Copy-number alterations for tumor progression inference

Claudia Cava, Italo Zoppis, Manuela Gariboldi, Isabella Castiglioni, Giancarlo Mauri, Marco Antoniotti

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

Abstract

Copy-number alterations (CNAs) represent an important component of genetic variations and play a significant role in many human diseases. Such alterations are related to certain types of cancers, including those of the pancreas, colon, and breast, among others. CNAs have been used as biomarkers for cancer prognosis in multiple studies, but few works report on the relation of CNAs with the disease progression. In this paper, we provide cases where the inference on the disease progression improves when exploiting CNA information. To this aim, a specific dissimilarity-based representation of patients is given. The employed framework outperforms a typical approach where patients are represented through a set of available attribute values. Three datasets were employed to validate the results of our analysis.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages104-109
Number of pages6
Volume7885 LNAI
ISBN (Print)9783642383250
DOIs
Publication statusPublished - 2013
Event14th Conference on Artificial Intelligence in Medicine, AIME 2013 - Murcia, Spain
Duration: May 29 2013Jun 1 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7885 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other14th Conference on Artificial Intelligence in Medicine, AIME 2013
CountrySpain
CityMurcia
Period5/29/136/1/13

Keywords

  • CNAs
  • dissimilarity representation
  • tumor progression

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'Copy-number alterations for tumor progression inference'. Together they form a unique fingerprint.

Cite this