Algorithms for text processing with errors and Uncertainties (Q84225): Difference between revisions
Jump to navigation
Jump to search
(Changed label, description and/or aliases in 1 language: remove_english_label) |
(Changed label, description and/or aliases in 1 language: translated_label) |
||
| label / en | label / en | ||
Algorithms for text processing with errors and Uncertainties | |||
Revision as of 12:31, 14 October 2020
Project in Poland financed by DG Regio
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Algorithms for text processing with errors and Uncertainties |
Project in Poland financed by DG Regio |
Statements
656,436.0 zloty
0 references
656,436.0 zloty
0 references
100.0 percent
0 references
1 July 2017
0 references
30 June 2019
0 references
UNIWERSYTET WARSZAWSKI
0 references
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)
0 references
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
0 references
Identifiers
POIR.04.04.00-00-24BA/16
0 references