Abstract P2-10-29: Time dependent breast cancer metastasis prediction using novel biological imaging, clinico-pathological and genomic data combined with Bayesian modeling to reduce over-fitting and improve on inter-cohort reproducibility.

I Sheeba; M Kelleher; K Lawler; F Festy; P Barber; E Shamill; P Gargi; G Weitsman; James Barrett; G Fruhwirth; L Huang; I Tullis; N Woodman; S Pinder; E Ofo; L Fernandes; M Beutler; S Ameer-Beg; L Holmberg; A Purushotham; F Fraternali; J Condeelis; A Hanby; C Gillett; P Ellis; B Vojnovic; A Coolen; T Ng

doi:10.1158/0008-5472.sabcs12-p2-10-29

Abstract P2-10-29: Time dependent breast cancer metastasis prediction using novel biological imaging, clinico-pathological and genomic data combined with Bayesian modeling to reduce over-fitting and improve on inter-cohort reproducibility.

I Sheeba, M Kelleher, K Lawler, F Festy, P Barber, E Shamill, P Gargi, G Weitsman, James Barrett, G Fruhwirth, L Huang, I Tullis, N Woodman, S Pinder, E Ofo, L Fernandes, M Beutler, S Ameer-Beg, L Holmberg, A PurushothamF Fraternali, J Condeelis, A Hanby, C Gillett, P Ellis, B Vojnovic, A Coolen, T Ng

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	P2-10-29-P2-10-29
Journal	Cancer Research
Volume	72
Issue number	24 Supplement
DOIs	https://doi.org/10.1158/0008-5472.sabcs12-p2-10-29
Publication status	Published - 1 Dec 2012

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10.1158/0008-5472.sabcs12-p2-10-29

https://doi.org/10.1158%2F0008-5472.sabcs12-p2-10-29

Cite this

Sheeba, I., Kelleher, M., Lawler, K., Festy, F., Barber, P., Shamill, E., Gargi, P., Weitsman, G., Barrett, J., Fruhwirth, G., Huang, L., Tullis, I., Woodman, N., Pinder, S., Ofo, E., Fernandes, L., Beutler, M., Ameer-Beg, S., Holmberg, L., ... Ng, T. (2012). Abstract P2-10-29: Time dependent breast cancer metastasis prediction using novel biological imaging, clinico-pathological and genomic data combined with Bayesian modeling to reduce over-fitting and improve on inter-cohort reproducibility. Cancer Research, 72(24 Supplement), P2-10-29-P2-10-29. https://doi.org/10.1158/0008-5472.sabcs12-p2-10-29

@article{e7773dc5613f49298b61313c811bbcdd,

title = "Abstract P2-10-29: Time dependent breast cancer metastasis prediction using novel biological imaging, clinico-pathological and genomic data combined with Bayesian modeling to reduce over-fitting and improve on inter-cohort reproducibility.",

author = "I Sheeba and M Kelleher and K Lawler and F Festy and P Barber and E Shamill and P Gargi and G Weitsman and James Barrett and G Fruhwirth and L Huang and I Tullis and N Woodman and S Pinder and E Ofo and L Fernandes and M Beutler and S Ameer-Beg and L Holmberg and A Purushotham and F Fraternali and J Condeelis and A Hanby and C Gillett and P Ellis and B Vojnovic and A Coolen and T Ng",

year = "2012",

month = dec,

day = "1",

doi = "10.1158/0008-5472.sabcs12-p2-10-29",

language = "English",

volume = "72",

pages = "P2--10--29--P2--10--29",

journal = "Cancer Research",

issn = "0008-5472",

publisher = "American Association for Cancer Research",

number = "24 Supplement",

}

Sheeba, I, Kelleher, M , Lawler, K, Festy, F, Barber, P, Shamill, E, Gargi, P, Weitsman, G , Barrett, J, Fruhwirth, G, Huang, L, Tullis, I, Woodman, N, Pinder, S , Ofo, E , Fernandes, L , Beutler, M , Ameer-Beg, S , Holmberg, L , Purushotham, A , Fraternali, F, Condeelis, J, Hanby, A, Gillett, C , Ellis, P , Vojnovic, B , Coolen, A & Ng, T 2012, 'Abstract P2-10-29: Time dependent breast cancer metastasis prediction using novel biological imaging, clinico-pathological and genomic data combined with Bayesian modeling to reduce over-fitting and improve on inter-cohort reproducibility.', Cancer Research, vol. 72, no. 24 Supplement, pp. P2-10-29-P2-10-29. https://doi.org/10.1158/0008-5472.sabcs12-p2-10-29

Abstract P2-10-29: Time dependent breast cancer metastasis prediction using novel biological imaging, clinico-pathological and genomic data combined with Bayesian modeling to reduce over-fitting and improve on inter-cohort reproducibility. / Sheeba, I; Kelleher, M ; Lawler, K et al.
In: Cancer Research, Vol. 72, No. 24 Supplement, 01.12.2012, p. P2-10-29-P2-10-29.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Abstract P2-10-29: Time dependent breast cancer metastasis prediction using novel biological imaging, clinico-pathological and genomic data combined with Bayesian modeling to reduce over-fitting and improve on inter-cohort reproducibility.

AU - Sheeba, I

AU - Kelleher, M

AU - Lawler, K

AU - Festy, F

AU - Barber, P

AU - Shamill, E

AU - Gargi, P

AU - Weitsman, G

AU - Barrett, James

AU - Fruhwirth, G

AU - Huang, L

AU - Tullis, I

AU - Woodman, N

AU - Pinder, S

AU - Ofo, E

AU - Fernandes, L

AU - Beutler, M

AU - Ameer-Beg, S

AU - Holmberg, L

AU - Purushotham, A

AU - Fraternali, F

AU - Condeelis, J

AU - Hanby, A

AU - Gillett, C

AU - Ellis, P

AU - Vojnovic, B

AU - Coolen, A

AU - Ng, T

PY - 2012/12/1

Y1 - 2012/12/1

U2 - 10.1158/0008-5472.sabcs12-p2-10-29

DO - 10.1158/0008-5472.sabcs12-p2-10-29

M3 - Article

SN - 0008-5472

VL - 72

SP - P2-10-29-P2-10-29

JO - Cancer Research

JF - Cancer Research

IS - 24 Supplement

ER -

Sheeba I, Kelleher M , Lawler K, Festy F, Barber P, Shamill E et al. Abstract P2-10-29: Time dependent breast cancer metastasis prediction using novel biological imaging, clinico-pathological and genomic data combined with Bayesian modeling to reduce over-fitting and improve on inter-cohort reproducibility. Cancer Research. 2012 Dec 1;72(24 Supplement):P2-10-29-P2-10-29. doi: 10.1158/0008-5472.sabcs12-p2-10-29