Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
A Conflict Resolution-Based Decentralized Multi-Agent Problem Solving Model
MAAMAW '92 Selected papers from the 4th European Workshop on on Modelling Autonomous Agents in a Multi-Agent World, Artificial Social Systems
Novel Unsupervised Feature Filtering of Biological Data
Bioinformatics
Accurate Cancer Classification Using Expressions of Very Few Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Attractive Feature Reduction Approach for Colon Data Classification
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 01
Multiagent Approach for Identifying Cancer Biomarkers
BIBM '09 Proceedings of the 2009 IEEE International Conference on Bioinformatics and Biomedicine
Multiagent reinforcement learning using function approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Hi-index | 0.00 |
Locating exceptional, abnormal or unusual trends in gene expression data to identifying disease biomarkers is the vital problem tackled in this paper. We developed a comprehensive framework that incorporates different perspectives each realised by an agent. Each agent applies its method to analyse the gene expression data and to come up with some candidate genes as potential cancer biomarkers. Further, gene enrichment, protein interaction, and miRNA regulation are given weight; they are used to confirm the discoveries by the major agents. We conducted experiments on two data sets; the obtained results are very encouraging with a high classification rate.