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Call for Papers
We encourage submission of quality papers in the following general topics of the conference:

BSBT attempts to capture cross-fertilization of ideas in the convergence of Bio and information science and the scope is kept deliberately broad to encourage participation. The following categorization may not exactly fit all but gives an indication of possible convergence areas and some examples of the communities from which our participants may originate:
 
 
Convergence Areas:      Relevant Communities:
 
Hardware meeting Biology Bioelectronics, Biomimetics, BioMEMS, Wearable Devices for the Ubiquitous Environment
 
Mathematics meeting Anatomy Biometric Security, Computational Anatomy, Visualisation: (molecules, blood flow)
 
Computing Evolution and Origin of Disease Evolutionary Theory, Genetics and Biomarkers, Phylogenetics and Cladistics, Simulation: (Gaia, Tierra)
 
Computing the Living System Cybernetics, Complexity, Computational Applications in Metabonomics, in-Silico Biology, Systems Biology
 
Modelling the Cell Microarray Analysis, Metabolic Control Analysis, Metabolomics, Robotic Chemistry, Pathways (Genetic, Signalling, Metabolic), Proteomics, Protein Folding, Molecular Docking, Transcriptomics: Promoters Motifs and RNA, Analogy between Linguistics and Chemistry, Information Management in Bioinformatics
 
Biologically Inspired Computation DNA Computing, Evolvable Hardware, Machine Intelligence, Immune Systems Computing, Genetic Programming
 
Computing Learning or Behaviour Mathematical Biology, Teaching Humanoid Robots, Modelling Learning in Living Systems, Ontologies
 

Papers on any aspect of the above topics are welcome. Below are just a few examples of the many challenges relating to the above topics for those with the disposition to tackle the interdisciplinary or a more ambitious paper - although some are less directly related to the topics of the conference, they are so interesting that we will accept serious contributions on any of these.

  • Study of Intelligence
    • Architecture of Brain and Mind
    • Definition of Intelligence: requirement to manipulate the environment; creativity; common sense.
    • Computational Intelligence and Intelligent Systems
  • Molecular Biology Drug Discovery and Evolution
    • Linguistics and Genetics: parallels between languages and chemistry and what discoveries can these reveal.
    • The C value enigma: new algorithms or tactics that may discover putative function of DNA and of our cellular molecular machinery.
    • Can cybernetic principles and Shannon laws help Molecular Biology?
    • Complexity threshold in Genomics: measures to discover what is the complexity beyond which it becomes reasonable to apply Occam's Razor.
    • Macro and Micro Evolution: what impact has "species as the individual".
    • Modelling approaches: empirical science; reductionism and objections to reductionism; complex systems theory and emergence; taxonomic analysis; role of analogy in discoveries; chance discovery.
    • Cladistics : can we define mathematical existence and uniqueness; can we set any bounds on past and future evolution.
    • Revealing experimental techniques.
  • Challenges in Biometrics:
    • Beyond biostatistics. Can we reverse engineer and refine recognition algorithms from vast amounts of data using AI and "white-box" analysis?
    • Presentational issues and social guarantees.
  • Interaction; Communication and Databases:
    • Context Databases: capturing context; feelings; hidden or intended meaning in databases.
    • Socio-technical: design and usability challenges in the interaction between humans and their personal computational systems.
    • Bio Interaction: Interaction Technology between Bio Systems and Machines.
    • Medical records and knowledge accumulation: where to place and how to ensure their integrity.
  • Research on Learning, and Predicting Social Behaviour or Culture
    • How do we learn most effectively? Which situations are learned best by means of simple mental exercises; or by playing pac -man; and which require immersion in realistic virtual reality games?
    • Learned or stochastic difference: predicting the ˇ°next timeˇ± behaviour: will the next event unfold differently because of what we have learned in the previous event? How much of it is non- ergodic?
    • "Composability" problem: in a quickly put together coalition each partner needs to guess the likely culture and behaviour of the other partner, what is the best strategy for doing this?
  • Psychology of human and machine
    • Instincts, goal setting, emotions and machine intelligence.
    • Plausible new devices to improve human life: e.g. fusion of wearable sensors and natural language processing for personal use in pattern matching during daily activities.
  • Forgotten technologies: resurrecting useful techniques lost in time.
 
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