BRAINSTORMING - Agent based Meta-learning Approach

Dariusz Plewczynski


Brainstorming meta-learning approach is performed by a set of cognitive agents (CA), each implementing different machine learning (ML) algorithm, and/or trained using diverse subsets of available features describing input examples. The goal of the meta-learning procedure is providing a general and flexible classification meta-model for a given training data. In the first phase all agents, when trained using different features describing training objects, construct the ensemble of classification models independently. In the second step all solutions are gathered and the consensus is built between them by optimizing the voting weights for all agents. No early solution, given even by a generally low performing agent, is not discarded until the late phase of prediction, when comparing different learning models draws the final conclusion. The final phase, i.e. brainstorming tries to balance the generality of solution and the overall cognitive performance of all CAs. The classification meta-model is than used for predictions of the classification membership for given testing examples. The method was recently used in several ML applications in bioinformatics and chemoinformatics by the author.


  1. Can, T., et al., Automated protein classification using consensus decision. Proc IEEE Comput Syst Bioinform Conf, 2004: p. 224-35.
  2. Han, X., Cancer molecular pattern discovery by subspace consensus kernel classification. Comput Syst Bioinformatics Conf, 2007. 6: p. 55-65.
  3. Schulze-Kremer, S. and R.D. King, IPSA-Inductive Protein Structure Analysis. Protein Eng, 1992. 5(5): p. 377-90.
  4. Vernikos, G. S. and J. Parkhill, Resolving the structural features of genomic islands: a machine learning approach. Genome Res, 2008. 18(2): p. 331-42.
  5. Arimoto, R., M. A. Prasad, and E.M. Gifford, Development of CYP3A4 inhibition models: comparisons of machine-learning techniques and molecular descriptors. J Biomol Screen, 2005. 10(3): p. 197-205.
  6. Bhasin, M. and G. P. Raghava, Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine, 2004. 22(23-24): p. 3195-204.
  7. Briem, H. and J. Gunther, Classifying "kinase inhibitorlikeness" by using machine-learning methods. Chembiochem, 2005. 6(3): p. 558-66.
  8. Burton, J., et al., Virtual screening for cytochromes p450: successes of machine learning filters. Comb Chem High Throughput Screen, 2009. 12(4): p. 369-82.
  9. Yao, X. Q., H. Zhu, and Z. S. She, A dynamic Bayesian network approach to protein secondary structure prediction. BMC Bioinformatics, 2008. 9: p. 49.
  10. Abrusan, G., et al., TEclass--a tool for automated classification of unknown eukaryotic transposable elements. Bioinformatics, 2009. 25(10): p. 1329-30.
  11. Hwang, S., Z. Gou, and I. B. Kuznetsov, DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins. Bioinformatics, 2007. 23(5): p. 634-6.
  12. Garg, P., et al., SubCellProt: predicting protein subcellular localization using machine learning approaches. In Silico Biol, 2009. 9(1-2): p. 35-44.
  13. Miller, M. L. and N. Blom, Kinase-specific prediction of protein phosphorylation sites. Methods Mol Biol, 2009. 527: p. 299-310, x.
  14. Bindewald, E. and B. A. Shapiro, RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers. RNA, 2006. 12(3): p. 342-52.
  15. Plewczynski, D. and K. Ginalski, The interactome: predicting the protein-protein interactions in cells. Cell Mol Biol Lett, 2009. 14(1): p. 1-22.
  16. Plewczynski, D., et al., The RPSP: Web server for prediction of signal peptides. Polymer, 2007. 48: p. 5493-5496.
  17. Plewczynski, D., A. H. Spieser, and U. Koch, Performance of Machine Learning Methods for Ligand-Based Virtual Screening. Combinatorial Chemistry & High Throughput Screening, 2009.
  18. Plewczynski, D., et al., AutoMotif server: prediction of single residue post-translational modifications in proteins. Bioinformatics, 2005. 21(10): p. 2525-7.
