K-Nearest Robust Active Learning on Big Data and Application in Epitope Prediction

B-cells that induce antigen-specific immune responses in vivo produce large numbers of antigen-specific antibodies by recognizing subregions (epitopes) of antigenic proteins, in which they can inhibit the function of antigen protein. Epitope region prediction facilitates the design and development o...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autor principal:	Tianchi Lu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology T Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/2f7a70c9964b49f9990857e056106750
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2f7a70c9964b49f9990857e056106750
record_format	dspace
spelling	oai:doaj.org-article:2f7a70c9964b49f9990857e0561067502021-11-22T01:11:01ZK-Nearest Robust Active Learning on Big Data and Application in Epitope Prediction1530-867710.1155/2021/8752022https://doaj.org/article/2f7a70c9964b49f9990857e0561067502021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8752022https://doaj.org/toc/1530-8677B-cells that induce antigen-specific immune responses in vivo produce large numbers of antigen-specific antibodies by recognizing subregions (epitopes) of antigenic proteins, in which they can inhibit the function of antigen protein. Epitope region prediction facilitates the design and development of vaccines that induce the production of antigen-specific antibodies. There are many diseases which are difficult to treat without vaccines. And the COVID-19 has destroyed many people’s lives. Therefore, making vaccines to COVID-19 is very important. Making vaccines needs a large number of experiments to get labeled targets. However, obtaining tremendous labeled data from experiments is a challenge for humans. Big data analysis has proposed some solutions to deal with this challenge. Big data technology has developed very fast and has been applied in many areas. In the bioinformatics area, big data analysis solves a large number of problems, particularly in the area of active learning. Active learning is a method of building more predictive models with less labeled data. Active learning establishes models with less data by asking the oracle (human) for the most valuable samples to train models. Hence, active learning’s application in making vaccines is meaningful that the scientists do not need to do tremendous experiments. This paper proposed a more robust active learning method based on uncertainty sampling and K-nearest density and applies it to the vaccine manufacture. This paper evaluates the new algorithm with accuracy and robustness. In order to evaluate the robustness of active learners, a new robustness index is designed in this paper. And this paper compares the new algorithm with a pool-based active learning algorithm, density-weighted active learning algorithm, and traditional machine learning algorithm. Finally, the new algorithm is applied to epitope prediction of B-cell data, which is significant to making vaccines.Tianchi LuHindawi-WileyarticleTechnologyTTelecommunicationTK5101-6720ENWireless Communications and Mobile Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Telecommunication TK5101-6720
spellingShingle	Technology T Telecommunication TK5101-6720 Tianchi Lu K-Nearest Robust Active Learning on Big Data and Application in Epitope Prediction
description	B-cells that induce antigen-specific immune responses in vivo produce large numbers of antigen-specific antibodies by recognizing subregions (epitopes) of antigenic proteins, in which they can inhibit the function of antigen protein. Epitope region prediction facilitates the design and development of vaccines that induce the production of antigen-specific antibodies. There are many diseases which are difficult to treat without vaccines. And the COVID-19 has destroyed many people’s lives. Therefore, making vaccines to COVID-19 is very important. Making vaccines needs a large number of experiments to get labeled targets. However, obtaining tremendous labeled data from experiments is a challenge for humans. Big data analysis has proposed some solutions to deal with this challenge. Big data technology has developed very fast and has been applied in many areas. In the bioinformatics area, big data analysis solves a large number of problems, particularly in the area of active learning. Active learning is a method of building more predictive models with less labeled data. Active learning establishes models with less data by asking the oracle (human) for the most valuable samples to train models. Hence, active learning’s application in making vaccines is meaningful that the scientists do not need to do tremendous experiments. This paper proposed a more robust active learning method based on uncertainty sampling and K-nearest density and applies it to the vaccine manufacture. This paper evaluates the new algorithm with accuracy and robustness. In order to evaluate the robustness of active learners, a new robustness index is designed in this paper. And this paper compares the new algorithm with a pool-based active learning algorithm, density-weighted active learning algorithm, and traditional machine learning algorithm. Finally, the new algorithm is applied to epitope prediction of B-cell data, which is significant to making vaccines.
format	article
author	Tianchi Lu
author_facet	Tianchi Lu
author_sort	Tianchi Lu
title	K-Nearest Robust Active Learning on Big Data and Application in Epitope Prediction
title_short	K-Nearest Robust Active Learning on Big Data and Application in Epitope Prediction
title_full	K-Nearest Robust Active Learning on Big Data and Application in Epitope Prediction
title_fullStr	K-Nearest Robust Active Learning on Big Data and Application in Epitope Prediction
title_full_unstemmed	K-Nearest Robust Active Learning on Big Data and Application in Epitope Prediction
title_sort	k-nearest robust active learning on big data and application in epitope prediction
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/2f7a70c9964b49f9990857e056106750
work_keys_str_mv	AT tianchilu knearestrobustactivelearningonbigdataandapplicationinepitopeprediction
_version_	1718418331563720704

K-Nearest Robust Active Learning on Big Data and Application in Epitope Prediction

Ejemplares similares