Summary
The proposed bioinformatics project has strong ties to even several diverse disciplines, namely computer science, molecular biology, genetics, mathematics and statistics. RNA Biology plays a central role in bioinformatics research such that numerous model-driven algorithms and methods are developed to predict and calculate structures and functions or compare sequences of RNA molecules that are essential in molecular and genetic processes and thus for medical and pharmaceutical applications.
Many of these problems can be cleanly phrased as optimization problems with 'optimal substructure' and thus solved exactly and efficiently by dynamic programming (for arbitrary parametrization of the objective function). This project will systematically explore the impact of parameter changes on the quality of results of DP optimization methods, such as predictions of molecule structures or comparison of sequences. Our approach strongly relies on algebraic dynamic programming (ADP), which decouples the decomposition of the search space from the algebra used to compute a final result. Thus, the ADP framework provides a unified setting and a generic implementation to quickly test working hypotheses. Here, it enables naturally implementing the suggested parametric optimization by developing novel algebras. Those include the polytope algebra, which allows to segment the parameter space based on its impact of the final prediction, and a formal derivative algebra, which allows to compute the derivative of ensemble predictions with respect to a given parameter. Conversely, those methods can be used to learn the optimal parameter sets based on a reference set of instances. This will result in deeper insights into robustness of the algorithms to changes of parameters or input data and hence, results can be assessed based on robustness measures and leads to the calculation of biologically more meaningful results.
The overall methodology will be applied to problems in RNA Bioinformatics.
Many of these problems can be cleanly phrased as optimization problems with 'optimal substructure' and thus solved exactly and efficiently by dynamic programming (for arbitrary parametrization of the objective function). This project will systematically explore the impact of parameter changes on the quality of results of DP optimization methods, such as predictions of molecule structures or comparison of sequences. Our approach strongly relies on algebraic dynamic programming (ADP), which decouples the decomposition of the search space from the algebra used to compute a final result. Thus, the ADP framework provides a unified setting and a generic implementation to quickly test working hypotheses. Here, it enables naturally implementing the suggested parametric optimization by developing novel algebras. Those include the polytope algebra, which allows to segment the parameter space based on its impact of the final prediction, and a formal derivative algebra, which allows to compute the derivative of ensemble predictions with respect to a given parameter. Conversely, those methods can be used to learn the optimal parameter sets based on a reference set of instances. This will result in deeper insights into robustness of the algorithms to changes of parameters or input data and hence, results can be assessed based on robustness measures and leads to the calculation of biologically more meaningful results.
The overall methodology will be applied to problems in RNA Bioinformatics.
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| Web resources: | https://cordis.europa.eu/project/id/101029676 |
| Start date: | 01-05-2021 |
| End date: | 17-12-2024 |
| Total budget - Public funding: | 196 707,84 Euro - 196 707,00 Euro |
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Original description
The proposed bioinformatics project has strong ties to even several diverse disciplines, namely computer science, molecular biology, genetics, mathematics and statistics. RNA Biology plays a central role in bioinformatics research such that numerous model-driven algorithms and methods are developed to predict and calculate structures and functions or compare sequences of RNA molecules that are essential in molecular and genetic processes and thus for medical and pharmaceutical applications.Many of these problems can be cleanly phrased as optimization problems with 'optimal substructure' and thus solved exactly and efficiently by dynamic programming (for arbitrary parametrization of the objective function). This project will systematically explore the impact of parameter changes on the quality of results of DP optimization methods, such as predictions of molecule structures or comparison of sequences. Our approach strongly relies on algebraic dynamic programming (ADP), which decouples the decomposition of the search space from the algebra used to compute a final result. Thus, the ADP framework provides a unified setting and a generic implementation to quickly test working hypotheses. Here, it enables naturally implementing the suggested parametric optimization by developing novel algebras. Those include the polytope algebra, which allows to segment the parameter space based on its impact of the final prediction, and a formal derivative algebra, which allows to compute the derivative of ensemble predictions with respect to a given parameter. Conversely, those methods can be used to learn the optimal parameter sets based on a reference set of instances. This will result in deeper insights into robustness of the algorithms to changes of parameters or input data and hence, results can be assessed based on robustness measures and leads to the calculation of biologically more meaningful results.
The overall methodology will be applied to problems in RNA Bioinformatics.
Status
SIGNEDCall topic
MSCA-IF-2020Update Date
28-04-2024
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