Multivariate Information Fusion for Identifying Antifungal Peptides with
Hilbert-Schmidt Independence Criterion

Zhou, Haohao; Wang, Hao; Ding, Yongsheng; Tang, Jijun

doi:10.2174/1574893616666210727161003

Cited by 11 publications

(5 citation statements)

References 49 publications

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“…At present, k-fold cross-validation and jackknife crossvalidation are widely used for prediction evaluation (Tabaie et al, 2021;Dao et al, 2022a;Xiao et al, 2022;Zhou et al, 2022). The jackknife test is a type of cross-validation that involves leaving one observation out of the dataset at a time and using the remaining observations to train a model.…”

Section: Evaluation Methodsmentioning

confidence: 99%

Recognition of outer membrane proteins using multiple feature fusion

Su¹,

Qian²,

Yang³

et al. 2023

Front. Genet.

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Introduction: Outer membrane proteins are crucial in maintaining the structural stability and permeability of the outer membrane. Outer membrane proteins exhibit several functions such as antigenicity and strong immunogenicity, which have potential applications in clinical diagnosis and disease prevention. However, wet experiments for studying OMPs are time and capital-intensive, thereby necessitating the use of computational methods for their identification.Methods: In this study, we developed a computational model to predict outer membrane proteins. The non-redundant dataset consists of a positive set of 208 outer membrane proteins and a negative set of 876 non-outer membrane proteins. In this study, we employed the pseudo amino acid composition method to extract feature vectors and subsequently utilized the support vector machine for prediction.Results and Discussion: In the Jackknife cross-validation, the overall accuracy and the area under receiver operating characteristic curve were observed to be 93.19% and 0.966, respectively. These results demonstrate that our model can produce accurate predictions, and could serve as a valuable guide for experimental research on outer membrane proteins.

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Section: Evaluation Methodsmentioning

confidence: 99%

Recognition of outer membrane proteins using multiple feature fusion

Su¹,

Qian²,

Yang³

et al. 2023

Front. Genet.

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“…Subsequently, these basic learners are combined and utilized in another machine learning method for prediction. − Common combination methods include majority voting for classification problems and weighted averaging for regression problems. However, we adopted an innovative approach that utilized support vector machines (SVMs) , to automatically learn the voting rules from the data to handle our prediction problem. − …”

Section: Introductionmentioning

confidence: 99%

“…However, we adopted an innovative approach that utilized support vector machines (SVMs) 23 , 24 to automatically learn the voting rules from the data to handle our prediction problem. 25 − 28 …”

Section: Introductionmentioning

confidence: 99%

MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier

Zhang,

Liu,

et al. 2024

ACS Omega

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In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to metal ions and the type of metal ion-binding protein, this study proposed a classifier named MIBPred. The classifier incorporated advanced Word2Vec technology from the field of natural language processing to extract semantic features of the protein sequence language and combined them with positionspecific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for the metal ion-binding protein classification task. In the model, we independently trained XGBoost, LightGBM, and CatBoost algorithms and integrated the output results through an SVM voting mechanism. This innovative combination has led to a significant breakthrough in the predictive performance of our model. As a result, we achieved accuracies of 95.13% and 85.19%, respectively, in predicting metal ion-binding proteins and their types. Our research not only confirms the effectiveness of Word2Vec technology in extracting semantic information from protein sequences but also highlights the outstanding performance of the MIBPred classifier in the problem of metal ion-binding protein types. This study provides a reliable tool and method for the in-depth exploration of the structure and function of metal ion-binding proteins.

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“…Although traditional physicochemical as well as biological experiments are desirable in terms of predictive accuracy, these methods are too cumbersome and require a great deal of human and material resources. To save time and financial costs, and to better understand the structure and function of membrane proteins, a number of calculations have been developed to efficiently discriminate between protein types ( Feng and Zhang, 2000 ; Cai et al, 2004 ; Zou et al, 2013 ; Wei et al, 2017 ; Zhou et al, 2021 ; Zou et al, 2020 ; Qian et al, 2021 ; Ding et al, 2021 ; Ding et al, 2021 ; Zou et al, 2021 ). The extant methods are in large part improvements on Chou’s algorithm ( Chou and Elrod, 1999 ).…”

Section: Introductionmentioning

confidence: 99%

Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks

Shen

et al. 2022

Front. Genet.

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Membrane proteins are an essential part of the body’s ability to maintain normal life activities. Further research into membrane proteins, which are present in all aspects of life science research, will help to advance the development of cells and drugs. The current methods for predicting proteins are usually based on machine learning, but further improvements in prediction effectiveness and accuracy are needed. In this paper, we propose a dynamic deep network architecture based on lifelong learning in order to use computers to classify membrane proteins more effectively. The model extends the application area of lifelong learning and provides new ideas for multiple classification problems in bioinformatics. To demonstrate the performance of our model, we conducted experiments on top of two datasets and compared them with other classification methods. The results show that our model achieves high accuracy (95.3 and 93.5%) on benchmark datasets and is more effective compared to other methods.

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Multivariate Information Fusion for Identifying Antifungal Peptides with Hilbert-Schmidt Independence Criterion

Cited by 11 publications

References 49 publications

Recognition of outer membrane proteins using multiple feature fusion

Recognition of outer membrane proteins using multiple feature fusion

MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier

Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks

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