Proteomic Pattern Classification Using Bio-markers for Prostate Cancer Diagnosis

• Published in: Computational and Information Science pp. 631 - 638
• Main Authors: Kim, Jung-Ja, Kim, Young-Ho, Won, Yonggwan
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2004; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Benign Prostate Hyperplasia; Computer science; control theory; systems; Decision Tree; Decision Tree Approach; Descendent Node; Exact sciences and technology; Pattern recognition. Digital image processing. Computational geometry; Proteomic Pattern; Human; Neural network; Malignant tumor; Hybrid system; Multiagent system; Cancerology; Multidimensional database; Pattern classification; Proteomics; Multilayer perceptrons; Bioinformatics; Prostate; Medical application; Multilayer network

Decision trees (DTs) and multi-layer perceptron (MLP) neural networks have long been successfully used to various pattern classification problems. Those two classification models have been applied to a number of diverse areas for the identification of ‘biologically relevant’ molecules. Surface enhan...