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Detection of gene annotations and protein-protein interaction associated disorders through transitive relationships between integrated annotations.

BMC genomics

Masseroli M, Canakoglu A, Quigliatti M.
PMID: 26046679
BMC Genomics. 2015;16:S5. doi: 10.1186/1471-2164-16-S6-S5. Epub 2015 Jun 01.

BACKGROUND: Increasingly high amounts of heterogeneous and valuable controlled biomolecular annotations are available, but far from exhaustive and scattered in many databases. Several annotation integration and prediction approaches have been proposed, but these issues are still unsolved. We previously...

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Masseroli M, Canakoglu A, Quigliatti M. Detection of gene annotations and protein-protein interaction associated disorders through transitive relationships between integrated annotations. BMC Genomics. 2015;16:S5doi: 10.1186/1471-2164-16-S6-S5.
Masseroli, M., Canakoglu, A., & Quigliatti, M. (2015). Detection of gene annotations and protein-protein interaction associated disorders through transitive relationships between integrated annotations. BMC genomics, 16S5. https://doi.org/10.1186/1471-2164-16-S6-S5
Masseroli, Marco, et al. "Detection of gene annotations and protein-protein interaction associated disorders through transitive relationships between integrated annotations." BMC genomics vol. 16 (2015): S5. doi: https://doi.org/10.1186/1471-2164-16-S6-S5
Masseroli M, Canakoglu A, Quigliatti M. Detection of gene annotations and protein-protein interaction associated disorders through transitive relationships between integrated annotations. BMC Genomics. 2015;16:S5. doi: 10.1186/1471-2164-16-S6-S5. Epub 2015 Jun 01. PMID: 26046679; PMCID: PMC4460591.
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Integrative analysis of human omics data using biomolecular networks.

Molecular bioSystems

Robinson JL, Nielsen J.
PMID: 27510223
Mol Biosyst. 2016 Oct 20;12(10):2953-64. doi: 10.1039/c6mb00476h. Epub 2016 Aug 11.

High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their...

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Robinson JL, Nielsen J. Integrative analysis of human omics data using biomolecular networks. Mol Biosyst. 2016;12(10):2953-64doi: 10.1039/c6mb00476h.
Robinson, J. L., & Nielsen, J. (2016). Integrative analysis of human omics data using biomolecular networks. Molecular bioSystems, 12(10), 2953-64. https://doi.org/10.1039/c6mb00476h
Robinson, Jonathan L, and Nielsen, Jens. "Integrative analysis of human omics data using biomolecular networks." Molecular bioSystems vol. 12,10 (2016): 2953-64. doi: https://doi.org/10.1039/c6mb00476h
Robinson JL, Nielsen J. Integrative analysis of human omics data using biomolecular networks. Mol Biosyst. 2016 Oct 20;12(10):2953-64. doi: 10.1039/c6mb00476h. Epub 2016 Aug 11. PMID: 27510223.
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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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Commentary: Inheritance of acquired characteristics: eliminating alternatives in the search for mechanisms. Commentary on Galton F: Feasible experiments on the possibility of transmitting acquired habits by means of inheritance.

International journal of epidemiology

Dias BG.
PMID: 26989214
Int J Epidemiol. 2016 Feb;45(1):20-3. doi: 10.1093/ije/dyw031.

No abstract available.

