DeepAIPs-SFLA: Deep Convolutional Model for Prediction of Anti-Inflammatory Peptides Using Binary Pattern Decomposition of Novel Multiview Descriptors with an SFLA Approach.

ABSTRACT: Inflammation is a vital biological response of the human immune system to harmful stimuli, and it plays a vital role in tissue repair and pathogen elimination. However, chronic inflammation can lead to severe diseases such as arthritis, cancer, cardiovascular disorders, and autoimmune conditions. Anti-inflammatory peptides (AIPs) have emerged as promising therapeutic agents owing to their high selectivity, potency toward target cells, and minimal side effects. Although numerous computational predictors exist for predicting AIP samples, most rely on traditional compositional features that fail to capture internal sequence ordering, local structural variations, and evolutionary information to determine peptide functionality. To address these problems, we propose DeepAIPs-SFLA, a novel deep l

SUBMITTER: Akbar S 

PROVIDER: S-EPMC12368731 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature