PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

Charoenkwan, Phasit; Kanthawong, Sakawrat; Schaduangrat, Nalini; Yana, Janchai; Shoombuatong, Watshara

doi:10.3390/cells9020353

Cited by 53 publications

(75 citation statements)

References 79 publications

(243 reference statements)

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“…Phase 2: Calculating the initial propensity score of 400 dipeptides on the first 15 residues from the C terminus (init-C15PS). According to Charoenkwan et al [34][35][36][37] , the init-C15PS is estimated, as follows:…”

Section: Protein Feature Representationmentioning

confidence: 99%

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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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Section: Protein Feature Representationmentioning

confidence: 99%

“…where values of W 1 and W 2 are 0.9 and 0.1, respectively. Furthermore, weights for W 1 and W 2 were set based on our previous studies 27, [34][35][36][37] .…”

Section: Protein Feature Representationmentioning

confidence: 99%

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

Charoenkwan

Chiangjong

Lee

et al. 2021

Sci Rep

Self Cite

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“…Thirdly, a robust feature selection algorithm is required to rank and select the best discrimination feature subset for the model prediction. Moreover, they are not generalized or transferable to researchers with informatics background who can develop in-house prediction models [ 8 , 9 ]. Motivated by the above mentioned limitations, Meta-iPVP were proposed which was employed the eficient feature representation approach to generate discriminative probabilistic features using SVM algorithm [ 9 ].…”

Section: Bacteriophage Virion Proteins Predictionmentioning

confidence: 99%

Application of machine learning in bacteriophage research

2021

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Phages are one of the key components in the structure, dynamics, and interactions of microbial communities in different bins. It has a clear impact on human health and the food industry. Bacteriophage characterization using in vitro approaches are time/cost consuming and laborious tasks. On the other hand, with the advent of new high-throughput sequencing technology, the development of a powerful computational framework to characterize the newly identified bacteriophages is inevitable for future research. Machine learning includes powerful techniques that enable the analysis of complex datasets for knowledge discovery and pattern recognition. In this study, we have conducted a comprehensive review of machine learning methods application using different types of features were applied in various aspects of bacteriophage research including, automated curation, identification, classification, host species recognition, virion protein identification, and life cycle prediction. Moreover, potential limitations and advantages of the developed frameworks were discussed.

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“…We carried out 10-fold CV test to assess model performance. In 10-fold CV, the benchmark dataset was randomly separated into 10 subgroups with roughly equal size [54][55][56][57][58][59][60][61][62][63][64], with each subgroup containing the same number of m6A and non-m6A samples [65,66]. One of the subgroups was considered as the validation set to assess the trained model and the remaining subgroups were used to train the model.…”

Section: Cross-validationmentioning

confidence: 99%

Extremely-randomized-tree-based Prediction of N6-methyladenosine Sites inSaccharomyces cerevisiae

Govindaraj¹,

Subramaniyam

Manavalan

2020

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Introduction: N6-methyladenosine (m6A) is one of the most common post-transcriptional modifications in RNA, which has been related to several biological processes. The accurate prediction of m6A sites from RNA sequences is one of the challenging tasks in computational biology. Several computational methods utilizing machine-learning algorithms have been proposed that accelerate in silico screening of m6A sites, thereby drastically reducing the experimental time and labor costs involved. Methodology: In this study, we proposed a novel computational predictor termed ERT-m6Apred, for the accurate prediction of m6A sites. To identify the feature encodings with more discriminative capability, we applied a two-step feature selection technique on seven different feature encodings and identified the corresponding optimal feature set. Results: Subsequently, performance comparison of the corresponding optimal feature set-based extremely randomized tree model revealed that Pseudo k-tuple composition encoding, which includes 14 physicochemical properties significantly outperformed other encodings. Moreover, ERT-m6Apred achieved an accuracy of 78.84% during cross-validation analysis, which is comparatively better than recently reported predictors. Conclusion: In summary, ERT-m6Apred predicts Saccharomyces cerevisiae m6A sites with higher accuracy, thus facilitating biological hypothesis generation and experimental validations.

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PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

Cited by 53 publications

References 79 publications

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

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

Application of machine learning in bacteriophage research

Extremely-randomized-tree-based Prediction of N6-methyladenosine Sites inSaccharomyces cerevisiae

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