Optimized Feature Learning for Anti-Inflammatory Peptide Prediction Using Parallel Distributed Computing

With recent advancements in computational biology, high throughput Next-Generation Sequencing (NGS) has become a de facto standard technology for gene expression studies, including DNAs, RNAs, and proteins; however, it generates several millions of sequences single run. Moreover, the raw sequencing datasets are increasing exponentially, doubling size every 18 months, leading to big data issue biology. inflammatory illnesses boosting immune function have recently attracted lot attention, yet accurate recognition Anti-Inflammatory Peptides (AIPs) through biological process is time-consuming as therapeutic agents inflammatory-related diseases. Similarly, precise classification these AIPs challenging traditional conventional machine learning algorithms. Parallel distributed computing models deep neural networks major platforms analytics now required This study proposes an efficient high-throughput anti-inflammatory peptide predictor based on parallel network model. The model performance extensively evaluated regarding measurement parameters such accuracy, efficiency, scalability, speedup sequential environments. encoding sequence were balanced using SMOTETomek approach, resulting high-accuracy performance. demonstrated speed up scalability compared other algorithms study’s outcome could promote parallel-based predicting anti-Inflammatory Peptides.