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Showing 1 to 12 of 52 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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On methods for gene function scoring as a means of facilitating the interpretation of microarray results.

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

Raghavan N, Amaratunga D, Cabrera J, Nie A, Qin J, McMillian M.
PMID: 16706726
J Comput Biol. 2006 Apr;13(3):798-809. doi: 10.1089/cmb.2006.13.798.

As gene annotation databases continue to evolve and improve, it has become feasible to incorporate the functional and pathway information about genes, available in these databases into the analysis of gene expression data, for a better understanding of the...

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Raghavan N, Amaratunga D, Cabrera J, et al. On methods for gene function scoring as a means of facilitating the interpretation of microarray results. J Comput Biol. 2006;13(3):798-809doi: 10.1089/cmb.2006.13.798.
Raghavan, N., Amaratunga, D., Cabrera, J., Nie, A., Qin, J., & McMillian, M. (2006). On methods for gene function scoring as a means of facilitating the interpretation of microarray results. Journal of computational biology : a journal of computational molecular cell biology, 13(3), 798-809. https://doi.org/10.1089/cmb.2006.13.798
Raghavan, N, et al. "On methods for gene function scoring as a means of facilitating the interpretation of microarray results." Journal of computational biology : a journal of computational molecular cell biology vol. 13,3 (2006): 798-809. doi: https://doi.org/10.1089/cmb.2006.13.798
Raghavan N, Amaratunga D, Cabrera J, Nie A, Qin J, McMillian M. On methods for gene function scoring as a means of facilitating the interpretation of microarray results. J Comput Biol. 2006 Apr;13(3):798-809. doi: 10.1089/cmb.2006.13.798. PMID: 16706726.
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PancanQTL: systematic identification of cis-eQTLs and trans-eQTLs in 33 cancer types.

Nucleic acids research

Gong J, Mei S, Liu C, Xiang Y, Ye Y, Zhang Z, Feng J, Liu R, Diao L, Guo AY, Miao X, Han L.
PMID: 29036324
Nucleic Acids Res. 2018 Jan 04;46:D971-D976. doi: 10.1093/nar/gkx861.

Expression quantitative trait locus (eQTL) analysis, which links variations in gene expression to genotypes, is essential to understanding gene regulation and to interpreting disease-associated loci. Currently identified eQTLs are mainly in samples of blood and other normal tissues. However,...

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Gong J, Mei S, Liu C, et al. PancanQTL: systematic identification of cis-eQTLs and trans-eQTLs in 33 cancer types. Nucleic Acids Res. 2018;46:D971-D976doi: 10.1093/nar/gkx861.
Gong, J., Mei, S., Liu, C., Xiang, Y., Ye, Y., Zhang, Z., Feng, J., Liu, R., Diao, L., Guo, A. Y., Miao, X., & Han, L. (2018). PancanQTL: systematic identification of cis-eQTLs and trans-eQTLs in 33 cancer types. Nucleic acids research, 46D971-D976. https://doi.org/10.1093/nar/gkx861
Gong, Jing, et al. "PancanQTL: systematic identification of cis-eQTLs and trans-eQTLs in 33 cancer types." Nucleic acids research vol. 46 (2018): D971-D976. doi: https://doi.org/10.1093/nar/gkx861
Gong J, Mei S, Liu C, Xiang Y, Ye Y, Zhang Z, Feng J, Liu R, Diao L, Guo AY, Miao X, Han L. PancanQTL: systematic identification of cis-eQTLs and trans-eQTLs in 33 cancer types. Nucleic Acids Res. 2018 Jan 04;46:D971-D976. doi: 10.1093/nar/gkx861. PMID: 29036324; PMCID: PMC5753226.
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Cellular reprogramming dynamics follow a simple 1D reaction coordinate.

Physical biology

Pusuluri ST, Lang AH, Mehta P, Castillo HE.
PMID: 29211687
Phys Biol. 2017 Dec 06;15(1):016001. doi: 10.1088/1478-3975/aa90e0.

Cellular reprogramming, the conversion of one cell type to another, induces global changes in gene expression involving thousands of genes, and understanding how cells globally alter their gene expression profile during reprogramming is an ongoing problem. Here we reanalyze...

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Pusuluri ST, Lang AH, Mehta P, et al. Cellular reprogramming dynamics follow a simple 1D reaction coordinate. Phys Biol. 2017;15(1):016001doi: 10.1088/1478-3975/aa90e0.
Pusuluri, S. T., Lang, A. H., Mehta, P., & Castillo, H. E. (2017). Cellular reprogramming dynamics follow a simple 1D reaction coordinate. Physical biology, 15(1), 016001. https://doi.org/10.1088/1478-3975/aa90e0
Pusuluri, Sai Teja, et al. "Cellular reprogramming dynamics follow a simple 1D reaction coordinate." Physical biology vol. 15,1 (2017): 016001. doi: https://doi.org/10.1088/1478-3975/aa90e0
Pusuluri ST, Lang AH, Mehta P, Castillo HE. Cellular reprogramming dynamics follow a simple 1D reaction coordinate. Phys Biol. 2017 Dec 06;15(1):016001. doi: 10.1088/1478-3975/aa90e0. PMID: 29211687; PMCID: PMC6482097.
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Working with Oligonucleotide Arrays.

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

Carvalho BS.
PMID: 27008013
Methods Mol Biol. 2016;1418:145-59. doi: 10.1007/978-1-4939-3578-9_7.

Preprocessing microarray data consists of a number of statistical procedures that convert the observed intensities into quantities that represent biological events of interest, like gene expression and allele-specific abundances. Here, we present a summary of the theory behind microarray...

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Carvalho BS. Working with Oligonucleotide Arrays. Methods Mol Biol. 2016;1418:145-59doi: 10.1007/978-1-4939-3578-9_7.
Carvalho, B. S. (2016). Working with Oligonucleotide Arrays. Methods in molecular biology (Clifton, N.J.), 1418145-59. https://doi.org/10.1007/978-1-4939-3578-9_7
Carvalho, Benilton S. "Working with Oligonucleotide Arrays." Methods in molecular biology (Clifton, N.J.) vol. 1418 (2016): 145-59. doi: https://doi.org/10.1007/978-1-4939-3578-9_7
Carvalho BS. Working with Oligonucleotide Arrays. Methods Mol Biol. 2016;1418:145-59. doi: 10.1007/978-1-4939-3578-9_7. PMID: 27008013.
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TTCA: an R package for the identification of differentially expressed genes in time course microarray data.

BMC bioinformatics

Albrecht M, Stichel D, Müller B, Merkle R, Sticht C, Gretz N, Klingmüller U, Breuhahn K, Matthäus F.
PMID: 28088176
BMC Bioinformatics. 2017 Jan 14;18(1):33. doi: 10.1186/s12859-016-1440-8.

BACKGROUND: The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other...

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Albrecht M, Stichel D, Müller B, et al. TTCA: an R package for the identification of differentially expressed genes in time course microarray data. BMC Bioinformatics. 2017;18(1):33doi: 10.1186/s12859-016-1440-8.
Albrecht, M., Stichel, D., Müller, B., Merkle, R., Sticht, C., Gretz, N., Klingmüller, U., Breuhahn, K., & Matthäus, F. (2017). TTCA: an R package for the identification of differentially expressed genes in time course microarray data. BMC bioinformatics, 18(1), 33. https://doi.org/10.1186/s12859-016-1440-8
Albrecht, Marco, et al. "TTCA: an R package for the identification of differentially expressed genes in time course microarray data." BMC bioinformatics vol. 18,1 (2017): 33. doi: https://doi.org/10.1186/s12859-016-1440-8
Albrecht M, Stichel D, Müller B, Merkle R, Sticht C, Gretz N, Klingmüller U, Breuhahn K, Matthäus F. TTCA: an R package for the identification of differentially expressed genes in time course microarray data. BMC Bioinformatics. 2017 Jan 14;18(1):33. doi: 10.1186/s12859-016-1440-8. PMID: 28088176; PMCID: PMC5237546.
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Systems biology and heart failure: concepts, methods, and potential research applications.

