short-paper

A noise-aware method for building radiation hybrid maps

Authors:

Raed I. Seetan,

Anne M. Denton,

M. Javed Iqbal,

Shahryar F. KianianAuthors Info & Claims

BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Pages 541 - 550

https://doi.org/10.1145/2649387.2649443

Published: 20 September 2014 Publication History

Abstract

The large numbers of markers in high-resolution radiation hybrid (RH) maps, increasingly necessitates the use of data mining techniques for reducing both the computational complexity and the impact of noise of the original data. Traditionally, the RH mapping process has been treated as equivalent to the traveling salesman problem, with the correspondingly high computational complexity. These techniques are also susceptible to noise, and unreliable marker can result in major disruptions of the overall order. In this paper, we propose a new approach that recognizes that the focus on nearest-neighbor distances that characterizes the traveling-salesman model, is no longer appropriate for the large number of markers in modern high-resolution mapping experiments. The proposed approach splits the mapping process into two levels, where the higher level only operates on the most stable markers of the lower level. A divide and conquer strategy, which is applied at the lower level, removes much of the impact of noise. Because of the high density of markers, only the most stable representatives from the lower level are then used at the higher level. The groupings within the lower level are so small that exhaustive search can be used. Markers are then mapped iteratively, while excluding problematic markers. The results for RH mapping dataset of the human genome show that the proposed approach can construct high-resolution maps with high agreement with the physical maps in a comparatively very short time.

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Cited By

Seetan RBible JKaravias MSeitan WThangiah S(2017)Radiation Hybrid Mapping: A Resampling-based Method for Building High-Resolution MapsAdvances in Science, Technology and Engineering Systems Journal10.25046/aj02031752:3(1390-1400)Online publication date: Aug-2017
https://doi.org/10.25046/aj0203175
Seetan RBible JKaravias MSeitan WThangiah S(2016)Consensus Clustering: A Resampling-Based Method for Building Radiation Hybrid Maps2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA.2016.0047(240-245)Online publication date: Dec-2016
https://doi.org/10.1109/ICMLA.2016.0047

Index Terms

A noise-aware method for building radiation hybrid maps
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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cover image ACM Conferences

BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

September 2014

851 pages

ISBN:9781450328944

DOI:10.1145/2649387

General Chairs:
Pierre Baldi
University of California, Irvine
,
Wei Wang
University of California, Los Angeles

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 20 September 2014

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Division of Integrative Organismal Systems

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BCB '14

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BCB '14: ACM-BCB '14

September 20 - 23, 2014

California, Newport Beach

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Cited By

Seetan RBible JKaravias MSeitan WThangiah S(2017)Radiation Hybrid Mapping: A Resampling-based Method for Building High-Resolution MapsAdvances in Science, Technology and Engineering Systems Journal10.25046/aj02031752:3(1390-1400)Online publication date: Aug-2017
https://doi.org/10.25046/aj0203175
Seetan RBible JKaravias MSeitan WThangiah S(2016)Consensus Clustering: A Resampling-Based Method for Building Radiation Hybrid Maps2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA.2016.0047(240-245)Online publication date: Dec-2016
https://doi.org/10.1109/ICMLA.2016.0047

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