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[2602.20198] KEMP-PIP: A Feature-Fusion Based Approach for Pro-inflammatory Peptide Prediction
Summary
The article presents KEMP-PIP, a novel hybrid machine learning framework designed for predicting pro-inflammatory peptides by integrating deep protein embeddings with handcrafted descriptors.
Why It Matters
Pro-inflammatory peptides are crucial in immune responses, yet their identification is challenging. KEMP-PIP offers a more efficient prediction method, potentially accelerating research in immunology and therapeutic development.
Key Takeaways
- KEMP-PIP integrates deep learning and handcrafted features for peptide prediction.
- The framework improves predictive performance over existing methods.
- A web server for KEMP-PIP is freely available for broader accessibility.
- Feature pruning and class-weighted regression help manage data complexity.
- Systematic ablation studies confirm the effectiveness of the feature integration.
Quantitative Biology > Quantitative Methods arXiv:2602.20198 (q-bio) [Submitted on 22 Feb 2026] Title:KEMP-PIP: A Feature-Fusion Based Approach for Pro-inflammatory Peptide Prediction Authors:Soumik Deb Niloy, Md. Fahmid-Ul-Alam Juboraj, Swakkhar Shatabda View a PDF of the paper titled KEMP-PIP: A Feature-Fusion Based Approach for Pro-inflammatory Peptide Prediction, by Soumik Deb Niloy and 2 other authors View PDF HTML (experimental) Abstract: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 S...