Multiclass Prediction with Partial Least Square Regression for Gene Expression Data: Applications in Breast Cancer Intrinsic Taxonomy
المؤلفون المشاركون
Tu, Shih-Hsin
Huang, Ching-Shui
Lien, Heng-Hui
Lai, Liang-Chuan
Chuang, Eric Y.
Huang, Chi-Cheng
المصدر
العدد
المجلد 2013، العدد 2013 (31 ديسمبر/كانون الأول 2013)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2013-12-30
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Multiclass prediction remains an obstacle for high-throughput data analysis such as microarray gene expression profiles.
Despite recent advancements in machine learning and bioinformatics, most classification tools were limited to the applications of binary responses.
Our aim was to apply partial least square (PLS) regression for breast cancer intrinsic taxonomy, of which five distinct molecular subtypes were identified.
The PAM50 signature genes were used as predictive variables in PLS analysis, and the latent gene component scores were used in binary logistic regression for each molecular subtype.
The 139 prototypical arrays for PAM50 development were used as training dataset, and three independent microarray studies with Han Chinese origin were used for independent validation (n=535).
The agreement between PAM50 centroid-based single sample prediction (SSP) and PLS-regression was excellent (weighted Kappa: 0.988) within the training samples, but deteriorated substantially in independent samples, which could attribute to much more unclassified samples by PLS-regression.
If these unclassified samples were removed, the agreement between PAM50 SSP and PLS-regression improved enormously (weighted Kappa: 0.829 as opposed to 0.541 when unclassified samples were analyzed).
Our study ascertained the feasibility of PLS-regression in multi-class prediction, and distinct clinical presentations and prognostic discrepancies were observed across breast cancer molecular subtypes.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Huang, Chi-Cheng& Tu, Shih-Hsin& Huang, Ching-Shui& Lien, Heng-Hui& Lai, Liang-Chuan& Chuang, Eric Y.. 2013. Multiclass Prediction with Partial Least Square Regression for Gene Expression Data: Applications in Breast Cancer Intrinsic Taxonomy. BioMed Research International،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1030227
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Huang, Chi-Cheng…[et al.]. Multiclass Prediction with Partial Least Square Regression for Gene Expression Data: Applications in Breast Cancer Intrinsic Taxonomy. BioMed Research International No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-1030227
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Huang, Chi-Cheng& Tu, Shih-Hsin& Huang, Ching-Shui& Lien, Heng-Hui& Lai, Liang-Chuan& Chuang, Eric Y.. Multiclass Prediction with Partial Least Square Regression for Gene Expression Data: Applications in Breast Cancer Intrinsic Taxonomy. BioMed Research International. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1030227
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1030227
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر