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Prediction of relevant biomedical documents: a human microbiome case study.

BioData mining

Thompson P, Madan JC, Moore JH.
PMID: 26361503
BioData Min. 2015 Sep 10;8:28. doi: 10.1186/s13040-015-0061-5. eCollection 2015.

BACKGROUND: Retrieving relevant biomedical literature has become increasingly difficult due to the large volume and rapid growth of biomedical publication. A query to a biomedical retrieval system often retrieves hundreds of results. Since the searcher will not likely consider...

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Thompson P, Madan JC, Moore JH. Prediction of relevant biomedical documents: a human microbiome case study. BioData Min. 2015;8:28doi: 10.1186/s13040-015-0061-5.
Thompson, P., Madan, J. C., & Moore, J. H. (2015). Prediction of relevant biomedical documents: a human microbiome case study. BioData mining, 828. https://doi.org/10.1186/s13040-015-0061-5
Thompson, Paul, et al. "Prediction of relevant biomedical documents: a human microbiome case study." BioData mining vol. 8 (2015): 28. doi: https://doi.org/10.1186/s13040-015-0061-5
Thompson P, Madan JC, Moore JH. Prediction of relevant biomedical documents: a human microbiome case study. BioData Min. 2015 Sep 10;8:28. doi: 10.1186/s13040-015-0061-5. eCollection 2015. PMID: 26361503; PMCID: PMC4564977.
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NETWORK-BASED GENOME WIDE STUDY OF HIPPOCAMPAL IMAGING PHENOTYPE IN ALZHEIMER'S DISEASE TO IDENTIFY FUNCTIONAL INTERACTION MODULES.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)

Yao X, Yan J, Risacher S, Moore J, Saykin A, Shen L.
PMID: 28989328
Proc IEEE Int Conf Acoust Speech Signal Process. 2017;2017:6170-6174. doi: 10.1109/ICASSP.2017.7953342. Epub 2017 Jun 19.

Identification of functional modules from biological network is a promising approach to enhance the statistical power of genome-wide association study (GWAS) and improve biological interpretation for complex diseases. The precise functions of genes are highly relevant to tissue context,...

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Yao X, Yan J, Risacher S, et al. NETWORK-BASED GENOME WIDE STUDY OF HIPPOCAMPAL IMAGING PHENOTYPE IN ALZHEIMER'S DISEASE TO IDENTIFY FUNCTIONAL INTERACTION MODULES. Proc IEEE Int Conf Acoust Speech Signal Process. 2017;2017:6170-6174doi: 10.1109/ICASSP.2017.7953342.
Yao, X., Yan, J., Risacher, S., Moore, J., Saykin, A., & Shen, L. (2017). NETWORK-BASED GENOME WIDE STUDY OF HIPPOCAMPAL IMAGING PHENOTYPE IN ALZHEIMER'S DISEASE TO IDENTIFY FUNCTIONAL INTERACTION MODULES. Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference), 20176170-6174. https://doi.org/10.1109/ICASSP.2017.7953342
Yao, Xiaohui, et al. "NETWORK-BASED GENOME WIDE STUDY OF HIPPOCAMPAL IMAGING PHENOTYPE IN ALZHEIMER'S DISEASE TO IDENTIFY FUNCTIONAL INTERACTION MODULES." Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) vol. 2017 (2017): 6170-6174. doi: https://doi.org/10.1109/ICASSP.2017.7953342
Yao X, Yan J, Risacher S, Moore J, Saykin A, Shen L. NETWORK-BASED GENOME WIDE STUDY OF HIPPOCAMPAL IMAGING PHENOTYPE IN ALZHEIMER'S DISEASE TO IDENTIFY FUNCTIONAL INTERACTION MODULES. Proc IEEE Int Conf Acoust Speech Signal Process. 2017;2017:6170-6174. doi: 10.1109/ICASSP.2017.7953342. Epub 2017 Jun 19. PMID: 28989328; PMCID: PMC5627518.
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Variant Set Enrichment: an R package to identify disease-associated functional genomic regions.

