Soft Computing Methods in Bioinformatics

Bioinformatics and computational biology present many complex optimization and data mining problems that can be addressed using soft computing methods. The amount of information from biological experiments and the applications involving large-scale high-throughput technologies is rapidly increasing nowadays. Soft computing and machine learning algorithms are well-suited for many bioinformatics problems including gene selection, clustering and classification, signal processing and image analysis. The scope of this special session covers the development of computational models and algorithms for bioinformatics data mining. This special session will highlight the applications of soft computing methods to a broad range of topics from bioinformatics.


  1. Microarray and gene expression analysis
  2. Gene regulatory and ontology networks
  3. Protein and RNA structure prediction
  4. Analysis of large biological datasets
  5. Data integration and fusion
  6. Classification and clustering
  7. Feature selection
  8. Signal processing
  9. Text mining in molecular biology
  10. Gene regulation and transcriptomics
  11. Molecular docking and drug design
  12. Metabolic pathway analysis
  13. Image analysis in bioinformatics
  14. Computational proteomics


This research is funded by the Ministry of Economy and Competitiviness project TIN2011-24302.

  • Camelia Chira, Instituto Tecnológico de Castilla y León (Spain) & Babes-Bolyai University (Romania)
  • José Ramón Villar, University of Oviedo (Spain)
  • Emilio Corchado, University of Salamanca (Spain)
  • Javier Sedano, Instituto Tecnológico de Castilla y León (Spain)


    Camelia Chira
    Instituto Tecnológico de Castilla y León (Spain) & Babes-Bolyai University (Romania)
    Polígono Industrial Villalonquéjar
    c/López Bravo 70, 09001 Burgos, Spain
    +34 947 298 471

Important Dates


Paper submission


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