eprintid: 15441 rev_number: 12 eprint_status: archive userid: 2 dir: disk0/00/01/54/41 datestamp: 2024-11-28 23:30:18 lastmod: 2024-11-28 23:30:19 status_changed: 2024-11-28 23:30:18 type: article metadata_visibility: show creators_name: Usmani, Salman Sadullah creators_name: Tuhin, Izaz Ahmmed creators_name: Mia, Md. Rajib creators_name: Islam, Md. Monirul creators_name: Mahmud, Imran creators_name: Uc Ríos, Carlos Eduardo creators_name: Fabian Gongora, Henry creators_name: Ashraf, Imran creators_name: Samad, Md. Abdus creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: carlos.uc@unini.edu.mx creators_id: henry.gongora@uneatlantico.es creators_id: creators_id: title: StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides ispublished: pub subjects: uneat_bm divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public abstract: Interleukin-10, a highly effective cytokine recognized for its anti-inflammatory properties, plays a critical role in the immune system. In addition to its well-documented capacity to mitigate inflammation, IL-10 can unexpectedly demonstrate pro-inflammatory characteristics under specific circumstances. The presence of both aspects emphasizes the vital need to identify the IL-10-induced peptide. To mitigate the drawbacks of manual identification, which include its high cost, this study introduces StackIL10, an ensemble learning model based on stacking, to identify IL-10-inducing peptides in a precise and efficient manner. Ten Amino-acid-composition-based Feature Extraction approaches are considered. The StackIL10, stacking ensemble, the model with five optimized Machine Learning Algorithm (specifically LGBM, RF, SVM, Decision Tree, KNN) as the base learners and a Logistic Regression as the meta learner was constructed, and the identification rate reached 91.7%, MCC of 0.833 with 0.9078 Specificity. Experiments were conducted to examine the impact of various enhancement techniques on the correctness of IL-10 Prediction. These experiments included comparisons between single models and various combinations of stacking-based ensemble models. It was demonstrated that the model proposed in this study was more effective than singular models and produced satisfactory results, thereby improving the identification of peptides that induce IL-10. date: 2024-11 publication: PLOS ONE volume: 19 number: 11 pagerange: e0313835 id_number: doi:10.1371/journal.pone.0313835 refereed: TRUE issn: 1932-6203 official_url: http://doi.org/10.1371/journal.pone.0313835 access: open language: en citation: Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Interleukin-10, a highly effective cytokine recognized for its anti-inflammatory properties, plays a critical role in the immune system. In addition to its well-documented capacity to mitigate inflammation, IL-10 can unexpectedly demonstrate pro-inflammatory characteristics under specific circumstances. The presence of both aspects emphasizes the vital need to identify the IL-10-induced peptide. To mitigate the drawbacks of manual identification, which include its high cost, this study introduces StackIL10, an ensemble learning model based on stacking, to identify IL-10-inducing peptides in a precise and efficient manner. Ten Amino-acid-composition-based Feature Extraction approaches are considered. The StackIL10, stacking ensemble, the model with five optimized Machine Learning Algorithm (specifically LGBM, RF, SVM, Decision Tree, KNN) as the base learners and a Logistic Regression as the meta learner was constructed, and the identification rate reached 91.7%, MCC of 0.833 with 0.9078 Specificity. Experiments were conducted to examine the impact of various enhancement techniques on the correctness of IL-10 Prediction. These experiments included comparisons between single models and various combinations of stacking-based ensemble models. It was demonstrated that the model proposed in this study was more effective than singular models and produced satisfactory results, thereby improving the identification of peptides that induce IL-10. metadata Usmani, Salman Sadullah; Tuhin, Izaz Ahmmed; Mia, Md. Rajib; Islam, Md. Monirul; Mahmud, Imran; Uc Ríos, Carlos Eduardo; Fabian Gongora, Henry; Ashraf, Imran y Samad, Md. Abdus mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, carlos.uc@unini.edu.mx, henry.gongora@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides. PLOS ONE, 19 (11). e0313835. ISSN 1932-6203 document_url: http://repositorio.uniromana.edu.do/id/eprint/15441/1/journal.pone.0313835.pdf