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Hypothesis-Driven Virtual Experiments: A Neuroinformatics Primer
| Author(s): | Kovalev D. Yu. |
| Published: | Lobachevskii Journal of Mathematics, 2023. Vol. 44. Iss. 1. P. 178–187. |
| Abstract: | |
| The paper demonstrates a new way of decomposing complex multidisciplinary problems using a platform for supporting the execution hypothesis-driven virtual experiments. The approach is based on combining methods and tools from several domains to support hypothesis-driven, data-intensive research. A primer shows all steps from virtual experiment life cycle from its specification till execution. Platform enables to store virtual experiments, its metadata and other core artifacts, analyze dependencies between hypotheses/models, abandon certain workflow routes, plan experiment execution, compare models, range hypotheses and generate models from data if required. Demonstration is carried out with the problem of identifying differences in functional dependencies for men and women and for young and middle-aged adults which comes from neuroinformatics domain.
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| Download: |
[ https://link.springer.com/article/10.1134/S1995080223010250 ]
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