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mACPpred
WebservermACPpred is a machine learning model for precise prediction of anticancer peptides (ACPs), employing a two-step feature selection protocol on seven encoding types covering sequence composition, physicochemical properties, and profile features. After extracting optimal features, it uses prediction probabilities as input vectors for support vector machine (SVM) integration.
mACPpred 2.0
WebservermACPpred 2.0 is a stacked deep learning (SDL)-based predictor for anticancer peptides (ACPs), integrating the latest benchmark dataset with hybrid feature representations (top 7 NLP pre-trained embeddings + 90-dimensional probabilistic features). It employs 1D CNN blocks to extract spatial features, pioneering the joint modeling of spatial and probabilistic representations.
mAHTPred
WebservermAHTPred is a sequence-based meta-predictor using 6 ML algorithms (ERT, RF, etc.) and 51 feature descriptors, developing meta-models via two-step feature selection and ensemble learning to improve antihypertensive peptide prediction.
mAHTPred
ToolAn ensemble deep learning model combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM), using Amino Acid Composition (AAC) and g-gap Dipeptide Composition (DPC) for feature extraction, achieving 95% and 88.9% accuracy on benchmark and independent datasets for antihypertensive peptide prediction.
MA-PEP
ToolMA-PEP is a prediction tool for anticancer peptides (ACPs) based on multiple attention mechanisms, integrating molecular-level chemical features and sequence information via feature enhancement and fusion to address limitations in multi-modal feature integration. The model enhances key feature representations through attention mechanisms, demonstrating superior prediction performance on multiple benchmark datasets. Visual analysis and case studies validate feature extraction reliability, providing a new approach for ACP exploration with code and datasets available online.