ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides

Rao, Bing; Zhou, Chen; Zhang, Guoying; Su, Ran; Wei, Leyi

doi:10.1093/bib/bbz088

Cited by 129 publications

(80 citation statements)

References 49 publications

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“…For performance evaluation, we used five commonly used metrics including sensitivity (SE), specificity (SP), accuracy (ACC), and Matthew's correlation coefficient (MCC), and AUC (Area Under the Curve), which are widely used in several bioinformatics fields [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] ACPs predicted as non-ACPs. The SE and SP metrics measure the predictive ability of the predictor for the positives and negatives, respectively, while the other two metrics, ACC and MCC, measure the overall predictive performance.…”

Section: Evaluation Metricsmentioning

confidence: 99%

ACP-GCN: The Identification of Anticancer Peptides Based on Graph Convolution Networks

Rao

Zhang

2020

IEEE Access

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Anticancer peptide (ACP) is a class of anti-cancer peptide which can inhibit and kill tumor cells. Identification of ACPs is of great significance for the development of new anti-cancer drugs. However, most of computational methods make predictions based on machine learning using hand-crafted features. In this paper, we propose a new graph learning based computational model, named ACP-GCN, to automatically and accurately predict ACPs based on graph convolution networks. In this model, we for the first time take the ACP prediction as a graph classification task, where each peptide sample is represented as a graph. The experimental results show that the proposed model outperforms most of state-of-the-art methods. Specifically, the specificity and accuracy of our predictor have reached 91.1% and 85.6%, which are 2.8-22.8% and 3.2-15% higher than that of other existing predictors, respectively, demonstrating that the proposed method can effectively distinguish ACPs from non-ACPs. The excellent predictive ability will rapidly push forward their applications in cancer therapy. INDEX TERMS anti-cancer peptides, graph convolution networks, machine learning, prediction methods

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Section: Evaluation Metricsmentioning

confidence: 99%

ACP-GCN: The Identification of Anticancer Peptides Based on Graph Convolution Networks

Rao

Zhang

2020

IEEE Access

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“…Therefore, the recent efforts have mainly focused on the development of computational methods, especially machine learning-based methods in order to expedite the identification of ACPs. Over the last decade, various intelligent statistical-based models have been proposed in the literature to accurately identify ACPs [11][12][13][14]. Hajisharifi et al, generated a non-redundant training dataset, the Hajisharifi-Chen (HC) dataset, which contained 138 ACPs and 206 non-ACPs, whereas the biological sequences were formulated using pseudo amino acid composition (PseAAC) and local-alignment based kernel for the identification of ACPs [15].…”

Section: Introductionmentioning

confidence: 99%

cACP-2LFS: Classification of Anticancer Peptides Using Sequential Discriminative Model of KSAAP and Two-Level Feature Selection Approach

et al. 2020

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Cancer is a leading killer disease globally, it occurs when the cellular changes cause the abnormal growth and division of the cells. Conventional treatment such as therapies and wet experimental methods are deemed unsatisfactory and worthless because of its huge cost and laborious nature. However, the recent innovation of anticancer peptides (ACPs) offers an effective way to treat cancer affected cells. Due to the rapid growth of biological sequences, truly identification of ACPs has become a difficult task for scientists. Therefore, measuring the importance of ACPs, an efficient and reliable intelligent model is highly essential to accurately identify its pattern. In this study, three distinct nature encoding schemes are employed to obtain features from peptide sequences. However, K-space amino acid pair (KSAAP) is used to extract highly correlated and effective descriptors. Apart from the sequential features, composite physiochemical properties are applied to gather local structure descriptors. Furthermore, to represent the intrinsic residue information of amino acids, autocovariance is also used. Additionally, a novel two-level feature selection (2LFS) method is utilized to select high discriminative features and to minimize the dimensionality of the proposed descriptors. At last, to examine the performance of the proposed model, several learning hypotheses are investigated to select a superior operational engine. To measure the generalization capability, two diverse benchmark datasets are used. After evaluating the empirical outcomes, KSAAP using 2LFS reported high classification results on both datasets. Whereas, the classification outcomes reveal that our proposed cACP-2LFS achieved ~11% improved performance accuracy than present models in the literature so far. It is expected that our proposed model might be useful in the area of medicine, proteomics, and research academia. The source code and all datasets are publicly available at https://github.com/shahidawkum/cACP-2LFS.

