KEMP-PIP: A Feature-Fusion Based Approach for Pro-inflammatory Peptide Prediction
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arXiv
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| Format: | Preprint |
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2026
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| author | Niloy, Soumik Deb Juboraj, Md. Fahmid-Ul-Alam Shatabda, Swakkhar |
| author_facet | Niloy, Soumik Deb Juboraj, Md. Fahmid-Ul-Alam Shatabda, Swakkhar |
| contents | Pro-inflammatory peptides (PIPs) play critical roles in immune signaling and inflammation but are difficult to identify experimentally due to costly and time-consuming assays. To address this challenge, we present KEMP-PIP, a hybrid machine learning framework that integrates deep protein embeddings with handcrafted descriptors for robust PIP prediction. Our approach combines contextual embeddings from pretrained ESM protein language models with multi-scale k-mer frequencies, physicochemical descriptors, and modlAMP sequence features. Feature pruning and class-weighted logistic regression manage high dimensionality and class imbalance, while ensemble averaging with an optimized decision threshold enhances the sensitivity--specificity balance. Through systematic ablation studies, we demonstrate that integrating complementary feature sets consistently improves predictive performance. On the standard benchmark dataset, KEMP-PIP achieves an MCC of 0.505, accuracy of 0.752, and AUC of 0.762, outperforming ProIn-fuse, MultiFeatVotPIP, and StackPIP. Relative to StackPIP, these results represent improvements of 9.5% in MCC and 4.8% in both accuracy and AUC. The KEMP-PIP web server is freely available at https://nilsparrow1920-kemp-pip.hf.space/ and the full implementation at https://github.com/S18-Niloy/KEMP-PIP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20198 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | KEMP-PIP: A Feature-Fusion Based Approach for Pro-inflammatory Peptide Prediction Niloy, Soumik Deb Juboraj, Md. Fahmid-Ul-Alam Shatabda, Swakkhar Quantitative Methods Machine Learning 92B20, 68T05 I.2.6; J.3; I.5.1 Pro-inflammatory peptides (PIPs) play critical roles in immune signaling and inflammation but are difficult to identify experimentally due to costly and time-consuming assays. To address this challenge, we present KEMP-PIP, a hybrid machine learning framework that integrates deep protein embeddings with handcrafted descriptors for robust PIP prediction. Our approach combines contextual embeddings from pretrained ESM protein language models with multi-scale k-mer frequencies, physicochemical descriptors, and modlAMP sequence features. Feature pruning and class-weighted logistic regression manage high dimensionality and class imbalance, while ensemble averaging with an optimized decision threshold enhances the sensitivity--specificity balance. Through systematic ablation studies, we demonstrate that integrating complementary feature sets consistently improves predictive performance. On the standard benchmark dataset, KEMP-PIP achieves an MCC of 0.505, accuracy of 0.752, and AUC of 0.762, outperforming ProIn-fuse, MultiFeatVotPIP, and StackPIP. Relative to StackPIP, these results represent improvements of 9.5% in MCC and 4.8% in both accuracy and AUC. The KEMP-PIP web server is freely available at https://nilsparrow1920-kemp-pip.hf.space/ and the full implementation at https://github.com/S18-Niloy/KEMP-PIP. |
| title | KEMP-PIP: A Feature-Fusion Based Approach for Pro-inflammatory Peptide Prediction |
| topic | Quantitative Methods Machine Learning 92B20, 68T05 I.2.6; J.3; I.5.1 |
| url | https://arxiv.org/abs/2602.20198 |