Metric-space search in bioinformatics

Authors:
Daniel P. Miranke
Affiliations:
University of Texas at Austin
Venue:
SIGSPATIAL Special
Year:
2010

Citing 6
Cited 1

A metric model of amino acid substitution

Bioinformatics
A fast coarse filtering method for peptide identification by mass spectrometry

Bioinformatics
Speeding up tandem mass spectrometry database search: metric embeddings and fast near neighbor search

Bioinformatics
Approximate Similarity Search in Genomic Sequence Databases Using Landmark-Guided Embedding

SISAP '08 Proceedings of the First International Workshop on Similarity Search and Applications (sisap 2008)
Mining gene functional networks to improve mass-spectrometry-based protein identification

Bioinformatics
Indexing DNA sequences using q-grams

DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications

Detecting fuzzy amino acid tandem repeats in protein sequences

Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Of the many problems in biological data retrieval, the problem of biological sequence retrieval has the highest profile. The standard solution to this problem, BLAST, has ascended, like google, to become synonymous with search. Also like Google, BLAST leverages statistical properties as heuristics to create a good user experience. Ironically, many early biological sequence similarity efforts explicitly sought to model evolutionary distance as a metric-distance. Recent interest in metric-index methods has rekindled these early directions. A review of these efforts provides an opportunity to characterize the challenges and opportunities in similarity search of biological data.