AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides

Structural and functional properties of food protein-derived antioxidant peptides

(2019) Ifeanyi D. Nwachukwu et al.   JOURNAL OF FOOD BIOCHEMISTRY

DeepFam: deep learning based alignment-free method for protein family modeling and prediction

(2018) Seokjun Seo et al.   BIOINFORMATICS

SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins

(2018) Lei Xu et al.   INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

Bioactive peptides as natural antioxidants in food products – A review

(2018) Jose M. Lorenzo et al.   TRENDS IN FOOD SCIENCE & TECHNOLOGY

An overview of multi-task learning

(2017) Yu Zhang et al.   National Science Review

Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric

(2017) Sabri Boughorbel et al.   PLoS One

Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks

(2016) David R. Kelley et al.   GENOME RESEARCH

The Structure-Activity Relationship of the Antioxidant Peptides from Natural Proteins

(2016) Tang-Bin Zou et al.   MOLECULES

UniProt: the universal protein knowledgebase

(2016)   NUCLEIC ACIDS RESEARCH

Antioxidant peptides from marine by-products: Isolation, identification and application in food systems. A review

(2016) Assaad Sila et al.   Journal of Functional Foods

Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy

(2016) Lina Zhang et al.   PLoS One

Structure-activity relationship of a series of antioxidant tripeptides derived from β-Lactoglobulin using QSAR modeling

(2015) Mai Tian et al.   Dairy Science & Technology

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

(2015) Babak Alipanahi et al.   NATURE BIOTECHNOLOGY

Predicting effects of noncoding variants with deep learning–based sequence model

(2015) Jian Zhou et al.   NATURE METHODS

Identifying Antioxidant Proteins by Using Optimal Dipeptide Compositions

(2015) Pengmian Feng et al.   Interdisciplinary Sciences-Computational Life Sciences

Antioxidants and human diseases

(2014) Peramaiyan Rajendran et al.   CLINICA CHIMICA ACTA

DFT-based quantitative structure–activity relationship studies for antioxidant peptides

(2014) Yunhui Cheng et al.   STRUCTURAL CHEMISTRY

Cross-validation pitfalls when selecting and assessing regression and classification models

(2014) Damjan Krstajic et al.   Journal of Cheminformatics

Prediction of the Radical Scavenging Activities of Some Antioxidant from Their Molecular Structure

(2013) Mohammad H. Fatemi et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

An antioxidant regenerating system for continuous quenching of free radicals in chronic wounds

(2012) Gibson S. Nyanhongo et al.   EUROPEAN JOURNAL OF PHARMACEUTICS AND BIOPHARMACEUTICS

Characterization of structure–antioxidant activity relationship of peptides in free radical systems using QSAR models: Key sequence positions and their amino acid properties

(2012) Yao-Wang Li et al.   JOURNAL OF THEORETICAL BIOLOGY

Revisiting the negative example sampling problem for predicting protein-protein interactions

(2011) Y. Park et al.   BIOINFORMATICS

Purification and biochemical characterization of antioxidant peptide from horse mackerel (Magalaspis cordyla) viscera protein

(2011) N.S. Sampath Kumar et al.   PEPTIDES

Some remarks on protein attribute prediction and pseudo amino acid composition

(2010) Kuo-Chen Chou   JOURNAL OF THEORETICAL BIOLOGY

Antioxidative peptides from food proteins: A review

(2010) Bahareh H. Sarmadi et al.   PEPTIDES

Upcoming challenges for multiple sequence alignment methods in the high-throughput era

(2009) Carsten Kemena et al.   BIOINFORMATICS

Antioxidative properties of protein hydrolysate from defatted peanut kernels treated with esperase

(2009) Jean-Yu Hwang et al.   LWT-FOOD SCIENCE AND TECHNOLOGY

In vitro antioxidant properties of rutin

(2007) Jianxiong Yang et al.   LWT-FOOD SCIENCE AND TECHNOLOGY