Heart failure reviews

Adams KF.
PMID: 19396630
Heart Fail Rev. 2010 Jul;15(4):371-98. doi: 10.1007/s10741-009-9138-x.

Dramatic advances in molecular biology dominated twentieth century biomedical science and delineated the function of individual genes and molecules in exquisite detail. However, biological processes cannot be fully understood based on the properties of individual genes and molecules alone,...

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Adams KF. Systems biology and heart failure: concepts, methods, and potential research applications. Heart Fail Rev. 2010;15(4):371-98doi: 10.1007/s10741-009-9138-x.
Adams, K. F. (2010). Systems biology and heart failure: concepts, methods, and potential research applications. Heart failure reviews, 15(4), 371-98. https://doi.org/10.1007/s10741-009-9138-x
Adams, Kirkwood F. "Systems biology and heart failure: concepts, methods, and potential research applications." Heart failure reviews vol. 15,4 (2010): 371-98. doi: https://doi.org/10.1007/s10741-009-9138-x
Adams KF. Systems biology and heart failure: concepts, methods, and potential research applications. Heart Fail Rev. 2010 Jul;15(4):371-98. doi: 10.1007/s10741-009-9138-x. PMID: 19396630.
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FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease.

Genome biology

Chen R, Morgan AA, Dudley J, Deshpande T, Li L, Kodama K, Chiang AP, Butte AJ.
PMID: 19061490
Genome Biol. 2008;9(12):R170. doi: 10.1186/gb-2008-9-12-r170. Epub 2008 Dec 05.

BACKGROUND: Candidate single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWASs) were often selected for validation based on their functional annotation, which was inadequate and biased. We propose to use the more than 200,000 microarray studies in the Gene...

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Chen R, Morgan AA, Dudley J, et al. FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease. Genome Biol. 2008;9(12):R170doi: 10.1186/gb-2008-9-12-r170.
Chen, R., Morgan, A. A., Dudley, J., Deshpande, T., Li, L., Kodama, K., Chiang, A. P., & Butte, A. J. (2008). FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease. Genome biology, 9(12), R170. https://doi.org/10.1186/gb-2008-9-12-r170
Chen, Rong, et al. "FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease." Genome biology vol. 9,12 (2008): R170. doi: https://doi.org/10.1186/gb-2008-9-12-r170
Chen R, Morgan AA, Dudley J, Deshpande T, Li L, Kodama K, Chiang AP, Butte AJ. FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease. Genome Biol. 2008;9(12):R170. doi: 10.1186/gb-2008-9-12-r170. Epub 2008 Dec 05. PMID: 19061490; PMCID: PMC2646274.
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Non-coding RNAs: identification of cancer-associated microRNAs by gene profiling.

Technology in cancer research & treatment

Ferdin J, Kunej T, Calin GA.
PMID: 20218735
Technol Cancer Res Treat. 2010 Apr;9(2):123-38. doi: 10.1177/153303461000900202.

MicroRNAs (miRNAs) belong to the heterogeneous class of non-coding RNAs (ncRNAs), which are by definition RNA molecules that do not encode for proteins, but have instead important structural, catalytic or regulatory functions. In this review we first provide an...