BioData mining

Ahmed M, Sallari RC, Guo H, Moore JH, He HH, Lupien M.
PMID: 28239419
BioData Min. 2017 Feb 22;10:9. doi: 10.1186/s13040-017-0129-5. eCollection 2017.

BACKGROUND: Genetic predispositions to diseases populate the noncoding regions of the human genome. Delineating their functional basis can inform on the mechanisms contributing to disease development. However, this remains a challenge due to the poor characterization of the noncoding...

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Ahmed M, Sallari RC, Guo H, et al. Variant Set Enrichment: an R package to identify disease-associated functional genomic regions. BioData Min. 2017;10:9doi: 10.1186/s13040-017-0129-5.
Ahmed, M., Sallari, R. C., Guo, H., Moore, J. H., He, H. H., & Lupien, M. (2017). Variant Set Enrichment: an R package to identify disease-associated functional genomic regions. BioData mining, 109. https://doi.org/10.1186/s13040-017-0129-5
Ahmed, Musaddeque, et al. "Variant Set Enrichment: an R package to identify disease-associated functional genomic regions." BioData mining vol. 10 (2017): 9. doi: https://doi.org/10.1186/s13040-017-0129-5
Ahmed M, Sallari RC, Guo H, Moore JH, He HH, Lupien M. Variant Set Enrichment: an R package to identify disease-associated functional genomic regions. BioData Min. 2017 Feb 22;10:9. doi: 10.1186/s13040-017-0129-5. eCollection 2017. PMID: 28239419; PMCID: PMC5320724.
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The multiscale backbone of the human phenotype network based on biological pathways.

BioData mining

Darabos C, White MJ, Graham BE, Leung DN, Williams SM, Moore JH.
PMID: 24460644
BioData Min. 2014 Jan 25;7(1):1. doi: 10.1186/1756-0381-7-1.

BACKGROUND: Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over...

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Darabos C, White MJ, Graham BE, et al. The multiscale backbone of the human phenotype network based on biological pathways. BioData Min. 2014;7(1):1doi: 10.1186/1756-0381-7-1.
Darabos, C., White, M. J., Graham, B. E., Leung, D. N., Williams, S. M., & Moore, J. H. (2014). The multiscale backbone of the human phenotype network based on biological pathways. BioData mining, 7(1), 1. https://doi.org/10.1186/1756-0381-7-1
Darabos, Christian, et al. "The multiscale backbone of the human phenotype network based on biological pathways." BioData mining vol. 7,1 (2014): 1. doi: https://doi.org/10.1186/1756-0381-7-1
Darabos C, White MJ, Graham BE, Leung DN, Williams SM, Moore JH. The multiscale backbone of the human phenotype network based on biological pathways. BioData Min. 2014 Jan 25;7(1):1. doi: 10.1186/1756-0381-7-1. PMID: 24460644; PMCID: PMC3924922.
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Multifactor dimensionality reduction reveals a three-locus epistatic interaction associated with susceptibility to pulmonary tuberculosis.

BioData mining

Collins RL, Hu T, Wejse C, Sirugo G, Williams SM, Moore JH.
PMID: 23418869
BioData Min. 2013 Feb 18;6(1):4. doi: 10.1186/1756-0381-6-4.

BACKGROUND: Identifying high-order genetics associations with non-additive (i.e. epistatic) effects in population-based studies of common human diseases is a computational challenge. Multifactor dimensionality reduction (MDR) is a machine learning method that was designed specifically for this problem. The goal...

