PredAPP: Predicting Anti-Parasitic Peptides with Undersampling and Ensemble Approaches.

Zhang, Wei; Xia, Enhua; Dai, Ruyu; Tang, Wending; Bin, Yannan; Xia, Junfeng

Zhang, Wei; Xia, Enhua; Dai, Ruyu; Tang, Wending; Bin, Yannan; Xia, Junfeng.

Afiliação

Zhang W; Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China.
Xia E; State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, 230036, Anhui, China.
Dai R; State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, 230036, Anhui, China.
Tang W; Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China.
Bin Y; Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China.
Xia J; Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China. ynbin@ahu.edu.cn.

Interdiscip Sci ; 14(1): 258-268, 2022 Mar.

Article em En | MEDLINE | ID: mdl-34608613

Assuntos

Aprendizado de Máquina; Peptídeos; Algoritmos; Computadores; Modelos Logísticos

Palavras-chave

Anti-parasitic peptide; Feature representation learning; Logistic regression; Undersampling method

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Peptídeos / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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