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Pan-Cancer Analysis of Mutation Hotspots in Protein Domains.

Cell systems

Miller ML, Reznik E, Gauthier NP, Aksoy BA, Korkut A, Gao J, Ciriello G, Schultz N, Sander C.
PMID: 27135912
Cell Syst. 2015 Sep 23;1(3):197-209. doi: 10.1016/j.cels.2015.08.014. Epub 2015 Sep 23.

In cancer genomics, recurrence of mutations in independent tumor samples is a strong indicator of functional impact. However, rare functional mutations can escape detection by recurrence analysis owing to lack of statistical power. We enhance statistical power by extending...

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Miller ML, Reznik E, Gauthier NP, et al. Pan-Cancer Analysis of Mutation Hotspots in Protein Domains. Cell Syst. 2015;1(3):197-209doi: 10.1016/j.cels.2015.08.014.
Miller, M. L., Reznik, E., Gauthier, N. P., Aksoy, B. A., Korkut, A., Gao, J., Ciriello, G., Schultz, N., & Sander, C. (2015). Pan-Cancer Analysis of Mutation Hotspots in Protein Domains. Cell systems, 1(3), 197-209. https://doi.org/10.1016/j.cels.2015.08.014
Miller, Martin L, et al. "Pan-Cancer Analysis of Mutation Hotspots in Protein Domains." Cell systems vol. 1,3 (2015): 197-209. doi: https://doi.org/10.1016/j.cels.2015.08.014
Miller ML, Reznik E, Gauthier NP, Aksoy BA, Korkut A, Gao J, Ciriello G, Schultz N, Sander C. Pan-Cancer Analysis of Mutation Hotspots in Protein Domains. Cell Syst. 2015 Sep 23;1(3):197-209. doi: 10.1016/j.cels.2015.08.014. Epub 2015 Sep 23. PMID: 27135912; PMCID: PMC4982675.
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Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH-Mutant Molecular Profiles.

Cell reports

Farshidfar F, Zheng S, Gingras MC, Newton Y, Shih J, Robertson AG, Hinoue T, Hoadley KA, Gibb EA, Roszik J, Covington KR, Wu CC, Shinbrot E, Stransky N, Hegde A, Yang JD, Reznik E, Sadeghi S, Pedamallu CS, Ojesina AI, Hess JM, Auman JT, Rhie SK, Bowlby R, Borad MJ, Zhu AX, Stuart JM, Sander C, Akbani R, Cherniack AD, Deshpande V, Mounajjed T, Foo WC, Torbenson MS, Kleiner DE, Laird PW, Wheeler DA, McRee AJ, Bathe OF, Andersen JB, Bardeesy N, Roberts LR, Kwong LN.
PMID: 28658632
Cell Rep. 2017 Jun 27;19(13):2878-2880. doi: 10.1016/j.celrep.2017.06.008.

No abstract available.

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Farshidfar F, Zheng S, Gingras MC, et al. Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH-Mutant Molecular Profiles. Cell Rep. 2017;19(13):2878-2880doi: 10.1016/j.celrep.2017.06.008.
Farshidfar, F., Zheng, S., Gingras, M. C., Newton, Y., Shih, J., Robertson, A. G., Hinoue, T., Hoadley, K. A., Gibb, E. A., Roszik, J., Covington, K. R., Wu, C. C., Shinbrot, E., Stransky, N., Hegde, A., Yang, J. D., Reznik, E., Sadeghi, S., Pedamallu, C. S., Ojesina, A. I., Hess, J. M., Auman, J. T., Rhie, S. K., Bowlby, R., Borad, M. J., Zhu, A. X., Stuart, J. M., Sander, C., Akbani, R., Cherniack, A. D., Deshpande, V., Mounajjed, T., Foo, W. C., Torbenson, M. S., Kleiner, D. E., Laird, P. W., Wheeler, D. A., McRee, A. J., Bathe, O. F., Andersen, J. B., Bardeesy, N., Roberts, L. R., & Kwong, L. N. (2017). Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH-Mutant Molecular Profiles. Cell reports, 19(13), 2878-2880. https://doi.org/10.1016/j.celrep.2017.06.008
Farshidfar, Farshad, et al. "Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH-Mutant Molecular Profiles." Cell reports vol. 19,13 (2017): 2878-2880. doi: https://doi.org/10.1016/j.celrep.2017.06.008
Farshidfar F, Zheng S, Gingras MC, Newton Y, Shih J, Robertson AG, Hinoue T, Hoadley KA, Gibb EA, Roszik J, Covington KR, Wu CC, Shinbrot E, Stransky N, Hegde A, Yang JD, Reznik E, Sadeghi S, Pedamallu CS, Ojesina AI, Hess JM, Auman JT, Rhie SK, Bowlby R, Borad MJ, Zhu AX, Stuart JM, Sander C, Akbani R, Cherniack AD, Deshpande V, Mounajjed T, Foo WC, Torbenson MS, Kleiner DE, Laird PW, Wheeler DA, McRee AJ, Bathe OF, Andersen JB, Bardeesy N, Roberts LR, Kwong LN. Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH-Mutant Molecular Profiles. Cell Rep. 2017 Jun 27;19(13):2878-2880. doi: 10.1016/j.celrep.2017.06.008. PMID: 28658632; PMCID: PMC6141445.
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Affinity regression predicts the recognition code of nucleic acid-binding proteins.

Nature biotechnology

Pelossof R, Singh I, Yang JL, Weirauch MT, Hughes TR, Leslie CS.
PMID: 26571099
Nat Biotechnol. 2015 Dec;33(12):1242-1249. doi: 10.1038/nbt.3343. Epub 2015 Nov 16.

