Simulating soft data to make soft data applicable to simulation

Background: Biomedical processes are often influenced by measures considered "non-crisp", "soft" or "subjective". Despite the growing awareness of the importance of such measures, they are rarely considered in biomedical simulation. This study introduces an input generator for soft data (input generator SD) that makes soft data applicable to simulation. Materials and methods: Machine learning approaches and standard regression techniques were applied to simulate odour intensity ratings. Results: The performance of all the applied methods was satisfactory and the results can be used to modify systems biological mathematical models. Conclusion: Soft data should no longer be discounted in systems biological simulations. Exemplarily, it can be demonstrated that the input generator SD produces results that are similar to those that the simulated system can generate. Machine learning and/or appropriate conventional mathematical approaches may be applied to simulate non-crisp processes that can be used to modify mathematical models of any granularity.