Q84225 (Q84225): Difference between revisions
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(Created claim: summary (P836): In pattern matching, it is very common that the input data is corrupted or that we only have an imprecise model of the data. The project focuses on design of efficient algorithms for pattern matching and data structures for indexing for data with errors and Uncertainties. Our primary motivation is molecular biology, where several models for uncertain data are used: texts with wildcards, indeterminate texts, weighted sequences (i.e., position wei...) |
(Changed label, description and/or aliases in 1 language: remove_english_label) |
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Revision as of 12:31, 14 October 2020
Project in Poland financed by DG Regio
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | No label defined |
Project in Poland financed by DG Regio |
Statements
656,436.0 zloty
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656,436.0 zloty
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100.0 percent
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1 July 2017
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30 June 2019
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UNIWERSYTET WARSZAWSKI
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In pattern matching, it is very common that the input data is corrupted or that we only have an imprecise model of the data. The project focuses on design of efficient algorithms for pattern matching and data structures for indexing for data with errors and uncertainties. Our primary motivation is molecular biology, where several models for uncertain data are used: texts with wildcards, indeterminate texts, weighted sequences (i.e., position weight matrices) and profiles. We consider approximate pattern matching under the Hamming distance and various kinds of approximate periodicities (quasiperiodicities) in texts. We aim at worst-case efficient algorithms; however, recent study in the area of fine-grained complexity suggests that for some of the problems on texts, the state-of-the-art or even naive algorithms are probably optimal. We also aim at experimental verification of our approaches. (Polish)
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In pattern matching, it is very common that the input data is corrupted or that we only have an imprecise model of the data. The project focuses on design of efficient algorithms for pattern matching and data structures for indexing for data with errors and Uncertainties. Our primary motivation is molecular biology, where several models for uncertain data are used: texts with wildcards, indeterminate texts, weighted sequences (i.e., position weight matrices) and profiles. We consider approximate pattern matching under the Hamming distance and various kinds of approximate periodicities (quasiperiodicities) in texts. We aim at worst-case efficient algorithms; however, recent study in the area of fine-grained complexity suggests that for some of the problems on texts, the state-of-the-art or even naive algorithms are probably optimal. We also aim at experimental verification of our approaches. (English)
14 October 2020
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Identifiers
POIR.04.04.00-00-24BA/16
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