The paper "Swarm-based support vector machine optimization for protein sequence-encoded prediction" by Prasanalakshmi Balaji, K. Srinivasan, R. Mahaveerakannan, Sudhanshu Maurya, and T. Rajesh Kumar introduces a novel method for predicting protein sequences using a combination of the chameleon swarm algorithm (CSA) and support vector machine (SVM). The method, called w-SVM, is designed to predict proteins based on their amino acid sequences, focusing on features such as mutation rate, hydrophilicity, and hydrophobicity. The feature extraction process involves 2DLDLA for mutation rate, CWT for hydrophilicity, and DWT for hydrophobicity. The experimental analysis is conducted using MATLAB, and the results are compared with state-of-the-art methods like LSTM, DSSP, and ARIMA. The study demonstrates that the proposed method outperforms these approaches in predicting proteins effectively. The paper also reviews existing literature on protein interaction prediction methods, highlighting their strengths and limitations.The paper "Swarm-based support vector machine optimization for protein sequence-encoded prediction" by Prasanalakshmi Balaji, K. Srinivasan, R. Mahaveerakannan, Sudhanshu Maurya, and T. Rajesh Kumar introduces a novel method for predicting protein sequences using a combination of the chameleon swarm algorithm (CSA) and support vector machine (SVM). The method, called w-SVM, is designed to predict proteins based on their amino acid sequences, focusing on features such as mutation rate, hydrophilicity, and hydrophobicity. The feature extraction process involves 2DLDLA for mutation rate, CWT for hydrophilicity, and DWT for hydrophobicity. The experimental analysis is conducted using MATLAB, and the results are compared with state-of-the-art methods like LSTM, DSSP, and ARIMA. The study demonstrates that the proposed method outperforms these approaches in predicting proteins effectively. The paper also reviews existing literature on protein interaction prediction methods, highlighting their strengths and limitations.