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“…As summarized in a recent review 2 , we could see that TargetACP has been developed by integrating the split amino acid composition and pseudo positionspecific scoring matrix descriptors 14 , which was shown to outperform SVM-based predictors [8][9][10][11][12]19,24 . In the meanwhile, the state-of-the-art ensemble methods comprising PEPred-Suite 20 and ACPred-Fuse 18 provided the highest prediction accuracies as evaluated on the dataset collected by Rao et al 18 . In ACPred-Fuse, it was developed using random forest (RF) model in conjunction with 114 feature descriptors.…”

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confidence: 97%

“…In the past few years, most methods in existence were developed via the use of machine learning (ML) and statistical methods as applied on peptide sequence information for discriminating ACPs from non-ACPs [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] . More details of those existing methods are summarized in two comprehensive review papers 2,3 .…”

mentioning

confidence: 99%

“…AntiCP 8 , Hajisharifi et al 's method 9 , ACPP 24 , iACP 10 , Li and Wang's method 11 , iACP-GAEnsC 12 , TargetACP 14 and ACPred 19 ) and the ensemble approach (i.e. MLACP 13 , ACPred 19 , PTPD 21 , ACP-DL 22 , PEPred-Suite 20 , ACPred-FL 15 , ACPred-Fuse 18 , PPTPP 23 and AntiCP_2.0 25 ) were widely used to develop ACP predictors. As summarized in a recent review 2 , we could see that TargetACP has been developed by integrating the split amino acid composition and pseudo positionspecific scoring matrix descriptors 14 , which was shown to outperform SVM-based predictors [8][9][10][11][12]19,24 .…”

mentioning

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Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method

Charoenkwan

Chiangjong

Lee

et al. 2021

Sci Rep

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As anticancer peptides (ACPs) have attracted great interest for cancer treatment, several approaches based on machine learning have been proposed for ACP identification. Although existing methods have afforded high prediction accuracies, however such models are using a large number of descriptors together with complex ensemble approaches that consequently leads to low interpretability and thus poses a challenge for biologists and biochemists. Therefore, it is desirable to develop a simple, interpretable and efficient predictor for accurate ACP identification as well as providing the means for the rational design of new anticancer peptides with promising potential for clinical application. Herein, we propose a novel flexible scoring card method (FSCM) making use of propensity scores of local and global sequential information for the development of a sequence-based ACP predictor (named iACP-FSCM) for improving the prediction accuracy and model interpretability. To the best of our knowledge, iACP-FSCM represents the first sequence-based ACP predictor for rationalizing an in-depth understanding into the molecular basis for the enhancement of anticancer activities of peptides via the use of FSCM-derived propensity scores. The independent testing results showed that the iACP-FSCM provided accuracies of 0.825 and 0.910 as evaluated on the main and alternative datasets, respectively. Results from comparative benchmarking demonstrated that iACP-FSCM could outperform seven other existing ACP predictors with marked improvements of 7% and 17% for accuracy and MCC, respectively, on the main dataset. Furthermore, the iACP-FSCM (0.910) achieved very comparable results to that of the state-of-the-art ensemble model AntiCP2.0 (0.920) as evaluated on the alternative dataset. Comparative results demonstrated that iACP-FSCM was the most suitable choice for ACP identification and characterization considering its simplicity, interpretability and generalizability. It is highly anticipated that the iACP-FSCM may be a robust tool for the rapid screening and identification of promising ACPs for clinical use.

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ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides

Cited by 129 publications

References 49 publications

ACP-GCN: The Identification of Anticancer Peptides Based on Graph Convolution Networks

ACP-GCN: The Identification of Anticancer Peptides Based on Graph Convolution Networks

cACP-2LFS: Classification of Anticancer Peptides Using Sequential Discriminative Model of KSAAP and Two-Level Feature Selection Approach

Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method

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