StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features
<p>Host defense or antimicrobial peptides (AMPs) are promising candidates for protecting host against microbial pathogens for example bacteria, virus, fungi, yeast. Defensins are the type of AMPs that act as potential therapeutic drug agent and perform vital role in various biological process....
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2024
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| الموضوعات: | |
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| _version_ | 1864513555956498432 |
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| author | Muhammad Arif (769250) |
| author2 | Saleh Musleh (15279190) Ali Ghulam (18321907) Huma Fida (17730455) Yasser Alqahtani (19760166) Tanvir Alam (638619) |
| author2_role | author author author author author |
| author_facet | Muhammad Arif (769250) Saleh Musleh (15279190) Ali Ghulam (18321907) Huma Fida (17730455) Yasser Alqahtani (19760166) Tanvir Alam (638619) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Arif (769250) Saleh Musleh (15279190) Ali Ghulam (18321907) Huma Fida (17730455) Yasser Alqahtani (19760166) Tanvir Alam (638619) |
| dc.date.none.fl_str_mv | 2024-08-23T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.ymeth.2024.08.001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/StackDPPred_Multiclass_prediction_of_defensin_peptides_using_stacked_ensemble_learning_with_optimized_features/27134397 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Bioinformatics and computational biology Information and computing sciences Machine learning Antimicrobial peptides (AMPs) Defensins Host defense Therapeutic agents Defensin peptides (DPs) Computational methods Machine learning |
| dc.title.none.fl_str_mv | StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Host defense or antimicrobial peptides (AMPs) are promising candidates for protecting host against microbial pathogens for example bacteria, virus, fungi, yeast. Defensins are the type of AMPs that act as potential therapeutic drug agent and perform vital role in various biological process. Conventional Experiments to identify defensin peptides (DPs) are time consuming and expensive. Thus, the shortcomings of wet lab experiments are leveraged by computational methods to accurately predict the functional types of DPs. In this paper, we aim to propose a novel multi-class ensemble-based prediction model called StackDPPred for identifying the properties of DPs. The peptide sequences are encoded using split amino acid composition (SAAC), segmented position specific scoring matrix (SegPSSM), histogram of oriented gradients-based PSSM (HOGPSSM) and feature extraction based graphical and statistical (FEGS) descriptors. Next, principal component analysis (PCA) is used to select the best subset of attributes. After that, the optimized features are fed into single machine learning and stacking-based ensemble classifiers. Furthermore, the ablation study demonstrates the robustness and efficacy of the stacking approach using reduced features for predicting DPs and their families. The proposed StackDPPred method improves the overall accuracy by 13.41% and 7.62% compared to existing DPs predictors iDPF-PseRAAC and iDEF-PseRAAC, respectively on validation test. Additionally, we applied the local interpretable model-agnostic explanations (LIME) algorithm to understand the contribution of selected features to the overall prediction. We believe, StackDPPred could serve as a valuable tool accelerating the screening of large-scale DPs and peptide-based drug discovery process.</p><h2>Other Information</h2> <p> Published in: Methods<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.ymeth.2024.08.001" target="_blank">https://dx.doi.org/10.1016/j.ymeth.2024.08.001</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_c48bcfad62f9a5ce2fe3a61425c14e43 |
| identifier_str_mv | 10.1016/j.ymeth.2024.08.001 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27134397 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized featuresMuhammad Arif (769250)Saleh Musleh (15279190)Ali Ghulam (18321907)Huma Fida (17730455)Yasser Alqahtani (19760166)Tanvir Alam (638619)Biological sciencesBioinformatics and computational biologyInformation and computing sciencesMachine learningAntimicrobial peptides (AMPs)DefensinsHost defenseTherapeutic agentsDefensin peptides (DPs)Computational methodsMachine learning<p>Host defense or antimicrobial peptides (AMPs) are promising candidates for protecting host against microbial pathogens for example bacteria, virus, fungi, yeast. Defensins are the type of AMPs that act as potential therapeutic drug agent and perform vital role in various biological process. Conventional Experiments to identify defensin peptides (DPs) are time consuming and expensive. Thus, the shortcomings of wet lab experiments are leveraged by computational methods to accurately predict the functional types of DPs. In this paper, we aim to propose a novel multi-class ensemble-based prediction model called StackDPPred for identifying the properties of DPs. The peptide sequences are encoded using split amino acid composition (SAAC), segmented position specific scoring matrix (SegPSSM), histogram of oriented gradients-based PSSM (HOGPSSM) and feature extraction based graphical and statistical (FEGS) descriptors. Next, principal component analysis (PCA) is used to select the best subset of attributes. After that, the optimized features are fed into single machine learning and stacking-based ensemble classifiers. Furthermore, the ablation study demonstrates the robustness and efficacy of the stacking approach using reduced features for predicting DPs and their families. The proposed StackDPPred method improves the overall accuracy by 13.41% and 7.62% compared to existing DPs predictors iDPF-PseRAAC and iDEF-PseRAAC, respectively on validation test. Additionally, we applied the local interpretable model-agnostic explanations (LIME) algorithm to understand the contribution of selected features to the overall prediction. We believe, StackDPPred could serve as a valuable tool accelerating the screening of large-scale DPs and peptide-based drug discovery process.</p><h2>Other Information</h2> <p> Published in: Methods<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.ymeth.2024.08.001" target="_blank">https://dx.doi.org/10.1016/j.ymeth.2024.08.001</a></p>2024-08-23T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.ymeth.2024.08.001https://figshare.com/articles/journal_contribution/StackDPPred_Multiclass_prediction_of_defensin_peptides_using_stacked_ensemble_learning_with_optimized_features/27134397CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/271343972024-08-23T15:00:00Z |
| spellingShingle | StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features Muhammad Arif (769250) Biological sciences Bioinformatics and computational biology Information and computing sciences Machine learning Antimicrobial peptides (AMPs) Defensins Host defense Therapeutic agents Defensin peptides (DPs) Computational methods Machine learning |
| status_str | publishedVersion |
| title | StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features |
| title_full | StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features |
| title_fullStr | StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features |
| title_full_unstemmed | StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features |
| title_short | StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features |
| title_sort | StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features |
| topic | Biological sciences Bioinformatics and computational biology Information and computing sciences Machine learning Antimicrobial peptides (AMPs) Defensins Host defense Therapeutic agents Defensin peptides (DPs) Computational methods Machine learning |