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Collins RL, Hu T, Wejse C, et al. Multifactor dimensionality reduction reveals a three-locus epistatic interaction associated with susceptibility to pulmonary tuberculosis. BioData Min. 2013;6(1):4doi: 10.1186/1756-0381-6-4.
Collins, R. L., Hu, T., Wejse, C., Sirugo, G., Williams, S. M., & Moore, J. H. (2013). Multifactor dimensionality reduction reveals a three-locus epistatic interaction associated with susceptibility to pulmonary tuberculosis. BioData mining, 6(1), 4. https://doi.org/10.1186/1756-0381-6-4
Collins, Ryan L, et al. "Multifactor dimensionality reduction reveals a three-locus epistatic interaction associated with susceptibility to pulmonary tuberculosis." BioData mining vol. 6,1 (2013): 4. doi: https://doi.org/10.1186/1756-0381-6-4
Collins RL, Hu T, Wejse C, Sirugo G, Williams SM, Moore JH. Multifactor dimensionality reduction reveals a three-locus epistatic interaction associated with susceptibility to pulmonary tuberculosis. BioData Min. 2013 Feb 18;6(1):4. doi: 10.1186/1756-0381-6-4. PMID: 23418869; PMCID: PMC3618340.
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Identification of epistatic interactions between the human RNA demethylases FTO and ALKBH5 with gene set enrichment analysis informed by differential methylation.

BMC proceedings

Piette ER, Moore JH.
PMID: 30275901
BMC Proc. 2018 Sep 17;12:59. doi: 10.1186/s12919-018-0122-0. eCollection 2018.

The Genetic Analysis Workshop (GAW) presents an opportunity to collaboratively evaluate methodology relevant to current issues in genetic epidemiology. The GAW20 data combine real clinical trial data with fictitious epigenetic drug response endpoints. Considering the evidence suggesting that networks...

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Piette ER, Moore JH. Identification of epistatic interactions between the human RNA demethylases FTO and ALKBH5 with gene set enrichment analysis informed by differential methylation. BMC Proc. 2018;12:59doi: 10.1186/s12919-018-0122-0.
Piette, E. R., & Moore, J. H. (2018). Identification of epistatic interactions between the human RNA demethylases FTO and ALKBH5 with gene set enrichment analysis informed by differential methylation. BMC proceedings, 1259. https://doi.org/10.1186/s12919-018-0122-0
Piette, Elizabeth R, and Moore, Jason H. "Identification of epistatic interactions between the human RNA demethylases FTO and ALKBH5 with gene set enrichment analysis informed by differential methylation." BMC proceedings vol. 12 (2018): 59. doi: https://doi.org/10.1186/s12919-018-0122-0
Piette ER, Moore JH. Identification of epistatic interactions between the human RNA demethylases FTO and ALKBH5 with gene set enrichment analysis informed by differential methylation. BMC Proc. 2018 Sep 17;12:59. doi: 10.1186/s12919-018-0122-0. eCollection 2018. PMID: 30275901; PMCID: PMC6157145.
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Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals.

BioData mining

Holzinger ER, Verma SS, Moore CB, Hall M, De R, Gilbert-Diamond D, Lanktree MB, Pankratz N, Amuzu A, Burt A, Dale C, Dudek S, Furlong CE, Gaunt TR, Kim DS, Riess H, Sivapalaratnam S, Tragante V, van Iperen EPA, Brautbar A, Carrell DS, Crosslin DR, Jarvik GP, Kuivaniemi H, Kullo IJ, Larson EB, Rasmussen-Torvik LJ, Tromp G, Baumert J, Cruickshanks KJ, Farrall M, Hingorani AD, Hovingh GK, Kleber ME, Klein BE, Klein R, Koenig W, Lange LA, Mӓrz W, North KE, Charlotte Onland-Moret N, Reiner AP, Talmud PJ, van der Schouw YT, Wilson JG, Kivimaki M, Kumari M, Moore JH, Drenos F, Asselbergs FW, Keating BJ, Ritchie MD.
PMID: 28770004
BioData Min. 2017 Jul 24;10:25. doi: 10.1186/s13040-017-0145-5. eCollection 2017.

