Abstract—In bioinformatics and computational biology, sequence classification is crucial for tasks such as protein function prediction, disease classification, and gene annotation. While deep learning has advanced this field, model performance is heavily influenced by the sequence encoding methods used. This study evaluates four encoding schemes—one-hot, k-mer (substring-based encoding), embeddings, and Position-Specific Scoring Matrix (PSSM) using Convolutional Neural Networks (CNNs) and Long Short-Term Memories (LSTMs). Annotated protein and DNA sequences were encoded, balanced, and trained under standardized conditions for fair comparison. Results show that k-mer encoding achieved the highest accuracy (89% with CNN, 90% with LSTM). LSTMs also performed well with embedding-based representations, effectively capturing sequence dependencies. In contrast, PSSM and one-hot encodings yielded lower accuracy, suggesting reduced suitability for deep learning. These findings provide practical guidance for selecting optimal model-encoding combinations, aiming to improve both accuracy and computational efficiency in sequence classification tasks.
 
Keywords—Convolutional Neural Network (CNN), deep learning, k-mer encoding, Long Short-Term Memory (LSTM), sequence classification, process innovation
 
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