Clusters identification in binary genomic data: The alternative offered by scan statistics approach

Danilo Pellin, Clelia Di Serio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In many different research area, identification of clusters or regions showing an increment in event rate over a given study area is an important and interesting problem. Nowadays literature concerning scan statistics is quite broad and methods can be subdivided based on dimensional complexity of the study area, assumption on distribution generating the data under the null hypothesis and shape-dimension of the scanning window. The aim of this study is to adapt and apply this methodology to the genomics field taking into account for some peculiarities of these data and to compare its performance to existing method based on DBSCAN algorithm.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages149-158
Number of pages10
Volume8452 LNBI
ISBN (Print)9783319090412
DOIs
Publication statusPublished - 2014
Event10th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2013 - Nice, France
Duration: Jun 20 2013Jun 22 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8452 LNBI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other10th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2013
Country/TerritoryFrance
CityNice
Period6/20/136/22/13

Keywords

  • Binary genomic event
  • Hotspot
  • Scan statistics

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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