Using Weighted Extreme Learning Machine Combined With Scale-Invariant Feature Transform to Predict Protein-Protein Interactions From Protein Evolutionary Information

Li, Jianqiang; Shi, Xiaofeng; You, Zhu‐Hong; Yi, Hai-Cheng; Chen, Zhuangzhuang; Lin, Qiuzhen; Fang, Min

doi:10.1109/tcbb.2020.2965919

Cited by 20 publications

(12 citation statements)

References 58 publications

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“…We compared MMI type 1 to MMI type 2 and also MMI type 3 to MMI type 4to determine the best equation to represent a particular element in a sequence of amino acids. By comparing the results, we found that (7) yields better results than (6). The average accuracy and F1 score of MMI types 2 and 4 were higher than those of MMI types 1 and 3, respectively.…”

Section: Defining Of the 𝒇(𝒂) Function In The MMImentioning

confidence: 89%

“…Until now, various types of coding techniques and machine learning-based computing methods for determining whether or not proteins interact have been widely developed. According to the previous research there are some methods that using information of evolution [6], using natural language processing [7], and using clustering methods [8], [9] to learn protein-protein interaction. The most popular method is predicting the interaction between two proteins based on the sequences of amino acids.…”

Section: Introductionmentioning

confidence: 99%

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Performance of multivariate mutual information and autocorrelation encoding methods for the prediction of protein-protein interactions

Bustamam

Sunggawa

Siswantining

2022

IJ-AI

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<span lang="EN-US">Protein interactions play an essential role in the study of how an organism can be infected with a disease and also its effects. One of the challenges in computational methods in the prediction of protein-protein interactions is how to represent a sequence of amino acids in a vector so that it can be used in machine learning to create a model that can predict whether or not an interaction occurs in a protein pair. This paper examined the qualitative feature encoding methods of amino acid sequence, namely, multivariate mutual information (MMI), and the quantitative feature encoding methods, namely, autocorrelation. We develop the new design for MMI and autocorrelation feature encoding methods which give better results than the previous research. There are four ways to build the MMI method and six ways to build the autocorrelation method that we tested. We also built four types of MMI-autocorrelation (mixed) method and look for the best form of each type of MMI, autocorrelation, and mixed-method. We combine these feature encoding methods with support vector machine (SVM) as machine learning methods. We also test the encoding methods we propose to several machine learning classifier methods, such as random forest (RF), k-nearest neighbor (KNN), and gradient boosting.</span>

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Section: Defining Of the 𝒇(𝒂) Function In The MMImentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Performance of multivariate mutual information and autocorrelation encoding methods for the prediction of protein-protein interactions

Bustamam

Sunggawa

Siswantining

2022

IJ-AI

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“…where x is an element in R n and b is an element in R. Some studies state that SVM is implemented successfully in regression and classi cation [52, 53,[57][58][59].…”

Section: Svm-ann Methodologymentioning

confidence: 99%

Intrusion Detection Systems in Cloud Computing Paradigm: Analysis and Overview

et al. 2022

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Cloud computing paradigm is growing rapidly, and it allows users to get services via the Internet as pay-per-use and it is convenient for developing, deploying, and accessing mobile applications. Currently, security is a requisite concern owning to the open and distributed nature of the cloud. Copious amounts of data are responsible for alluring hackers. Thus, developing efficacious IDS is an imperative task. This article analyzed four intrusion detection systems for the detection of attacks. Two standard benchmark datasets, namely, NSL-KDD and UNSW-NB15, were used for the simulations. Additionally, this study highlights the proliferating challenges for the security of sensitive user data and gives useful recommendations to address the identified issues. Finally, the projected results show that the hybridization method with support vector machine classifier outperforms the existing techniques in the case of the datasets investigated.

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“…In order to solve the problem that ELM is sensitive to the input weights and biases, Li et al [17] proposed a WOA-ELM algorithm which applied the whale optimization algorithm (WOA) to optimize the input weights and biases of ELM for its performance improvement. In response to the class imbalance problem, weighted extreme learning machine (WELM) [18][19][20] was proposed, in which di erent weights are assigned for each training sample based on two di erent strategies. SMOTE based on class-speci c extreme learning machine (SMOTE-CSELM) [21] was also presented by exploiting the bene t of both the minority oversampling and the class-speci c regularization.…”

Section: Introductionmentioning

confidence: 99%

TSTELM: Two-Stage Transfer Extreme Learning Machine for Unsupervised Domain Adaptation

Zang

et al. 2022

Computational Intelligence and Neuroscience

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As a single-layer feedforward network (SLFN), extreme learning machine (ELM) has been successfully applied for classification and regression in machine learning due to its faster training speed and better generalization. However, it will perform poorly for domain adaptation in which the distributions between training data and testing data are inconsistent. In this article, we propose a novel ELM called two-stage transfer extreme learning machine (TSTELM) to solve this problem. At the statistical matching stage, we adopt maximum mean discrepancy (MMD) to narrow the distribution difference of the output layer between domains. In addition, at the subspace alignment stage, we align the source and target model parameters, design target cross-domain mean approximation, and add the output weight approximation to further promote the knowledge transferring across domains. Moreover, the prediction of test sample is jointly determined by the ELM parameters generated at the two stages. Finally, we investigate the proposed approach in classification task and conduct experiments on four public domain adaptation datasets. The result indicates that TSTELM could effectively enhance the knowledge transfer ability of ELM with higher accuracy than other existing transfer and non-transfer classifiers.

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Using Weighted Extreme Learning Machine Combined With Scale-Invariant Feature Transform to Predict Protein-Protein Interactions From Protein Evolutionary Information

Cited by 20 publications

References 58 publications

Performance of multivariate mutual information and autocorrelation encoding methods for the prediction of protein-protein interactions

Performance of multivariate mutual information and autocorrelation encoding methods for the prediction of protein-protein interactions

Intrusion Detection Systems in Cloud Computing Paradigm: Analysis and Overview

TSTELM: Two-Stage Transfer Extreme Learning Machine for Unsupervised Domain Adaptation

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