Determining antigenic variants is a critical task in vaccine production. In recent decades, besides the well-known analysis of hemagglutination inhibition assay, various in silico models have been developed to predict...
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ISBN:
(数字)9781728186290
ISBN:
(纸本)9781728186306
Determining antigenic variants is a critical task in vaccine production. In recent decades, besides the well-known analysis of hemagglutination inhibition assay, various in silico models have been developed to predict antigenic variants mainly based on the hemagglutinin antigen sequence. One of the initial but necessary steps in modeling is the numerical representation of the protein such that the characteristics of the protein can be reflected more significantly in associated with antigenicity. This paper is devoted to the comparison of various simplified forms of the amino acid alphabet and their performance in predicting the antigenic evolution of the influenza virus. The experimental results indicate that some simplified alphabets are robust than the conventional standard amino acid alphabet in terms of accuracy of predicting antigenic variants of the influenza virus.
Assessment of antigenic similarity between strains of the influenza virus is a crucial factor when planning vaccine compositions. To perform this, a gold-standard laboratory procedure, hemagglutination inhibition assa...
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ISBN:
(纸本)9781665476249
Assessment of antigenic similarity between strains of the influenza virus is a crucial factor when planning vaccine compositions. To perform this, a gold-standard laboratory procedure, hemagglutination inhibition assay, is conventionally used. Despite its theoretical importance and accuracy, this procedure suffers from several shortcomings, including high time consumption. Therefore, various computer-aided and math.matical methods have been developed to acquire earlier knowledge on the antigenic characteristics of currently circulating viruses. In this paper, we introduce a state-of-the-art ensemble artificial neural network model based on features derived from multi-representation of antigenicity. Generally, each feature is generated from an optimized convolutional neural network whose input describes the genetic difference between viruses in a specific numerical space. The space is determined based on embedding of the genetic sequence by a reduced amino acid alphabet and the Word2Vec framework. Our experiments indicated that the proposed model outperformed approaches from the literature by achieving an accuracy level of 0.933 for the HINI subtype. This implies possible application of our model as a promising exploratory tool in practical tasks of virus control.
Viral population surveillance is an essential task in public health, especially in preventing pandemics. The Omsk hemorrhagic fever virus (OHFV) is an etiological agent which is close to tick-borne encephalitis virus....
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Given the inorder and preorder traversal of a binary tree whose labels are all distinct, one can reconstruct the tree. This article examines two existing algorithms for rebuilding the tree in a functional framework, u...
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