Robust Naïve Bayes Algorithm for Gene Expression and PPI Data - Ahmed, Md. Shakil; Kamruzzaman, Md.
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The study of Microarray Gene Expression and Protein-Protein Interaction (PPI) Sites are most recent research wings in Bioinformatics and Computational Biology. It is very essential for drug design and personalized medicine. The statistical classification is a supervised learning approach concerned with separating distinct sets of objects and with allocating new objects to previously defined groups. Especially it plays the significant roles in the field of data mining and pattern recognition. In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on…mehr

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Produktbeschreibung
The study of Microarray Gene Expression and Protein-Protein Interaction (PPI) Sites are most recent research wings in Bioinformatics and Computational Biology. It is very essential for drug design and personalized medicine. The statistical classification is a supervised learning approach concerned with separating distinct sets of objects and with allocating new objects to previously defined groups. Especially it plays the significant roles in the field of data mining and pattern recognition. In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on the Bayes theorem with strong independence assumptions among the features. The Naive Bayes Classifier technique is particularly suited when the dimensionality of the inputs is high. Naïve Bayes classification method is one of the most popular statistical techniques for analyzing multivariate data in multidisciplinary fields in Bioinformatics and Computational Biology.
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Autorenporträt
Md. Shakil Ahmed is a Researcher and Fiction Writer. He Received his B.Sc and M.Sc in Statistics and Bioinformatics from the University of Rajshahi, Bangladesh. He has published 6 articles in the international journals with good ISI impact factor and 9 full length & 20 abstract articles in the international conference proceedings.