Biomarker Selection and Classification of “-Omics” Data Using a Two-Step Bayes Classification Framework
المؤلفون المشاركون
Assawamakin, Anunchai
Prueksaaroon, Supakit
Kulawonganunchai, Supasak
Shaw, Philip James
Varavithya, Vara
Ruangrajitpakorn, Taneth
Tongsima, Sissades
المصدر
العدد
المجلد 2013، العدد 2013 (31 ديسمبر/كانون الأول 2013)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2013-09-11
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine.
However, current machine learning approaches are either too complex or perform poorly.
Here, a novel two-step machine-learning framework is presented to address this need.
First, a Naïve Bayes estimator is used to rank features from which the top-ranked will most likely contain the most informative features for prediction of the underlying biological classes.
The top-ranked features are then used in a Hidden Naïve Bayes classifier to construct a classification prediction model from these filtered attributes.
In order to obtain the minimum set of the most informative biomarkers, the bottom-ranked features are successively removed from the Naïve Bayes-filtered feature list one at a time, and the classification accuracy of the Hidden Naïve Bayes classifier is checked for each pruned feature set.
The performance of the proposed two-step Bayes classification framework was tested on different types of -omics datasets including gene expression microarray, single nucleotide polymorphism microarray (SNParray), and surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) proteomic data.
The proposed two-step Bayes classification framework was equal to and, in some cases, outperformed other classification methods in terms of prediction accuracy, minimum number of classification markers, and computational time.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Assawamakin, Anunchai& Prueksaaroon, Supakit& Kulawonganunchai, Supasak& Shaw, Philip James& Varavithya, Vara& Ruangrajitpakorn, Taneth…[et al.]. 2013. Biomarker Selection and Classification of “-Omics” Data Using a Two-Step Bayes Classification Framework. BioMed Research International،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1003436
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Assawamakin, Anunchai…[et al.]. Biomarker Selection and Classification of “-Omics” Data Using a Two-Step Bayes Classification Framework. BioMed Research International No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-1003436
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Assawamakin, Anunchai& Prueksaaroon, Supakit& Kulawonganunchai, Supasak& Shaw, Philip James& Varavithya, Vara& Ruangrajitpakorn, Taneth…[et al.]. Biomarker Selection and Classification of “-Omics” Data Using a Two-Step Bayes Classification Framework. BioMed Research International. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1003436
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1003436
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