Unsupervised ensemble learning for genome sequencing
Visualitza/Obre
10.1016/j.patcog.2022.108721
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/367991
Tipus de documentArticle
Data publicació2022-09
Condicions d'accésAccés obert
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continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional
ProjecteAVANCES EN CODIFICACION Y PROCESADO DE SEÑAL PARA LA SOCIEDAD DIGITAL (AEI-PID2019-104958RB-C41)
Abstract
Unsupervised ensemble learning refers to methods devised for a particular task that combine data provided by decision learners taking into account their reliability, which is usually inferred from the data. Here, the variant calling step of the next generation sequencing technologies is formulated as an unsupervised ensemble classification problem. A variant calling algorithm based on the expectation-maximization algorithm is further proposed that estimates the maximum-a-posteriori decision among a number of classes larger than the number of different labels provided by the learners. Experimental results with real human DNA sequencing data show that the proposed algorithm is competitive compared to state-of-the-art variant callers as GATK, HTSLIB, and Platypus.
CitacióPagès-Zamora, A. [et al.]. Unsupervised ensemble learning for genome sequencing. "Pattern recognition", Setembre 2022, vol. 129, article 108721, p. 1-9.
ISSN0031-3203
Versió de l'editorhttps://www.sciencedirect.com/science/article/pii/S0031320322002023
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