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Germline whole exome sequencing and large-scale replication identifies .

Oncotarget

Dicks E, Song H, Ramus SJ, Oudenhove EV, Tyrer JP, Intermaggio MP, Kar S, Harrington P, Bowtell DD, Group AS, Cicek MS, Cunningham JM, Fridley BL, Alsop J, Jimenez-Linan M, Piskorz A, Goranova T, Kent E, Siddiqui N, Paul J, Crawford R, Poblete S, Lele S, Sucheston-Campbell L, Moysich KB, Sieh W, McGuire V, Lester J, Odunsi K, Whittemore AS, Bogdanova N, Dürst M, Hillemanns P, Karlan BY, Gentry-Maharaj A, Menon U, Tischkowitz M, Levine D, Brenton JD, Dörk T, Goode EL, Gayther SA, Pharoah DPP.
PMID: 28881617
Oncotarget. 2017 Mar 03;8(31):50930-50940. doi: 10.18632/oncotarget.15871. eCollection 2017 Aug 01.

We analyzed whole exome sequencing data in germline DNA from 412 high grade serous ovarian cancer (HGSOC) cases from The Cancer Genome Atlas Project and identified 5,517 genes harboring a predicted deleterious germline coding mutation in at least one...

Cite
Dicks E, Song H, Ramus SJ, et al. Germline whole exome sequencing and large-scale replication identifies . Oncotarget. 2017;8(31):50930-50940doi: 10.18632/oncotarget.15871.
Dicks, E., Song, H., Ramus, S. J., Oudenhove, E. V., Tyrer, J. P., Intermaggio, M. P., Kar, S., Harrington, P., Bowtell, D. D., Group, A. S., Cicek, M. S., Cunningham, J. M., Fridley, B. L., Alsop, J., Jimenez-Linan, M., Piskorz, A., Goranova, T., Kent, E., Siddiqui, N., Paul, J., Crawford, R., Poblete, S., Lele, S., Sucheston-Campbell, L., Moysich, K. B., Sieh, W., McGuire, V., Lester, J., Odunsi, K., Whittemore, A. S., Bogdanova, N., Dürst, M., Hillemanns, P., Karlan, B. Y., Gentry-Maharaj, A., Menon, U., Tischkowitz, M., Levine, D., Brenton, J. D., Dörk, T., Goode, E. L., Gayther, S. A., & Pharoah, D. P. P. (2017). Germline whole exome sequencing and large-scale replication identifies . Oncotarget, 8(31), 50930-50940. https://doi.org/10.18632/oncotarget.15871
Dicks, Ed, et al. "Germline whole exome sequencing and large-scale replication identifies ." Oncotarget vol. 8,31 (2017): 50930-50940. doi: https://doi.org/10.18632/oncotarget.15871
Dicks E, Song H, Ramus SJ, Oudenhove EV, Tyrer JP, Intermaggio MP, Kar S, Harrington P, Bowtell DD, Group AS, Cicek MS, Cunningham JM, Fridley BL, Alsop J, Jimenez-Linan M, Piskorz A, Goranova T, Kent E, Siddiqui N, Paul J, Crawford R, Poblete S, Lele S, Sucheston-Campbell L, Moysich KB, Sieh W, McGuire V, Lester J, Odunsi K, Whittemore AS, Bogdanova N, Dürst M, Hillemanns P, Karlan BY, Gentry-Maharaj A, Menon U, Tischkowitz M, Levine D, Brenton JD, Dörk T, Goode EL, Gayther SA, Pharoah DPP. Germline whole exome sequencing and large-scale replication identifies . Oncotarget. 2017 Mar 03;8(31):50930-50940. doi: 10.18632/oncotarget.15871. eCollection 2017 Aug 01. PMID: 28881617; PMCID: PMC5584218.
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Parenclitic networks for predicting ovarian cancer.

Oncotarget

Whitwell HJ, Blyuss O, Menon U, Timms JF, Zaikin A.
PMID: 29854310
Oncotarget. 2018 Apr 27;9(32):22717-22726. doi: 10.18632/oncotarget.25216. eCollection 2018 Apr 27.

Prediction and diagnosis of complex disease may not always be possible with a small number of biomarkers. Modern 'omics' technologies make it possible to cheaply and quantitatively assay hundreds of molecules generating large amounts of data from individual samples....

Cite
Whitwell HJ, Blyuss O, Menon U, et al. Parenclitic networks for predicting ovarian cancer. Oncotarget. 2018;9(32):22717-22726doi: 10.18632/oncotarget.25216.
Whitwell, H. J., Blyuss, O., Menon, U., Timms, J. F., & Zaikin, A. (2018). Parenclitic networks for predicting ovarian cancer. Oncotarget, 9(32), 22717-22726. https://doi.org/10.18632/oncotarget.25216
Whitwell, Harry J, et al. "Parenclitic networks for predicting ovarian cancer." Oncotarget vol. 9,32 (2018): 22717-22726. doi: https://doi.org/10.18632/oncotarget.25216
Whitwell HJ, Blyuss O, Menon U, Timms JF, Zaikin A. Parenclitic networks for predicting ovarian cancer. Oncotarget. 2018 Apr 27;9(32):22717-22726. doi: 10.18632/oncotarget.25216. eCollection 2018 Apr 27. PMID: 29854310; PMCID: PMC5978260.
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A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer.

Biomedical signal processing and control

Vázquez MA, Mariño IP, Blyuss O, Ryan A, Gentry-Maharaj A, Kalsi J, Manchanda R, Jacobs I, Menon U, Zaikin A.
PMID: 30245736
Biomed Signal Process Control. 2018 Sep;46:86-93. doi: 10.1016/j.bspc.2018.07.001.

We present a quantitative study of the performance of two automatic methods for the early detection of ovarian cancer that can exploit longitudinal measurements of multiple biomarkers. The study is carried out for a subset of the data collected...

Cite
Vázquez MA, Mariño IP, Blyuss O, et al. A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer. Biomed Signal Process Control. 2018;46:86-93doi: 10.1016/j.bspc.2018.07.001.
Vázquez, M. A., Mariño, I. P., Blyuss, O., Ryan, A., Gentry-Maharaj, A., Kalsi, J., Manchanda, R., Jacobs, I., Menon, U., & Zaikin, A. (2018). A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer. Biomedical signal processing and control, 4686-93. https://doi.org/10.1016/j.bspc.2018.07.001
Vázquez, Manuel A, et al. "A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer." Biomedical signal processing and control vol. 46 (2018): 86-93. doi: https://doi.org/10.1016/j.bspc.2018.07.001
Vázquez MA, Mariño IP, Blyuss O, Ryan A, Gentry-Maharaj A, Kalsi J, Manchanda R, Jacobs I, Menon U, Zaikin A. A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer. Biomed Signal Process Control. 2018 Sep;46:86-93. doi: 10.1016/j.bspc.2018.07.001. PMID: 30245736; PMCID: PMC6146655.
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