The combination approach of SVM and ECOC for powerful identification and classification of transcription factor

Background: Transcription factors (TFs) are core functional proteins which play important roles in gene expression control, and they are key factors for gene regulation network construction. Traditionally, they were identified and classified through experimental approaches. In order to save time and reduce costs, many computational methods have been developed to identify TFs from new proteins and to classify the resulted TFs. Though these methods have facilitated screening of TFs to some extent, low accuracy is still a common problem. With the fast growing number of new proteins, more precise algorithms for identifying TFs from new proteins and classifying the consequent TFs are in a high demand. Results: The support vector machine (SVM) algorithm was utilized to construct an automatic detector for TF identification, where protein domains and functional sites were employed as feature vectors. Error-correcting output coding (ECOC) algorithm, which was originated from information and co...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Guangyong Zheng, Ziliang Qian, Qing Yang, Chaochun Wei, Lu Xie, Yangyong Zhu, Yixue Li
• Keywords: Algorithm, BMCBI 2008, Protein, Support Vector Machine