کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
377960 658857 2009 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Fully non-homogeneous hidden Markov model double net: A generative model for haplotype reconstruction and block discovery
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Fully non-homogeneous hidden Markov model double net: A generative model for haplotype reconstruction and block discovery
چکیده انگلیسی

SummaryObjectiveIn the last decade, haplotype reconstruction in unrelated individuals and haplotype block discovery have riveted the attention of computer scientists due to the involved strong computational aspects. Such tasks are usually addressed separately, but recently, statistical techniques have permitted them to be solved jointly. Following this trend we propose a generative model that permits researchers to solve the two problems jointly.MethodThe model inference is based on variational learning, which permits one to estimate quickly the model parameters while remaining robust even to local minima. The model parameters are then used to segment genotypes into blocks by thresholding a quantitative measure of boundary presence.ResultsExperiments on real data are presented, and state-of-the-art systems for haplotype reconstruction and strategies for block estimation are considered as comparison.ConclusionsThe proposed method can be used for a fast and reliable estimation of haplotype frequencies and the relative block structure. Moreover, the method can be easily used as part of a more complex system. The threshold used for block discovery can be related to the quality-of-fit reached in the model learning, resulting in an unsupervised strategy for block estimation.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Artificial Intelligence in Medicine - Volume 45, Issues 2–3, February–March 2009, Pages 135–150
نویسندگان
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