Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model

Cancer is still a severe health problem globally. The therapy of cancer traditionally involves the use of radiotherapy or anticancer drugs to kill cancer cells, but these methods are quite expensive and have side effects, which will cause great harm to patients. With the find of anticancer peptides...
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Autor*in:	Qingwen Li [verfasserIn] Wenyang Zhou [verfasserIn] Donghua Wang [verfasserIn] Sui Wang [verfasserIn] Qingyuan Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	anticancer peptide feature extraction feature model feature selection machine learning

Übergeordnetes Werk:	In: Frontiers in Bioengineering and Biotechnology - Frontiers Media S.A., 2014, 8(2020)
Übergeordnetes Werk:	volume:8 ; year:2020

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fbioe.2020.00892

Katalog-ID:	DOAJ06556801X

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language	English
source	In Frontiers in Bioengineering and Biotechnology 8(2020) volume:8 year:2020
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container_title	Frontiers in Bioengineering and Biotechnology
authorswithroles_txt_mv	Qingwen Li @@aut@@ Wenyang Zhou @@aut@@ Donghua Wang @@aut@@ Sui Wang @@aut@@ Qingyuan Li @@aut@@
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callnumber-first	T - Technology
author	Qingwen Li
spellingShingle	Qingwen Li misc TP248.13-248.65 misc anticancer peptide misc feature extraction misc feature model misc feature selection misc machine learning misc Biotechnology Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model
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topic_title	TP248.13-248.65 Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model anticancer peptide feature extraction feature model feature selection machine learning
topic	misc TP248.13-248.65 misc anticancer peptide misc feature extraction misc feature model misc feature selection misc machine learning misc Biotechnology
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title	Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model
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title_auth	Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model
abstract	Cancer is still a severe health problem globally. The therapy of cancer traditionally involves the use of radiotherapy or anticancer drugs to kill cancer cells, but these methods are quite expensive and have side effects, which will cause great harm to patients. With the find of anticancer peptides (ACPs), significant progress has been achieved in the therapy of tumors. Therefore, it is invaluable to accurately identify anticancer peptides. Although biochemical experiments can solve this work, this method is expensive and time-consuming. To promote the application of anticancer peptides in cancer therapy, machine learning can be used to recognize anticancer peptides by extracting the feature vectors of anticancer peptides. Nevertheless, poor performance usually be found in training the machine learning model to utilizing high-dimensional features in practice. In order to solve the above job, this paper put forward a 19-dimensional feature model based on anticancer peptide sequences, which has lower dimensionality and better performance than some existing methods. In addition, this paper also separated a model with a low number of dimensions and acceptable performance. The few features identified in this study may represent the important features of anticancer peptides.
abstractGer	Cancer is still a severe health problem globally. The therapy of cancer traditionally involves the use of radiotherapy or anticancer drugs to kill cancer cells, but these methods are quite expensive and have side effects, which will cause great harm to patients. With the find of anticancer peptides (ACPs), significant progress has been achieved in the therapy of tumors. Therefore, it is invaluable to accurately identify anticancer peptides. Although biochemical experiments can solve this work, this method is expensive and time-consuming. To promote the application of anticancer peptides in cancer therapy, machine learning can be used to recognize anticancer peptides by extracting the feature vectors of anticancer peptides. Nevertheless, poor performance usually be found in training the machine learning model to utilizing high-dimensional features in practice. In order to solve the above job, this paper put forward a 19-dimensional feature model based on anticancer peptide sequences, which has lower dimensionality and better performance than some existing methods. In addition, this paper also separated a model with a low number of dimensions and acceptable performance. The few features identified in this study may represent the important features of anticancer peptides.
abstract_unstemmed	Cancer is still a severe health problem globally. The therapy of cancer traditionally involves the use of radiotherapy or anticancer drugs to kill cancer cells, but these methods are quite expensive and have side effects, which will cause great harm to patients. With the find of anticancer peptides (ACPs), significant progress has been achieved in the therapy of tumors. Therefore, it is invaluable to accurately identify anticancer peptides. Although biochemical experiments can solve this work, this method is expensive and time-consuming. To promote the application of anticancer peptides in cancer therapy, machine learning can be used to recognize anticancer peptides by extracting the feature vectors of anticancer peptides. Nevertheless, poor performance usually be found in training the machine learning model to utilizing high-dimensional features in practice. In order to solve the above job, this paper put forward a 19-dimensional feature model based on anticancer peptide sequences, which has lower dimensionality and better performance than some existing methods. In addition, this paper also separated a model with a low number of dimensions and acceptable performance. The few features identified in this study may represent the important features of anticancer peptides.
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title_short	Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model
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up_date	2024-07-03T15:26:19.892Z
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