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Dias BG. Commentary: Inheritance of acquired characteristics: eliminating alternatives in the search for mechanisms. Commentary on Galton F: Feasible experiments on the possibility of transmitting acquired habits by means of inheritance. Int J Epidemiol. 2016;45(1):20-3doi: 10.1093/ije/dyw031.
Dias, B. G. (2016). Commentary: Inheritance of acquired characteristics: eliminating alternatives in the search for mechanisms. Commentary on Galton F: Feasible experiments on the possibility of transmitting acquired habits by means of inheritance. International journal of epidemiology, 45(1), 20-3. https://doi.org/10.1093/ije/dyw031
Dias, B G. "Commentary: Inheritance of acquired characteristics: eliminating alternatives in the search for mechanisms. Commentary on Galton F: Feasible experiments on the possibility of transmitting acquired habits by means of inheritance." International journal of epidemiology vol. 45,1 (2016): 20-3. doi: https://doi.org/10.1093/ije/dyw031
Dias BG. Commentary: Inheritance of acquired characteristics: eliminating alternatives in the search for mechanisms. Commentary on Galton F: Feasible experiments on the possibility of transmitting acquired habits by means of inheritance. Int J Epidemiol. 2016 Feb;45(1):20-3. doi: 10.1093/ije/dyw031. PMID: 26989214; PMCID: PMC5013890.
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Applying stability selection to consistently estimate sparse principal components in high-dimensional molecular data.

Bioinformatics (Oxford, England)

Sill M, Saadati M, Benner A.
PMID: 25861969
Bioinformatics. 2015 Aug 15;31(16):2683-90. doi: 10.1093/bioinformatics/btv197. Epub 2015 Apr 10.

MOTIVATION: Principal component analysis (PCA) is a basic tool often used in bioinformatics for visualization and dimension reduction. However, it is known that PCA may not consistently estimate the true direction of maximal variability in high-dimensional, low sample size...

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Sill M, Saadati M, Benner A. Applying stability selection to consistently estimate sparse principal components in high-dimensional molecular data. Bioinformatics. 2015;31(16):2683-90doi: 10.1093/bioinformatics/btv197.
Sill, M., Saadati, M., & Benner, A. (2015). Applying stability selection to consistently estimate sparse principal components in high-dimensional molecular data. Bioinformatics (Oxford, England), 31(16), 2683-90. https://doi.org/10.1093/bioinformatics/btv197
Sill, Martin, et al. "Applying stability selection to consistently estimate sparse principal components in high-dimensional molecular data." Bioinformatics (Oxford, England) vol. 31,16 (2015): 2683-90. doi: https://doi.org/10.1093/bioinformatics/btv197
Sill M, Saadati M, Benner A. Applying stability selection to consistently estimate sparse principal components in high-dimensional molecular data. Bioinformatics. 2015 Aug 15;31(16):2683-90. doi: 10.1093/bioinformatics/btv197. Epub 2015 Apr 10. PMID: 25861969; PMCID: PMC4528629.
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Network-Based Analysis of Schizophrenia Genome-Wide Association Data to Detect the Joint Functional Association Signals.

PloS one

Chang S, Fang K, Zhang K, Wang J.
PMID: 26193471
PLoS One. 2015 Jul 20;10(7):e0133404. doi: 10.1371/journal.pone.0133404. eCollection 2015.

Schizophrenia is a common psychiatric disorder with high heritability and complex genetic architecture. Genome-wide association studies (GWAS) have identified several significant loci associated with schizophrenia. However, the explained heritability is still low. Growing evidence has shown schizophrenia is attributable...

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Chang S, Fang K, Zhang K, et al. Network-Based Analysis of Schizophrenia Genome-Wide Association Data to Detect the Joint Functional Association Signals. PLoS One. 2015;10(7):e0133404doi: 10.1371/journal.pone.0133404.
Chang, S., Fang, K., Zhang, K., & Wang, J. (2015). Network-Based Analysis of Schizophrenia Genome-Wide Association Data to Detect the Joint Functional Association Signals. PloS one, 10(7), e0133404. https://doi.org/10.1371/journal.pone.0133404
Chang, Suhua, et al. "Network-Based Analysis of Schizophrenia Genome-Wide Association Data to Detect the Joint Functional Association Signals." PloS one vol. 10,7 (2015): e0133404. doi: https://doi.org/10.1371/journal.pone.0133404
Chang S, Fang K, Zhang K, Wang J. Network-Based Analysis of Schizophrenia Genome-Wide Association Data to Detect the Joint Functional Association Signals. PLoS One. 2015 Jul 20;10(7):e0133404. doi: 10.1371/journal.pone.0133404. eCollection 2015. PMID: 26193471; PMCID: PMC4508050.
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DISEASES: text mining and data integration of disease-gene associations.

Methods (San Diego, Calif.)

Pletscher-Frankild S, Pallejà A, Tsafou K, Binder JX, Jensen LJ.
PMID: 25484339
Methods. 2015 Mar;74:83-9. doi: 10.1016/j.ymeth.2014.11.020. Epub 2014 Dec 05.

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for...

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Pletscher-Frankild S, Pallejà A, Tsafou K, et al. DISEASES: text mining and data integration of disease-gene associations. Methods. 2014;74:83-9doi: 10.1016/j.ymeth.2014.11.020.
Pletscher-Frankild, S., Pallejà, A., Tsafou, K., Binder, J. X., & Jensen, L. J. (2015). DISEASES: text mining and data integration of disease-gene associations. Methods (San Diego, Calif.), 7483-9. https://doi.org/10.1016/j.ymeth.2014.11.020
Pletscher-Frankild, Sune, et al. "DISEASES: text mining and data integration of disease-gene associations." Methods (San Diego, Calif.) vol. 74 (2015): 83-9. doi: https://doi.org/10.1016/j.ymeth.2014.11.020
Pletscher-Frankild S, Pallejà A, Tsafou K, Binder JX, Jensen LJ. DISEASES: text mining and data integration of disease-gene associations. Methods. 2015 Mar;74:83-9. doi: 10.1016/j.ymeth.2014.11.020. Epub 2014 Dec 05. PMID: 25484339.
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Gene-environment interactions--the BioBank UK study.

The pharmacogenomics journal

Wright AF, Carothers AD, Campbell H.
PMID: 12049178
Pharmacogenomics J. 2002;2(2):75-82. doi: 10.1038/sj.tpj.6500085.

No abstract available.

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Wright AF, Carothers AD, Campbell H. Gene-environment interactions--the BioBank UK study. Pharmacogenomics J. 2002;2(2):75-82doi: 10.1038/sj.tpj.6500085.
Wright, A. F., Carothers, A. D., & Campbell, H. (2002). Gene-environment interactions--the BioBank UK study. The pharmacogenomics journal, 2(2), 75-82. https://doi.org/10.1038/sj.tpj.6500085
Wright, A F, et al. "Gene-environment interactions--the BioBank UK study." The pharmacogenomics journal vol. 2,2 (2002): 75-82. doi: https://doi.org/10.1038/sj.tpj.6500085
Wright AF, Carothers AD, Campbell H. Gene-environment interactions--the BioBank UK study. Pharmacogenomics J. 2002;2(2):75-82. doi: 10.1038/sj.tpj.6500085. PMID: 12049178.
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DRAGON View: information visualization for annotated microarray data.

Bioinformatics (Oxford, England)

Bouton CM, Pevsner J.
PMID: 11847082
Bioinformatics. 2002 Feb;18(2):323-4. doi: 10.1093/bioinformatics/18.2.323.

The DRAGON View information visualization tools aid in the comprehensive analysis of large-scale gene expression data that has been annotated with biologically relevant information through the generation of three types of complementary graphical outputs.

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Bouton CM, Pevsner J. DRAGON View: information visualization for annotated microarray data. Bioinformatics. 2002;18(2):323-4doi: 10.1093/bioinformatics/18.2.323.
Bouton, C. M., & Pevsner, J. (2002). DRAGON View: information visualization for annotated microarray data. Bioinformatics (Oxford, England), 18(2), 323-4. https://doi.org/10.1093/bioinformatics/18.2.323
Bouton, Christopher M L S, and Pevsner, Jonathan. "DRAGON View: information visualization for annotated microarray data." Bioinformatics (Oxford, England) vol. 18,2 (2002): 323-4. doi: https://doi.org/10.1093/bioinformatics/18.2.323
Bouton CM, Pevsner J. DRAGON View: information visualization for annotated microarray data. Bioinformatics. 2002 Feb;18(2):323-4. doi: 10.1093/bioinformatics/18.2.323. PMID: 11847082.
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GoMiner: a resource for biological interpretation of genomic and proteomic data.

