Performance comparison of machine learning methods for prognosis of hormone receptor status in breast cancer tissue samples

KALINLI, Adem; Sarikoc, Fatih; AKGÜN, Hülya; ÖZTÜRK, Figen

doi:10.1016/j.cmpb.2012.12.005

Performance comparison of machine learning methods for prognosis of hormone receptor status in breast cancer tissue samples

Atıf İçin Kopyala

KALINLI A., Sarikoc F., AKGÜN H., ÖZTÜRK F.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, cilt.110, sa.3, ss.298-307, 2013 (SCI-Expanded)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 110 Sayı: 3
Basım Tarihi: 2013
Doi Numarası: 10.1016/j.cmpb.2012.12.005
Dergi Adı: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
Sayfa Sayıları: ss.298-307
Hacettepe Üniversitesi Adresli: Hayır

Özet

We examined the classification and prognostic scoring performances of several computer methods on different feature sets to obtain objective and reproducible analysis of estrogen receptor status in breast cancer tissue samples.