Alignment-free sequence comparison using absent words
- Submitting institution
-
Loughborough University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1959
- Type
- D - Journal article
- DOI
-
10.1016/j.ic.2018.06.002
- Title of journal
- Information and Computation
- Article number
- -
- First page
- 57
- Volume
- 262
- Issue
- Part 1
- ISSN
- 0890-5401
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work impacts the state of the art by providing the first efficient algorithms for the problem of sequence comparison (how similar two sequences are) by making use of the information they are missing (as opposed to the shared similarities). All results are backed by open-source implementations which investigate the potential resulting applications, e.g. for music (classifying early OMRs of music pieces: http://research.gold.ac.uk/24502/) and biology (bacteria classification: doi:10.3934/medsci.2018.1.23).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -