Treffer: PPPA : Push and pull pedigree analyzer for large and complex pedigree databases

Title:
PPPA : Push and pull pedigree analyzer for large and complex pedigree databases
Source:
Advances in databases and information systems (10th East European conference, ADBIS 2006, Thessaloniki, Greece, September 3-7, 2006)Lecture notes in computer science. :339-352
Publisher Information:
Berlin; New York: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 25 ref 1
Original Material:
INIST-CNRS
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Science Free University of Bozen-Bolzano Dominikanerplatz-3, 39100, Bozen, Italy
ISSN:
0302-9743
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19150784
Database:
PASCAL Archive

Weitere Informationen

In this paper we introduce a novel push and pull technique to analyze pedigree data. We present the Push and Pull Pedigree Analyzer (PPPA) to organize large and complex pedigrees and investigate the development of genetic diseases. PPPA receives as input a pedigree (ancestry information) of different families. For each person the pedigree contains information about the occurrence of a specific genetic disease. We propose a new solution to arrange and visualize the individuals of the pedigree based on the relationships between individuals and information about the disease. PPPA starts with random positions of the individuals, and iteratively pushes apart non-relatives with opposite diseases patterns and pulls together relatives with identical disease patterns. The goal is a visualization that groups families with homogeneous disease patterns. We investigate our solution experimentally with genetic data from peoples from South Tyrol, Italy. We show that the algorithm converges independent of the number of individuals n and the complexity of the relationships. The runtime of the algorithm is super-linear wrt n. The space complexity of the algorithm is linear wrt n. The visual analysis of the method confirms that our push and pull technique successfully deals with large and complex pedigrees.