Using a low correlation high orthogonality feature set and machine learning methods to identify plant pentatricopeptide repeat coding gene/protein

Feng, Changli; Zou, Quan; Wang, Donghua

doi:10.1016/j.neucom.2020.02.079

Cited by 3 publications

(3 citation statements)

References 37 publications

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“…Hence, these features had to be removed before analyzing the importance of the others. Furthermore, another test also had to be performed first by comparing the precisions under different thresholds, in order to determine the optimum threshold for removing the redundant features [87]. The results are shown in Figure 4D, from which it can be observed that the amplitudes of the four matrices begin to flatten when the threshold is larger than 0.7.…”

Section: Resultsmentioning

confidence: 99%

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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Section: Resultsmentioning

confidence: 99%

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

et al. 2021

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“…Maximum-Relevance-Maximum-Distance in [32,33] and Analysis of Variance (ANOVA) in [34] are typical feature selection approaches. For optimum feature representation, [35][36][37] used the principal component analysis (PCA) and misclassifcation error (MCE) to extract optimal feature representation for pentatricopeptide-repeat proteins prediction and got 97.9% accuracy. Li et al in [33] used the above method to design a model for the prediction of anticancer peptide sequences with 19-dimensional attributes.…”

Section: Introductionmentioning

confidence: 99%

IGPred‐HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning‐Based Approach

Ali

Alturise

Alkhalifah

et al. 2023

Computational Intelligence and Neuroscience

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Motivation. Immunoglobulin proteins (IGP) (also called antibodies) are glycoproteins that act as B-cell receptors against external or internal antigens like viruses and bacteria. IGPs play a significant role in diverse cellular processes ranging from adhesion to cell recognition. IGP identifications via the in-silico approach are faster and more cost-effective than wet-lab technological methods. Methods. In this study, we developed an intelligent theoretical deep learning framework, “IGPred-HDnet” for the discrimination of IGPs and non-IGPs. Three types of promising descriptors are feature extraction based on graphical and statistical features (FEGS), amphiphilic pseudo-amino acid composition (Amp-PseAAC), and dipeptide composition (DPC) to extract the graphical, physicochemical, and sequential features. Next, the extracted attributes are evaluated through machine learning, i.e., decision tree (DT), support vector machine (SVM), k-nearest neighbour (KNN), and hierarchical deep network (HDnet) classifiers. The proposed predictor IGPred-HDnet was trained and tested using a 10-fold cross-validation and independent test. Results and Conclusion. The success rates in terms of accuracy (ACC) and Matthew’s correlation coefficient (MCC) of IGPred-HDnet on training and independent dataset (Dtrain Dtest) are ACC = 98.00%, 99.10%, and MCC = 0.958, and 0.980 points, respectively. The empirical outcomes demonstrate that the IGPred-HDnet model efficacy on both datasets using the novel FEGS feature and HDnet algorithm achieved superior predictions to other existing computational models. We hope this research will provide great insights into the large-scale identification of IGPs and pharmaceutical companies in new drug design.

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“…MRMD ( Zou et al, 2016 ; Ao et al, 2020 ; Li et al, 2020a ; Li et al, 2020b ; Meng et al, 2020 ) and ANOVA ( Anderson, 2001 ; Lv et al, 2019 ) are standard feature selection methods. For optimal feature identification, ( Feng et al, 2021 ) uses the PCA and MCE methods to make the features orthogonal and obtain the core feature set with the minimum 10-dimensional attributes for PPR gene identification and realized 97.9% accuracy. ( Li et al, 2020b ) used a 19-dimensional feature model to classify anticancer peptide sequences.…”

Section: Introductionmentioning

confidence: 99%

Immunoglobulin Classification Based on FC* and GC* Features

et al. 2022

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Immunoglobulins have a pivotal role in disease regulation. Therefore, it is vital to accurately identify immunoglobulins to develop new drugs and research related diseases. Compared with utilizing high-dimension features to identify immunoglobulins, this research aimed to examine a method to classify immunoglobulins and non-immunoglobulins using two features, FC* and GC*. Classification of 228 samples (109 immunoglobulin samples and 119 non-immunoglobulin samples) revealed that the overall accuracy was 80.7% in 10-fold cross-validation using the J48 classifier implemented in Weka software. The FC* feature identified in this study was found in the immunoglobulin subtype domain, which demonstrated that this extracted feature could represent functional and structural properties of immunoglobulins for forecasting.

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Using a low correlation high orthogonality feature set and machine learning methods to identify plant pentatricopeptide repeat coding gene/protein

Cited by 3 publications

References 37 publications

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

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

IGPred‐HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning‐Based Approach

Immunoglobulin Classification Based on FC* and GC* Features

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