, N., & Xia, J. (2019). NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic acids research, 47W234-W241. https://doi.org/10.1093/nar/gkz240
Zhou, Guangyan, et al. "NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis." Nucleic acids research vol. 47 (2019): W234-W241. doi: https://doi.org/10.1093/nar/gkz240
Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019 Jul 02;47:W234-W241. doi: 10.1093/nar/gkz240. PMID: 30931480; PMCID: PMC6602507.
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ROC'n'Ribo: Characterizing a Riboswitching Expression System by Modeling Single-Cell Data.

ACS synthetic biology

Schneider C, Bronstein L, Diemer J, Koeppl H, Suess B.
PMID: 28591515
ACS Synth Biol. 2017 Jul 21;6(7):1211-1224. doi: 10.1021/acssynbio.6b00322. Epub 2017 Jun 30.

RNA-engineered systems offer simple and versatile control over gene expression in many organisms. In particular, the design and implementation of riboswitches presents a unique opportunity to manipulate any reporter device in cis, executing tight temporal and spatial control at...

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Schneider C, Bronstein L, Diemer J, et al. ROC'n'Ribo: Characterizing a Riboswitching Expression System by Modeling Single-Cell Data. ACS Synth Biol. 2017;6(7):1211-1224doi: 10.1021/acssynbio.6b00322.
Schneider, C., Bronstein, L., Diemer, J., Koeppl, H., & Suess, B. (2017). ROC'n'Ribo: Characterizing a Riboswitching Expression System by Modeling Single-Cell Data. ACS synthetic biology, 6(7), 1211-1224. https://doi.org/10.1021/acssynbio.6b00322
Schneider, Christopher, et al. "ROC'n'Ribo: Characterizing a Riboswitching Expression System by Modeling Single-Cell Data." ACS synthetic biology vol. 6,7 (2017): 1211-1224. doi: https://doi.org/10.1021/acssynbio.6b00322
Schneider C, Bronstein L, Diemer J, Koeppl H, Suess B. ROC'n'Ribo: Characterizing a Riboswitching Expression System by Modeling Single-Cell Data. ACS Synth Biol. 2017 Jul 21;6(7):1211-1224. doi: 10.1021/acssynbio.6b00322. Epub 2017 Jun 30. PMID: 28591515.
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Showing 1 to 9 of 9 entries