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Supervised and unsupervised algorithms for bioinformatics and data science

Sohail, Ayesha ; Arif, Fatima

Progress in biophysics and molecular biology, 2020-03, Vol.151, p.14-22 [Periódico revisado por pares]

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Título:
Supervised and unsupervised algorithms for bioinformatics and data science
Autor: Sohail, Ayesha ; Arif, Fatima
Assuntos: Algorithms ; Computational Biology - methods ; Data Science - methods ; Evolutionary bioinformatics ; Machine learning ; Supervised Machine Learning ; Support vector machine learning ; Unsupervised Machine Learning
É parte de: Progress in biophysics and molecular biology, 2020-03, Vol.151, p.14-22
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-3
content type line 23
ObjectType-Review-1
Descrição: Bioinformatics refers to an ever evolving huge field of research based on millions of algorithms, designated to several data banks. Such algorithms are either supervised or unsupervised. In this article, a detailed overview of the supervised and unsupervised techniques is presented with the aid of examples. The aim of this article is to provide the readers with the basic understanding of the state of the art models, which are key ingredients of explainable machine learning in the field of bioinformatics.
Editor: England: Elsevier Ltd
Idioma: Inglês

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