BACKGROUND: The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol...

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Holzinger ER, Verma SS, Moore CB, et al. Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData Min. 2017;10:25doi: 10.1186/s13040-017-0145-5.
Holzinger, E. R., Verma, S. S., Moore, C. B., Hall, M., De, R., Gilbert-Diamond, D., Lanktree, M. B., Pankratz, N., Amuzu, A., Burt, A., Dale, C., Dudek, S., Furlong, C. E., Gaunt, T. R., Kim, D. S., Riess, H., Sivapalaratnam, S., Tragante, V., van Iperen, E. P. A., Brautbar, A., Carrell, D. S., Crosslin, D. R., Jarvik, G. P., Kuivaniemi, H., Kullo, I. J., Larson, E. B., Rasmussen-Torvik, L. J., Tromp, G., Baumert, J., Cruickshanks, K. J., Farrall, M., Hingorani, A. D., Hovingh, G. K., Kleber, M. E., Klein, B. E., Klein, R., Koenig, W., Lange, L. A., Mӓrz, W., North, K. E., Charlotte Onland-Moret, N., Reiner, A. P., Talmud, P. J., van der Schouw, Y. T., Wilson, J. G., Kivimaki, M., Kumari, M., Moore, J. H., Drenos, F., Asselbergs, F. W., Keating, B. J., & Ritchie, M. D. (2017). Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData mining, 1025. https://doi.org/10.1186/s13040-017-0145-5
Holzinger, Emily R, et al. "Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals." BioData mining vol. 10 (2017): 25. doi: https://doi.org/10.1186/s13040-017-0145-5
Holzinger ER, Verma SS, Moore CB, Hall M, De R, Gilbert-Diamond D, Lanktree MB, Pankratz N, Amuzu A, Burt A, Dale C, Dudek S, Furlong CE, Gaunt TR, Kim DS, Riess H, Sivapalaratnam S, Tragante V, van Iperen EPA, Brautbar A, Carrell DS, Crosslin DR, Jarvik GP, Kuivaniemi H, Kullo IJ, Larson EB, Rasmussen-Torvik LJ, Tromp G, Baumert J, Cruickshanks KJ, Farrall M, Hingorani AD, Hovingh GK, Kleber ME, Klein BE, Klein R, Koenig W, Lange LA, Mӓrz W, North KE, Charlotte Onland-Moret N, Reiner AP, Talmud PJ, van der Schouw YT, Wilson JG, Kivimaki M, Kumari M, Moore JH, Drenos F, Asselbergs FW, Keating BJ, Ritchie MD. Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData Min. 2017 Jul 24;10:25. doi: 10.1186/s13040-017-0145-5. eCollection 2017. PMID: 28770004; PMCID: PMC5525436.
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Gene ontology analysis of pairwise genetic associations in two genome-wide studies of sporadic ALS.

BioData mining

Kim NC, Andrews PC, Asselbergs FW, Frost HR, Williams SM, Harris BT, Read C, Askland KD, Moore JH.
PMID: 22839596
BioData Min. 2012 Jul 28;5(1):9. doi: 10.1186/1756-0381-5-9.

BACKGROUND: It is increasingly clear that common human diseases have a complex genetic architecture characterized by both additive and nonadditive genetic effects. The goal of the present study was to determine whether patterns of both additive and nonadditive genetic...

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Kim NC, Andrews PC, Asselbergs FW, et al. Gene ontology analysis of pairwise genetic associations in two genome-wide studies of sporadic ALS. BioData Min. 2012;5(1):9doi: 10.1186/1756-0381-5-9.
Kim, N. C., Andrews, P. C., Asselbergs, F. W., Frost, H. R., Williams, S. M., Harris, B. T., Read, C., Askland, K. D., & Moore, J. H. (2012). Gene ontology analysis of pairwise genetic associations in two genome-wide studies of sporadic ALS. BioData mining, 5(1), 9. https://doi.org/10.1186/1756-0381-5-9
Kim, Nora Chung, et al. "Gene ontology analysis of pairwise genetic associations in two genome-wide studies of sporadic ALS." BioData mining vol. 5,1 (2012): 9. doi: https://doi.org/10.1186/1756-0381-5-9
Kim NC, Andrews PC, Asselbergs FW, Frost HR, Williams SM, Harris BT, Read C, Askland KD, Moore JH. Gene ontology analysis of pairwise genetic associations in two genome-wide studies of sporadic ALS. BioData Min. 2012 Jul 28;5(1):9. doi: 10.1186/1756-0381-5-9. PMID: 22839596; PMCID: PMC3463436.
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Improving QSAR Modeling for Predictive Toxicology using Publicly Aggregated Semantic Graph Data and Graph Neural Networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

Romano JD, Hao Y, Moore JH.
PMID: 34890148
Pac Symp Biocomput. 2022;27:187-198.

Quantitative Structure-Activity Relationship (QSAR) modeling is a common computational technique for predicting chemical toxicity, but a lack of new methodological innovations has impeded QSAR performance on many tasks. We show that contemporary QSAR modeling for predictive toxicology can be...

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Romano JD, Hao Y, Moore JH. Improving QSAR Modeling for Predictive Toxicology using Publicly Aggregated Semantic Graph Data and Graph Neural Networks. Pac Symp Biocomput. 2022;27:187-198
Romano, J. D., Hao, Y., & Moore, J. H. (2022). Improving QSAR Modeling for Predictive Toxicology using Publicly Aggregated Semantic Graph Data and Graph Neural Networks. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 27187-198.
Romano, Joseph D, et al. "Improving QSAR Modeling for Predictive Toxicology using Publicly Aggregated Semantic Graph Data and Graph Neural Networks." Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing vol. 27 (2022): 187-198.
Romano JD, Hao Y, Moore JH. Improving QSAR Modeling for Predictive Toxicology using Publicly Aggregated Semantic Graph Data and Graph Neural Networks. Pac Symp Biocomput. 2022;27:187-198. PMID: 34890148; PMCID: PMC8714189.
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Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals.

BioData mining

Holzinger ER, Verma SS, Moore CB, Hall M, De R, Gilbert-Diamond D, Lanktree MB, Pankratz N, Amuzu A, Burt A, Dale C, Dudek S, Furlong CE, Gaunt TR, Kim DS, Riess H, Sivapalaratnam S, Tragante V, van Iperen EPA, Brautbar A, Carrell DS, Crosslin DR, Jarvik GP, Kuivaniemi H, Kullo IJ, Larson EB, Rasmussen-Torvik LJ, Tromp G, Baumert J, Cruickshanks KJ, Farrall M, Hingorani AD, Hovingh GK, Kleber ME, Klein BE, Klein R, Koenig W, Lange LA, Mӓrz W, North KE, Charlotte Onland-Moret N, Reiner AP, Talmud PJ, van der Schouw YT, Wilson JG, Kivimaki M, Kumari M, Moore JH, Drenos F, Asselbergs FW, Keating BJ, Ritchie MD.
PMID: 28770004
BioData Min. 2017 Jul 24;10:25. doi: 10.1186/s13040-017-0145-5. eCollection 2017.

BACKGROUND: The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol...

Cite
Holzinger ER, Verma SS, Moore CB, et al. Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData Min. 2017;10:25doi: 10.1186/s13040-017-0145-5.
Holzinger, E. R., Verma, S. S., Moore, C. B., Hall, M., De, R., Gilbert-Diamond, D., Lanktree, M. B., Pankratz, N., Amuzu, A., Burt, A., Dale, C., Dudek, S., Furlong, C. E., Gaunt, T. R., Kim, D. S., Riess, H., Sivapalaratnam, S., Tragante, V., van Iperen, E. P. A., Brautbar, A., Carrell, D. S., Crosslin, D. R., Jarvik, G. P., Kuivaniemi, H., Kullo, I. J., Larson, E. B., Rasmussen-Torvik, L. J., Tromp, G., Baumert, J., Cruickshanks, K. J., Farrall, M., Hingorani, A. D., Hovingh, G. K., Kleber, M. E., Klein, B. E., Klein, R., Koenig, W., Lange, L. A., Mӓrz, W., North, K. E., Charlotte Onland-Moret, N., Reiner, A. P., Talmud, P. J., van der Schouw, Y. T., Wilson, J. G., Kivimaki, M., Kumari, M., Moore, J. H., Drenos, F., Asselbergs, F. W., Keating, B. J., & Ritchie, M. D. (2017). Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData mining, 1025. https://doi.org/10.1186/s13040-017-0145-5
Holzinger, Emily R, et al. "Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals." BioData mining vol. 10 (2017): 25. doi: https://doi.org/10.1186/s13040-017-0145-5
Holzinger ER, Verma SS, Moore CB, Hall M, De R, Gilbert-Diamond D, Lanktree MB, Pankratz N, Amuzu A, Burt A, Dale C, Dudek S, Furlong CE, Gaunt TR, Kim DS, Riess H, Sivapalaratnam S, Tragante V, van Iperen EPA, Brautbar A, Carrell DS, Crosslin DR, Jarvik GP, Kuivaniemi H, Kullo IJ, Larson EB, Rasmussen-Torvik LJ, Tromp G, Baumert J, Cruickshanks KJ, Farrall M, Hingorani AD, Hovingh GK, Kleber ME, Klein BE, Klein R, Koenig W, Lange LA, Mӓrz W, North KE, Charlotte Onland-Moret N, Reiner AP, Talmud PJ, van der Schouw YT, Wilson JG, Kivimaki M, Kumari M, Moore JH, Drenos F, Asselbergs FW, Keating BJ, Ritchie MD. Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData Min. 2017 Jul 24;10:25. doi: 10.1186/s13040-017-0145-5. eCollection 2017. PMID: 28770004; PMCID: PMC5525436.
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Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals.

BioData mining

Holzinger ER, Verma SS, Moore CB, Hall M, De R, Gilbert-Diamond D, Lanktree MB, Pankratz N, Amuzu A, Burt A, Dale C, Dudek S, Furlong CE, Gaunt TR, Kim DS, Riess H, Sivapalaratnam S, Tragante V, van Iperen EPA, Brautbar A, Carrell DS, Crosslin DR, Jarvik GP, Kuivaniemi H, Kullo IJ, Larson EB, Rasmussen-Torvik LJ, Tromp G, Baumert J, Cruickshanks KJ, Farrall M, Hingorani AD, Hovingh GK, Kleber ME, Klein BE, Klein R, Koenig W, Lange LA, Mӓrz W, North KE, Charlotte Onland-Moret N, Reiner AP, Talmud PJ, van der Schouw YT, Wilson JG, Kivimaki M, Kumari M, Moore JH, Drenos F, Asselbergs FW, Keating BJ, Ritchie MD.
PMID: 28770004
BioData Min. 2017 Jul 24;10:25. doi: 10.1186/s13040-017-0145-5. eCollection 2017.

BACKGROUND: The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol...

Cite
Holzinger ER, Verma SS, Moore CB, et al. Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData Min. 2017;10:25doi: 10.1186/s13040-017-0145-5.
Holzinger, E. R., Verma, S. S., Moore, C. B., Hall, M., De, R., Gilbert-Diamond, D., Lanktree, M. B., Pankratz, N., Amuzu, A., Burt, A., Dale, C., Dudek, S., Furlong, C. E., Gaunt, T. R., Kim, D. S., Riess, H., Sivapalaratnam, S., Tragante, V., van Iperen, E. P. A., Brautbar, A., Carrell, D. S., Crosslin, D. R., Jarvik, G. P., Kuivaniemi, H., Kullo, I. J., Larson, E. B., Rasmussen-Torvik, L. J., Tromp, G., Baumert, J., Cruickshanks, K. J., Farrall, M., Hingorani, A. D., Hovingh, G. K., Kleber, M. E., Klein, B. E., Klein, R., Koenig, W., Lange, L. A., Mӓrz, W., North, K. E., Charlotte Onland-Moret, N., Reiner, A. P., Talmud, P. J., van der Schouw, Y. T., Wilson, J. G., Kivimaki, M., Kumari, M., Moore, J. H., Drenos, F., Asselbergs, F. W., Keating, B. J., & Ritchie, M. D. (2017). Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData mining, 1025. https://doi.org/10.1186/s13040-017-0145-5
Holzinger, Emily R, et al. "Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals." BioData mining vol. 10 (2017): 25. doi: https://doi.org/10.1186/s13040-017-0145-5
Holzinger ER, Verma SS, Moore CB, Hall M, De R, Gilbert-Diamond D, Lanktree MB, Pankratz N, Amuzu A, Burt A, Dale C, Dudek S, Furlong CE, Gaunt TR, Kim DS, Riess H, Sivapalaratnam S, Tragante V, van Iperen EPA, Brautbar A, Carrell DS, Crosslin DR, Jarvik GP, Kuivaniemi H, Kullo IJ, Larson EB, Rasmussen-Torvik LJ, Tromp G, Baumert J, Cruickshanks KJ, Farrall M, Hingorani AD, Hovingh GK, Kleber ME, Klein BE, Klein R, Koenig W, Lange LA, Mӓrz W, North KE, Charlotte Onland-Moret N, Reiner AP, Talmud PJ, van der Schouw YT, Wilson JG, Kivimaki M, Kumari M, Moore JH, Drenos F, Asselbergs FW, Keating BJ, Ritchie MD. Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData Min. 2017 Jul 24;10:25. doi: 10.1186/s13040-017-0145-5. eCollection 2017. PMID: 28770004; PMCID: PMC5525436.
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Evolutionary triangulation: informing genetic association studies with evolutionary evidence.

BioData mining

Huang M, Graham BE, Zhang G, Harder R, Kodaman N, Moore JH, Muglia L, Williams SM.
PMID: 27042214
BioData Min. 2016 Apr 02;9:12. doi: 10.1186/s13040-016-0091-7. eCollection 2016.

Genetic studies of human diseases have identified many variants associated with pathogenesis and severity. However, most studies have used only statistical association to assess putative relationships to disease, and ignored other factors for evaluation. For example, evolution is a...

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Huang M, Graham BE, Zhang G, et al. Evolutionary triangulation: informing genetic association studies with evolutionary evidence. BioData Min. 2016;9:12doi: 10.1186/s13040-016-0091-7.
Huang, M., Graham, B. E., Zhang, G., Harder, R., Kodaman, N., Moore, J. H., Muglia, L., & Williams, S. M. (2016). Evolutionary triangulation: informing genetic association studies with evolutionary evidence. BioData mining, 912. https://doi.org/10.1186/s13040-016-0091-7
Huang, Minjun, et al. "Evolutionary triangulation: informing genetic association studies with evolutionary evidence." BioData mining vol. 9 (2016): 12. doi: https://doi.org/10.1186/s13040-016-0091-7
Huang M, Graham BE, Zhang G, Harder R, Kodaman N, Moore JH, Muglia L, Williams SM. Evolutionary triangulation: informing genetic association studies with evolutionary evidence. BioData Min. 2016 Apr 02;9:12. doi: 10.1186/s13040-016-0091-7. eCollection 2016. PMID: 27042214; PMCID: PMC4818851.
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