Genome biology

Zeeberg BR, Feng W, Wang G, Wang MD, Fojo AT, Sunshine M, Narasimhan S, Kane DW, Reinhold WC, Lababidi S, Bussey KJ, Riss J, Barrett JC, Weinstein JN.
PMID: 12702209
Genome Biol. 2003;4(4):R28. doi: 10.1186/gb-2003-4-4-r28. Epub 2003 Mar 25.

We have developed GoMiner, a program package that organizes lists of 'interesting' genes (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. GoMiner provides quantitative and statistical output...

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Zeeberg BR, Feng W, Wang G, et al. GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol. 2003;4(4):R28doi: 10.1186/gb-2003-4-4-r28.
Zeeberg, B. R., Feng, W., Wang, G., Wang, M. D., Fojo, A. T., Sunshine, M., Narasimhan, S., Kane, D. W., Reinhold, W. C., Lababidi, S., Bussey, K. J., Riss, J., Barrett, J. C., & Weinstein, J. N. (2003). GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome biology, 4(4), R28. https://doi.org/10.1186/gb-2003-4-4-r28
Zeeberg, Barry R, et al. "GoMiner: a resource for biological interpretation of genomic and proteomic data." Genome biology vol. 4,4 (2003): R28. doi: https://doi.org/10.1186/gb-2003-4-4-r28
Zeeberg BR, Feng W, Wang G, Wang MD, Fojo AT, Sunshine M, Narasimhan S, Kane DW, Reinhold WC, Lababidi S, Bussey KJ, Riss J, Barrett JC, Weinstein JN. GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol. 2003;4(4):R28. doi: 10.1186/gb-2003-4-4-r28. Epub 2003 Mar 25. PMID: 12702209; PMCID: PMC154579.
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Chemical geneticists unify their data.

Drug discovery today

Mandavilli A.
PMID: 12547009
Drug Discov Today. 2002 Dec 01;7(23):1148-9. doi: 10.1016/s1359-6446(02)02523-0.

No abstract available.

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Mandavilli A. Chemical geneticists unify their data. Drug Discov Today. 2002;7(23):1148-9doi: 10.1016/s1359-6446(02)02523-0.
Mandavilli, A. (2002). Chemical geneticists unify their data. Drug discovery today, 7(23), 1148-9. https://doi.org/10.1016/s1359-6446(02)02523-0
Mandavilli, Apoorva. "Chemical geneticists unify their data." Drug discovery today vol. 7,23 (2002): 1148-9. doi: https://doi.org/10.1016/s1359-6446(02)02523-0
Mandavilli A. Chemical geneticists unify their data. Drug Discov Today. 2002 Dec 01;7(23):1148-9. doi: 10.1016/s1359-6446(02)02523-0. PMID: 12547009.
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Finding your way.

Nature

Buckingham S.
PMID: 12968187
Nature. 2003 Sep 11;425(6954):209. doi: 10.1038/425209b.

No abstract available.

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Buckingham S. Finding your way. Nature. 2003;425(6954):209doi: 10.1038/425209b.
Buckingham, S. (2003). Finding your way. Nature, 425(6954), 209. https://doi.org/10.1038/425209b
Buckingham, Steve. "Finding your way." Nature vol. 425,6954 (2003): 209. doi: https://doi.org/10.1038/425209b
Buckingham S. Finding your way. Nature. 2003 Sep 11;425(6954):209. doi: 10.1038/425209b. PMID: 12968187.
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Showing 13 to 24 of 455 entries