| | |
| | |
Stat |
Members: 3643 Articles: 2'488'730 Articles rated: 2609
29 March 2024 |
|
| | | |
|
Article overview
| |
|
Detecting somatic mutations in genomic sequences by means of Kolmogorov-Arnold analysis | V.G. Gurzadyan
; H. Yan
; G. Vlahovic
; A. Kashin
; P. Killela
; Z. Reitman
; S. Sargsyan
; G. Yegorian
; G. Milledge
; B. Vlahovic
; | Date: |
12 Jun 2015 | Abstract: | The Kolmogorov-Arnold stochasticity parameter technique is applied for the
first time to the study of cancer genome sequencing, to reveal mutations. Using
data generated by next generation sequencing technologies, we have analyzed the
exome sequences of brain tumor patients with matched tumor and normal blood. We
show that mutations contained in sequencing data can be revealed using this
technique thus providing a new methodology for determining subsequences of
given length containing mutations i.e. its value differs from those of
subsequences without mutations. A potential application for this technique
involves simplifying the procedure of finding segments with mutations, speeding
up genomic research, and accelerating its implementation in clinical
diagnostic. Moreover, the prediction of a mutation associated to a family of
frequent mutations in numerous types of cancers based purely on the value of
the Kolmogorov function, indicates that this applied marker may recognize
genomic sequences that are in extremely low abundance and can be used in
revealing new types of mutations. | Source: | arXiv, 1506.4080 | Services: | Forum | Review | PDF | Favorites |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser claudebot
|
| |
|
|
|
| News, job offers and information for researchers and scientists:
| |