Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Enhanced Biclustering on Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Solving Multiobjective Optimization Problems Using an Artificial Immune System
Genetic Programming and Evolvable Machines
An effective use of crowding distance in multiobjective particle swarm optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Biclustering of Expression Data Using Simulated Annealing
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
A multi-objective approach to discover biclusters in microarray data
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Applying biclustering to text mining: an immune-inspired approach
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
A novel genetic algorithm based on immunity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 0.00 |
High throughput technologies yield large-scale datasets on genomic variation in diverse populations, allowing the study of these variations and their association with disease and their complex traits. Systematic functional characterization of genes identified in the genome sequencing projects is urgently needed in the post-genomic era. Biclustering, which searches for subsets of individuals that are coherent in their behavior across a subset of the features, is a very useful data mining technique in microarray data analysis and has presented its advantages in many applications. This paper proposes a novel multi-objective immune biclustering (MOIB) algorithm, based on the immune response principle of the immune system, to mine biclusters from microarray data. In the algorithm, we extends ε-dominance and performs the mechanism of crowding computation to obtain many Pareto optimal solutions distributed onto the Pareto front. Experimental results on real datasets show that our approach can effectively find more significant biclusters than other biclustering algorithms.