An Evolutionary Algorithm Based on Minkowski Distance for Many-Objective Optimization

Xu, Hang; Zeng, Wenhua; Zeng, Xiangxiang; Yen, Gary G.

doi:10.1109/tcyb.2018.2856208

Cited by 100 publications

(41 citation statements)

References 68 publications

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“…It might also be important to integrate evolutionary information using tools like evolutionary trees and networks (Yang et al, 2013, 2014). Finally, computational intelligence such as neural networks (Song et al, 2018a,b) and evolutionary algorithms (Hang et al, 2018) can be applied in this field.…”

Section: Resultsmentioning

confidence: 99%

Identification of Phage Viral Proteins With Hybrid Sequence Features

Wang

2019

Front. Microbiol.

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The uniqueness of bacteriophages plays an important role in bioinformatics research. In real applications, the function of the bacteriophage virion proteins is the main area of interest. Therefore, it is very important to classify bacteriophage virion proteins and non-phage virion proteins accurately. Extracting comprehensive and effective sequence features from proteins plays a vital role in protein classification. In order to more fully represent protein information, this paper is more comprehensive and effective by combining the features extracted by the feature information representation algorithm based on sequence information (CCPA) and the feature representation algorithm based on sequence and structure information. After extracting features, the Max-Relevance-Max-Distance (MRMD) algorithm is used to select the optimal feature set with the strongest correlation between class labels and low redundancy between features. Given the randomness of the samples selected by the random forest classification algorithm and the randomness features for producing each node variable, a random forest method is employed to perform 10-fold cross-validation on the bacteriophage protein classification. The accuracy of this model is as high as 93.5% in the classification of phage proteins in this study. This study also found that, among the eight physicochemical properties considered, the charge property has the greatest impact on the classification of bacteriophage proteins These results indicate that the model discussed in this paper is an important tool in bacteriophage protein research.

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Section: Resultsmentioning

confidence: 99%

Identification of Phage Viral Proteins With Hybrid Sequence Features

Wang

2019

Front. Microbiol.

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“…Additionally, we found that the proposed PAGI could achieve a satisfactory performance for all datasets by the MAPK signaling pathway. Although PAGI had above advantages, we believe there is still room to study pathway activity more effectively, by employing new generation machine learning [61]- [67] and computational intelligence algorithms [68]- [73].…”

Section: Discussionmentioning

confidence: 99%

Classification of Cancers Based on a Comprehensive Pathway Activity Inferred by Genes and Their Interactions

Zhao

Kou

et al. 2020

IEEE Access

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Cancers, a group of multifactorial complex diseases, are generally caused by mutation of multiple genes or dysregulation of gene interactions. Applying machine learning methods to microarray gene expression profiles for disease classification is a popular method to predict disease state or outcome. Traditional computational methods that detect genes differentially expressed between cancer and normal samples are ineffective in independent cohorts of patients. However, current methods consider pathways as simple gene sets and include pathway topological information but ignore significant individual genes and interactions between genes, which are essential to infer a more robust pathway activity. In this study, we proposed a novel approach to describe the activity of a pathway that incorporates both the differential expression degree of genes between the case and control and the interaction strength between genes. We applied the method to the classifications of seven cancers. Within-dataset experiments and cross-dataset experiments demonstrated that our novel method achieved robust and superior performance when compared to the five existing methods.

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“…The use of a single Matplotlib coordinate diagram is simple to operate and has many limitations; thus, it cannot reach high accuracy. In the later stage, more comprehensive computational intelligence method such as neural networks (Song et al, 2018a ; Zhou et al, 2018 ; Bao et al, 2019 ; Hong et al, 2019 ; Sun et al, 2020 ), network methods (Sun et al, 2014 ; Zhou et al, 2015 , 2016 ; Song et al, 2018b ; Zeng et al, 2018 ) and evolutionary strategies (Xu et al, 2019a , b ; Zeng et al, 2019b ) can be adopted to take the extracted protein features as input. Thus, the positive and negative samples can be divided more accurately, and accuracy can be obtained.…”

Section: Discussionmentioning

confidence: 99%

Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods

Chen

Wang

2020

Front. Bioeng. Biotechnol.

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The G Protein-Coupled Receptor (GPCR) family consists of more than 800 different members. In this article, we attempt to use the physicochemical properties of Composition, Transition, Distribution (CTD) to represent GPCRs. The dimensionality reduction method of MRMD2.0 filters the physicochemical properties of GPCR redundancy. Matplotlib plots the coordinates to distinguish GPCRs from other protein sequences. The chart data show a clear distinction effect, and there is a well-defined boundary between the two. The experimental results show that our method can predict GPCRs.

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An Evolutionary Algorithm Based on Minkowski Distance for Many-Objective Optimization

Cited by 100 publications

References 68 publications

Identification of Phage Viral Proteins With Hybrid Sequence Features

Identification of Phage Viral Proteins With Hybrid Sequence Features

Classification of Cancers Based on a Comprehensive Pathway Activity Inferred by Genes and Their Interactions

Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods

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