NDLI: A noise-aware method for building radiation hybrid maps

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A noise-aware method for building radiation hybrid maps

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A noise-aware method for building radiation hybrid maps

Content Provider	ACM Digital Library
Author	Seetan, Raed I. Denton, Anne M. Kianian, Shahryar F. Kumar, Ajay Al-Azzam, Omar Iqbal, M. Javed
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.
Starting Page	541
Ending Page	550
Page Count	10
File Format	PDF
ISBN	9781450328944
DOI	10.1145/2649387.2649443
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-09-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Radiation hybrid mapping Clustering Data mining Noisy datasets Bioinformatics High-resolution maps
Content Type	Text
Resource Type	Article

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