  19. Plewczynski, D., et al., Virtual High Throughput Screening Using Combined Random Forest and Flexible Docking. Combinatorial Chemistry & High Throughput Screening, 2009.
  20. Plewczynski, D., S. A. H. Spieser, and U. Koch, Assessing different classification methods for virtual screening. Journal of Chemical Information and Modeling, 2006. 46(3): p. 1098-1106.
  21. Bruce, C. L., et al., Contemporary QSAR classifiers compared. J Chem Inf Model, 2007. 47(1): p. 219-27.
  22. Islam, M. M., et al., Bagging and boosting negatively correlated neural networks. IEEE Trans Syst Man Cybern B Cybern, 2008. 38(3): p. 771-84.
  23. Plewczynski, D., S. A. Spieser, and U. Koch, Performance of machine learning methods for ligand-based virtual screening. Comb Chem High Throughput Screen, 2009. 12(4): p. 358-68.
  24. Schwenk, H. and Y. Bengio, Boosting neural networks. Neural Comput, 2000. 12(8): p. 1869-87.
  25. Serpen, G., D. K. Tekkedil, and M. Orra, A knowledgebased artificial neural network classifier for pulmonary embolism diagnosis. Comput Biol Med, 2008. 38(2): p. 204-20.
  26. Shrestha, D. L. and D. P. Solomatine, Experiments with AdaBoost.RT, an improved boosting scheme for regression. Neural Comput, 2006. 18(7): p. 1678-710.
  27. 27. Peng, Y., A novel ensemble machine learning for robust microarray data classification. Comput Biol Med, 2006. 36(6): p. 553-73.
  28. Wang, C. W., New ensemble machine learning method for classification and prediction on gene expression data. Conf Proc IEEE Eng Med Biol Soc, 2006. 1: p. 3478- 81.
  29. Yang, J. Y., et al., A hybrid machine learning-based method for classifying the Cushing's Syndrome with comorbid adrenocortical lesions. BMC Genomics, 2008. 9 Suppl 1: p. S23.
  30. Devillers, J., et al., Internet resources for agent-based modelling. SAR QSAR Environ Res. 21(3-4): p. 337- 50.
  31. Moore, D., et al., Extending drug ethno-epidemiology using agent-based modelling. Addiction, 2009. 104(12): p. 1991-7.
  32. Farmer, J. D. and D. Foley, The economy needs agentbased modelling. Nature, 2009. 460(7256): p. 685-6.
  33. Gu, W. and R.J. Novak, Agent-based modelling of mosquito foraging behaviour for malaria control. Trans R Soc Trop Med Hyg, 2009. 103(11): p. 1105- 12.
  34. Pogson, M., et al., Introducing spatial information into predictive NF-kappaB modelling--an agent-based approach. PLoS ONE, 2008. 3(6): p. e2367.
  35. 35. Sun, T., et al., Agent based modelling helps in understanding the rules by which fibroblasts support keratinocyte colony formation. PLoS ONE, 2008. 3(5): p. e2129.
  36. Bryson, J. J., Y. Ando, and H. Lehmann, Agent-based modelling as scientific method: a case study analysing primate social behaviour. Philos Trans R Soc Lond B Biol Sci, 2007. 362(1485): p. 1685-98.
  37. Pogson, M., et al., Formal agent-based modelling of intracellular chemical interactions. Biosystems, 2006. 85(1): p. 37-45.
  38. Walker, D. C., et al., The epitheliome: agent-based modelling of the social behaviour of cells. Biosystems, 2004. 76(1-3): p. 89-100.
  39. Plewczynski, D., Mean-field theory of meta-learning. Journal of Statistical Mechanics-Theory and Experiment, 2009: p. -.
  40. Plewczynski, D., Landau theory of social clustering. Physica A, 1998. 261(3-4): p. 608-617.
  41. Plewczynski, D., Brainstorming: weighted voting prediction of inhibitors for protein targets. J Mol Model, 2010.

Paper Citation

in Harvard Style

Plewczynski D. (2011). BRAINSTORMING - Agent based Meta-learning Approach . In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8425-41-6, pages 487-490. DOI: 10.5220/0003310104870490

in Bibtex Style

author={Dariusz Plewczynski},
title={BRAINSTORMING - Agent based Meta-learning Approach},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},

in EndNote Style

JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - BRAINSTORMING - Agent based Meta-learning Approach
SN - 978-989-8425-41-6
AU - Plewczynski D.
PY - 2011
SP - 487
EP - 490
DO - 10.5220/0003310104870490