Prediction of antioxidant proteins by incorporating statistical moments based features into Chou's PseAAC

Butt, Ahmad Hassan; Rasool, Nouman; Khan, Yaser Daanial

doi:10.1016/j.jtbi.2019.04.019

Cited by 52 publications

(22 citation statements)

References 30 publications

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“…To enhance the prediction model in our research, and also compare with previous studies, we exploited 2 different validation datasets: (1) the package 1 was prepared as same as the dataset constructed in [ 5 ] with only 120 positive samples; the package 2 included 74 positive samples and 392 negative samples following Zhang et al [ 19 ]. Finally, we also manually collected a novel set of antioxidant proteins from the UniProt (TrEMBL) database to serve as an external set for validating the potential of our models on newly discovered proteins.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, to show the efficiency of the applied model in this study, we compared our antioxidant proteins predictor with the results of two similar studies from Butt et al [ 5 ] and Zhang et al [ 19 ]. These studies are similar topics with us in building an antioxidant protein predictor.…”

Section: Methodsmentioning

confidence: 99%

“…The concentration of free radicals can rapidly increase due to external factors such as X-rays, cigarette smoke, industrial chemicals, food preservatives, or environmental pollution [ 5 ]. The extension of free radical concentration in the human body can create an imbalance between antioxidants and free radicals (this phenomenon is called oxidative stress) [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the enormous amount of antioxidant data, as well as the wide range of common databases for protein properties [ 5 ], have prompted us to develop a new machine learning model based on a variety of sequencing properties of proteins to produce an aid-tool that has a higher rate of accurate prediction. In this study, we established a prediction model based on the same benchmarking (training) dataset with Butt et al [ 5 ] to compare the efficiency of applying more features to the prediction model including 249 positive and 1531 negative antioxidant protein sequences. With the benchmarking dataset, we obtained nine feature sets with an accuracy higher than 70% throughout screening 17 feature sets by the Random Forest classifier.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Model for Identifying Antioxidant Proteins Using Features Calculated from Primary Sequences

Lam

Туан

et al. 2020

Biology

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Antioxidant proteins are involved importantly in many aspects of cellular life activities. They protect the cell and DNA from oxidative substances (such as peroxide, nitric oxide, oxygen-free radicals, etc.) which are known as reactive oxygen species (ROS). Free radical generation and antioxidant defenses are opposing factors in the human body and the balance between them is necessary to maintain a healthy body. An unhealthy routine or the degeneration of age can break the balance, leading to more ROS than antioxidants, causing damage to health. In general, the antioxidant mechanism is the combination of antioxidant molecules and ROS in a one-electron reaction. Creating computational models to promptly identify antioxidant candidates is essential in supporting antioxidant detection experiments in the laboratory. In this study, we proposed a machine learning-based model for this prediction purpose from a benchmark set of sequencing data. The experiments were conducted by using 10-fold cross-validation on the training process and validated by three different independent datasets. Different machine learning and deep learning algorithms have been evaluated on an optimal set of sequence features. Among them, Random Forest has been identified as the best model to identify antioxidant proteins with the highest performance. Our optimal model achieved high accuracy of 84.6%, as well as a balance in sensitivity (81.5%) and specificity (85.1%) for antioxidant protein identification on the training dataset. The performance results from different independent datasets also showed the significance in our model compared to previously published works on antioxidant protein identification.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning Model for Identifying Antioxidant Proteins Using Features Calculated from Primary Sequences

Lam

Туан

et al. 2020

Biology

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“…Several properties of data are described by different orders of moments. Some moments are used to reveal eccentricity and orientation of data while some are used to estimate the data size [45]- [48]. Several moments have been formed by various mathematicians and statisticians based on famous distribution functions and polynomials.…”

Section: Statistical Momentsmentioning

confidence: 99%

EnhancerP-2L: A Gene regulatory site identification tool for DNA enhancer region using CREs motifs

Ah¹,

Alkhalaf

Iqbal

et al. 2020

Preprint

Self Cite

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Enhancers are DNA fragments that do not encode RNA molecules and proteins, but they act critically in the production of RNAs and proteins by controlling gene expression. Prediction of enhancers and their strength plays significant role in regulating gene expression. Prediction of enhancer regions, in sequences of DNA, is considered a difficult task due to the fact that they are not close to the target gene, have less common motifs and are mostly tissue/cell specific. In recent past, several bioinformatics tools were developed to discriminate enhancers from other regulatory elements and to identify their strengths as well.However the need for improvement in the quality of its prediction method requires enhancements in its application value practically. In this study, we proposed a new method that builds on nucleotide composition and statistical moment based features to distinguish between enhancers and non-enhancers and additionally determine their strength. Our proposed method achieved accuracy better than current state-of-the-art methods using 5-fold and 10-fold cross-validation. The outcomes from our proposed method suggest that the use of statistical moments based features could bear more efficient and effective results. For the accessibility of the scientific community, we have developed a user-friendly web server for EnhancerP-2L which will increase the impact of bioinformatics on medicinal chemistry and drive medical science into an unprecedented resolution. Web server is freely accessible at http://www.biopred.org/enpred.

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ORS‐Pred: An optimized reduced scheme‐based identifier for antioxidant proteins

et al. 2021

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Antioxidant proteins can terminate a chain of reactions caused by free radicals and protect cells from damage. To identify antioxidant proteins rapidly, a computational model was proposed based on the optimized recoding scheme, sequence information and machine learning methods. First, over 600 recoding schemes were collected to build a scheme set. Then, the original sequence was recoded as a reduced expression whose g‐gap dipeptides (g = 0, 1, 2) were used as the features of proteins. Furthermore, a random forest method was used to evaluate the classification ability of the obtained dipeptide features. After going through all schemes, the best predictive performance scheme was chosen as the optimized reduction scheme. Finally, for the RF method, a grid search strategy was used to select a better parameter combination to identify antioxidant proteins. In the experiment, the present method correctly recognized 90.13–99.87% of the antioxidant samples. Other experimental results also proved that the present method was efficient to identify antioxidant proteins. Finally, we also developed a web server that was freely accessible to researchers.

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Prediction of antioxidant proteins by incorporating statistical moments based features into Chou's PseAAC

Cited by 52 publications

References 30 publications

Machine Learning Model for Identifying Antioxidant Proteins Using Features Calculated from Primary Sequences

Machine Learning Model for Identifying Antioxidant Proteins Using Features Calculated from Primary Sequences

EnhancerP-2L: A Gene regulatory site identification tool for DNA enhancer region using CREs motifs

ORS‐Pred: An optimized reduced scheme‐based identifier for antioxidant proteins

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