2020
DOI: 10.1016/j.jtbi.2020.110215
|View full text |Cite
|
Sign up to set email alerts
|

Graph based analysis for gene segment organization In a scrambled genome

Abstract: DNA rearrangement processes recombine gene segments that are organized on the chromosome in a variety of ways. The segments can overlap, interleave or one may be a subsegment of another. We use directed graphs to represent segment organizations on a given locus where contigs containing rearranged segments represent vertices and the edges correspond to the segment relationships. Using graph properties we associate a point in a higher dimensional Euclidean space to each graph such that cluster formations and ana… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
2
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
3
1
1

Relationship

2
3

Authors

Journals

citations
Cited by 5 publications
(2 citation statements)
references
References 41 publications
(51 reference statements)
0
2
0
Order By: Relevance
“…In addition, the topological analysis of the image allows to extract features that are invariant to the spatial transformation and more robust to noise [4], [37]. TDA-based methods have shown excellent performance in several applications including neuroscience [13], [20], [24], bioscience [15], [21], and images [11], [19], among others.…”
Section: Introductionmentioning
confidence: 99%
“…In addition, the topological analysis of the image allows to extract features that are invariant to the spatial transformation and more robust to noise [4], [37]. TDA-based methods have shown excellent performance in several applications including neuroscience [13], [20], [24], bioscience [15], [21], and images [11], [19], among others.…”
Section: Introductionmentioning
confidence: 99%
“…These sets of tools have matured and become an area of research known as Topological Data Analysis (TDA) (Edelsbrunner & Harer (2010);Carlsson (2009)). TDA-based methods have shown excellent performance in several applications including neuroscience (Lee et al (2012)), bioscience (Chan et al (2013);DeWoskin et al (2010); Hajij et al (2020b)), in the study of graphs (Petri et al (2013a;b)), time-varying data (Edelsbrunner et al (2004); Hajij et al (2018)) among others.…”
Section: Introductionmentioning
confidence: 99%