Computability and learnability in sequential weightless neural networks

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IEEE/ACM Transactions on Computational Biology and Bioinformatics: Vol. 10, No. 4, July-August 2013

1. Guest Editorial for Special Section on BSB 2012 - Marcilio C.P. de Souto and Maricel Kann / M. C. P. de Souto, M. Kann 2. Extending the Algebraic Formalism for Genome Rearrangements to Include Linear Chromosomes / Pedro Feijao, Joao Meidanis 3. 2D Meets 4G : G-Quadruplexes in RNA Secondary Structure Prediction / Ronny Lorenz, Stephan H. Bernhart, Jing Qin 4. Proximity Measures for Clustering Gene Expression Microarray data : A Validation Methodology and a Comparative Analysis/ Pablo A. Jaskowiak, Ricardo J.G.B. Campello, and Ivan G. Costa. 5. A Closed-Loo Control Scheme for Streering Stead States of Glycolysis and Glycogenolysis Pathway / S. Panja, S. Patra, A. Mukherjee, M. Basu, S. Sengupta, P. K. Dutta 6. A Divide and Conquer Approach for Construction of Large-Scale Signaling Networks from PPI and RNAi Data Using Linear Programing / O. E. Ozsoy, T. Can 7. A Knowledge-Based Multiple-Sequence Alignment Algorithm / K. D. Nguyen, Y. Pan 8. A Two-Phase Bio-NER System Based on Untegrated Classifiers and Multiagent Strategy / L. Li W Fan, D. Huang 9. An Improved Approximation Algorithm for Scaffold Filling to Maximize the Common Adjacencies / N. Liu, H. Jiang, D. Zhu, B. Zhu 10. An Optimization Rule for in Silico Identification of Targeted Overproduction in Metabolic Pathways / M. Das, C. A. Murthy, R. K. De 11. Algebraic Representation of Asynchronous Multiple-Valued Networks and Its Dynamics / C. Luo, X. Wang 12. Algorithms of Genome-scale Phylogenetics Using Gene Tree Parsimony / M. S. Bansal, O. Eulenstein 13. Analytical Solution of Steady-State Equations for Chemical Reaction Networks with Bilinear Rate Laws / A. M. Halasz, H. -J. Lai, M. McCabe Pryor, K. Radhakrishnan, J. S. Edwar

Selecting Machine Learning Algorithms Using the Ranking Meta-Learning Approach

In this work, we present the use of Ranking Meta-Learning approaches to ranking and selecting algorithms for problems of time series forecasting and clustering of gene expression data. Given a problem (forecasting or clustering), the Meta-Learning approach provides a ranking of the candidate algorithms, according to the characteristics of the problem’s dataset. The best ranked algorithm can be returned as the selected one. In order to evaluate the Ranking Meta-Learning proposal, prototypes were implemented to rank artificial neural networks models for forecasting financial and economic time series and to rank clustering algorithms in the context of cancer gene expression microarray datasets. The case studies regard experiments to measure the correlation between the suggested rankings of algorithms and the ideal rankings. The results revealed that Meta-Learning was able to suggest more adequate rankings in both domains of application considered