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Ferdin J, Kunej T, Calin GA. Non-coding RNAs: identification of cancer-associated microRNAs by gene profiling. Technol Cancer Res Treat. 2010;9(2):123-38doi: 10.1177/153303461000900202.
Ferdin, J., Kunej, T., & Calin, G. A. (2010). Non-coding RNAs: identification of cancer-associated microRNAs by gene profiling. Technology in cancer research & treatment, 9(2), 123-38. https://doi.org/10.1177/153303461000900202
Ferdin, Jana, et al. "Non-coding RNAs: identification of cancer-associated microRNAs by gene profiling." Technology in cancer research & treatment vol. 9,2 (2010): 123-38. doi: https://doi.org/10.1177/153303461000900202
Ferdin J, Kunej T, Calin GA. Non-coding RNAs: identification of cancer-associated microRNAs by gene profiling. Technol Cancer Res Treat. 2010 Apr;9(2):123-38. doi: 10.1177/153303461000900202. PMID: 20218735.
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Functional annotation and identification of candidate disease genes by computational analysis of normal tissue gene expression data.

PloS one

Miozzi L, Piro RM, Rosa F, Ala U, Silengo L, Di Cunto F, Provero P.
PMID: 18560577
PLoS One. 2008 Jun 18;3(6):e2439. doi: 10.1371/journal.pone.0002439.

BACKGROUND: High-throughput gene expression data can predict gene function through the "guilt by association" principle: coexpressed genes are likely to be functionally associated.METHODOLOGY/PRINCIPAL FINDINGS: We analyzed publicly available expression data on normal human tissues. The analysis is based on...

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Miozzi L, Piro RM, Rosa F, et al. Functional annotation and identification of candidate disease genes by computational analysis of normal tissue gene expression data. PLoS One. 2008;3(6):e2439doi: 10.1371/journal.pone.0002439.
Miozzi, L., Piro, R. M., Rosa, F., Ala, U., Silengo, L., Di Cunto, F., & Provero, P. (2008). Functional annotation and identification of candidate disease genes by computational analysis of normal tissue gene expression data. PloS one, 3(6), e2439. https://doi.org/10.1371/journal.pone.0002439
Miozzi, Laura, et al. "Functional annotation and identification of candidate disease genes by computational analysis of normal tissue gene expression data." PloS one vol. 3,6 (2008): e2439. doi: https://doi.org/10.1371/journal.pone.0002439
Miozzi L, Piro RM, Rosa F, Ala U, Silengo L, Di Cunto F, Provero P. Functional annotation and identification of candidate disease genes by computational analysis of normal tissue gene expression data. PLoS One. 2008 Jun 18;3(6):e2439. doi: 10.1371/journal.pone.0002439. PMID: 18560577; PMCID: PMC2409962.
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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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QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics.

BMC bioinformatics

O'Reilly PG, Wen Q, Bankhead P, Dunne PD, McArt DG, McPherson S, Hamilton PW, Mills KI, Zhang SD.
PMID: 27143038
BMC Bioinformatics. 2016 May 04;17(1):198. doi: 10.1186/s12859-016-1062-1.

BACKGROUND: Gene expression connectivity mapping has proven to be a powerful and flexible tool for research. Its application has been shown in a broad range of research topics, most commonly as a means of identifying potential small molecule compounds,...

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O'Reilly PG, Wen Q, Bankhead P, et al. QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics. BMC Bioinformatics. 2016;17(1):198doi: 10.1186/s12859-016-1062-1.
O'Reilly, P. G., Wen, Q., Bankhead, P., Dunne, P. D., McArt, D. G., McPherson, S., Hamilton, P. W., Mills, K. I., & Zhang, S. D. (2016). QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics. BMC bioinformatics, 17(1), 198. https://doi.org/10.1186/s12859-016-1062-1
O'Reilly, Paul G, et al. "QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics." BMC bioinformatics vol. 17,1 (2016): 198. doi: https://doi.org/10.1186/s12859-016-1062-1
O'Reilly PG, Wen Q, Bankhead P, Dunne PD, McArt DG, McPherson S, Hamilton PW, Mills KI, Zhang SD. QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics. BMC Bioinformatics. 2016 May 04;17(1):198. doi: 10.1186/s12859-016-1062-1. PMID: 27143038; PMCID: PMC4855472.
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Showing 1 to 12 of 52 entries