Unsupervised ensemble learning for genome sequencing
| Autor: | Alba Pagès-Zamora, Idoia Ochoa, Gonzalo Ruiz Cavero, Pol Villalvilla-Ornat |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Informàtica::Aplicacions de la informàtica::Bioinformàtica [Àrees temàtiques de la UPC]
Unsupervised multi-class ensemble Unsupervised multi-class ensemble classifier Framework Área Ciencias de la Computación y Tecnología Informática GATK Classifier Genome sequencing Enginyeria de la telecomunicació::Processament del senyal [Àrees temàtiques de la UPC] Gens -- Mapatge ComputingMethodologies_PATTERNRECOGNITION Artificial Intelligence Signal Processing Variant calling Expectation maximization algorithm Computer Vision and Pattern Recognition Algorismes EM Expectation-maximization algorithms Software Gene mapping |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Pattern Recognition, 129 |
| ISSN: | 0031-3203 1873-5142 |
| Popis: | 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. Pattern Recognition, 129 ISSN:0031-3203 ISSN:1873-5142 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect