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Pedro A. Bernaola-Galván

dblp:06/2348 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 1999
0000-0002-2895-3116ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › sequence analysis
DNA sequence analysis
0.011999
SEGMENT: identifying compositional domains in DNA sequences · Bioinform. 1999
Bioinformatics and computational biology › sequence analysis › sequence annotation
sequence segmentation
0.011999
SEGMENT: identifying compositional domains in DNA sequences · Bioinform. 1999
Bioinformatics and computational biology › sequence analysis
sequence complexity analysis
0.011999
SEGMENT: identifying compositional domains in DNA sequences · Bioinform. 1999

Methods — techniques the papers use, named apart from their topics

local optimization · 0.0heuristic segmentation · 0.0
YearPublicationVenuePosition
1999 SEGMENT: identifying compositional domains in DNA sequences
abstract
MOTIVATION: DNA sequences are formed by patches or domains of different nucleotide composition. In a few simple sequences, domains can simply be identified by eye; however, most DNA sequences show a complex compositional heterogeneity (fractal structure), which cannot be properly detected by current methods. Recently, a computationally efficient segmentation method to analyse such nonstationary sequence structures, based on the Jensen-Shannon entropic divergence, has been described. Specific algorithms implementing this method are now needed. RESULTS: Here we describe a heuristic segmentation algorithm for DNA sequences, which was implemented on a Windows program (SEGMENT). The program divides a DNA sequence into compositionally homogeneous domains by iterating a local optimization procedure at a given statistical significance. Once a sequence is partitioned into domains, a global measure of sequence compositional complexity (SCC), accounting for both the sizes and compositional biases of all the domains in the sequence, is derived. SEGMENT computes SCC as a function of the significance level, which provides a multiscale view of sequence complexity.
José L. Oliver, Ramón Román-Roldán, Javier Pérez, Pedro A. Bernaola-Galván
Bioinform.4
1996 Application of information theory to DNA sequence analysis: A review
Ramón Román-Roldán, Pedro A. Bernaola-Galván, José L. Oliver
Pattern Recognit.2
1994 Entropic feature for sequence pattern through iterated function systems
Ramón Román-Roldán, Pedro A. Bernaola-Galván, José L. Oliver
Pattern Recognit. Lett.2