Predicting the affinity profiles of nucleic acid-binding proteins directly from the protein sequence is a challenging problem. We present a statistical approach for learning the recognition code of a family of transcription factors or RNA-binding proteins (RBPs) from high-throughput...

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Pelossof R, Singh I, Yang JL, et al. Affinity regression predicts the recognition code of nucleic acid-binding proteins. Nat Biotechnol. 2015;33(12):1242-1249doi: 10.1038/nbt.3343.
Pelossof, R., Singh, I., Yang, J. L., Weirauch, M. T., Hughes, T. R., & Leslie, C. S. (2015). Affinity regression predicts the recognition code of nucleic acid-binding proteins. Nature biotechnology, 33(12), 1242-1249. https://doi.org/10.1038/nbt.3343
Pelossof, Raphael, et al. "Affinity regression predicts the recognition code of nucleic acid-binding proteins." Nature biotechnology vol. 33,12 (2015): 1242-1249. doi: https://doi.org/10.1038/nbt.3343
Pelossof R, Singh I, Yang JL, Weirauch MT, Hughes TR, Leslie CS. Affinity regression predicts the recognition code of nucleic acid-binding proteins. Nat Biotechnol. 2015 Dec;33(12):1242-1249. doi: 10.1038/nbt.3343. Epub 2015 Nov 16. PMID: 26571099; PMCID: PMC4871164.
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Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin.

Cell

Hoadley KA, Yau C, Wolf DM, Cherniack AD, Tamborero D, Ng S, Leiserson MDM, Niu B, McLellan MD, Uzunangelov V, Zhang J, Kandoth C, Akbani R, Shen H, Omberg L, Chu A, Margolin AA, Van't Veer LJ, Lopez-Bigas N, Laird PW, Raphael BJ, Ding L, Robertson AG, Byers LA, Mills GB, Weinstein JN, Van Waes C, Chen Z, Collisson EA, Benz CC, Perou CM, Stuart JM.
PMID: 25109877
Cell. 2014 Aug 14;158(4):929-944. doi: 10.1016/j.cell.2014.06.049. Epub 2014 Aug 07.

Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform...

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Hoadley KA, Yau C, Wolf DM, et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell. 2014;158(4):929-944doi: 10.1016/j.cell.2014.06.049.
Hoadley, K. A., Yau, C., Wolf, D. M., Cherniack, A. D., Tamborero, D., Ng, S., Leiserson, M. D. M., Niu, B., McLellan, M. D., Uzunangelov, V., Zhang, J., Kandoth, C., Akbani, R., Shen, H., Omberg, L., Chu, A., Margolin, A. A., Van't Veer, L. J., Lopez-Bigas, N., Laird, P. W., Raphael, B. J., Ding, L., Robertson, A. G., Byers, L. A., Mills, G. B., Weinstein, J. N., Van Waes, C., Chen, Z., Collisson, E. A., Benz, C. C., Perou, C. M., & Stuart, J. M. (2014). Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell, 158(4), 929-944. https://doi.org/10.1016/j.cell.2014.06.049
Hoadley, Katherine A, et al. "Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin." Cell vol. 158,4 (2014): 929-944. doi: https://doi.org/10.1016/j.cell.2014.06.049
Hoadley KA, Yau C, Wolf DM, Cherniack AD, Tamborero D, Ng S, Leiserson MDM, Niu B, McLellan MD, Uzunangelov V, Zhang J, Kandoth C, Akbani R, Shen H, Omberg L, Chu A, Margolin AA, Van't Veer LJ, Lopez-Bigas N, Laird PW, Raphael BJ, Ding L, Robertson AG, Byers LA, Mills GB, Weinstein JN, Van Waes C, Chen Z, Collisson EA, Benz CC, Perou CM, Stuart JM. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell. 2014 Aug 14;158(4):929-944. doi: 10.1016/j.cell.2014.06.049. Epub 2014 Aug 07. PMID: 25109877; PMCID: PMC4152462.
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SPARSE INTEGRATIVE CLUSTERING OF MULTIPLE OMICS DATA SETS.

The annals of applied statistics

Shen R, Wang S, Mo Q.
PMID: 24587839
Ann Appl Stat. 2013 Apr 09;7(1):269-294. doi: 10.1214/12-AOAS578.

High resolution microarrays and second-generation sequencing platforms are powerful tools to investigate genome-wide alterations in DNA copy number, methylation, and gene expression associated with a disease. An integrated genomic profiling approach measuring multiple omics data types simultaneously in the...

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Shen R, Wang S, Mo Q. SPARSE INTEGRATIVE CLUSTERING OF MULTIPLE OMICS DATA SETS. Ann Appl Stat. 2013;7(1):269-294doi: 10.1214/12-AOAS578.
Shen, R., Wang, S., & Mo, Q. (2013). SPARSE INTEGRATIVE CLUSTERING OF MULTIPLE OMICS DATA SETS. The annals of applied statistics, 7(1), 269-294. https://doi.org/10.1214/12-AOAS578
Shen, Ronglai, et al. "SPARSE INTEGRATIVE CLUSTERING OF MULTIPLE OMICS DATA SETS." The annals of applied statistics vol. 7,1 (2013): 269-294. doi: https://doi.org/10.1214/12-AOAS578
Shen R, Wang S, Mo Q. SPARSE INTEGRATIVE CLUSTERING OF MULTIPLE OMICS DATA SETS. Ann Appl Stat. 2013 Apr 09;7(1):269-294. doi: 10.1214/12-AOAS578. PMID: 24587839; PMCID: PMC3935438.
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Showing 1 to 5 of 5 entries