Attention-enhanced CNN-BiLSTM models for predicting luteinizing hormone sequence variations

Luteinizing hormone (LH) plays a pivotal role in regulating reproductive function and alterations in its amino acid sequence can profoundly affect hormonal activity. Variations in protein sequence is crucial for understanding hormonal imbalances and associated reproductive disorders. In this study, we present a deep learning-based computational framework for predicting LH sequence alterations using protein sequence data. Protein sequences were pre-processed, numerically encoded and balanced prior to model development. Individual deep learning models, including convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks, were implemented and compared with hybrid attention-enhanced architectures, culminating in the proposed CNN + Attention + BiLSTM model. Experimental evaluation using stratified training, validation and testing splits revealed that the CNN model achieved a testing accuracy of 86.07%, whereas the BiLSTM network improved performance to 91.47%, underscoring the importance of modeling sequential dependencies. Notably, the proposed CNN + Attention + BiLSTM model outperformed all other architectures, achieving testing and validation accuracies of 99.42% and 99.60%, respectively, along with near-perfect precision (0.995), recall (0.99) and F1-score (0.995). This study validates the efficacy of attention-based hybrid deep learning architectures in predicting variations in LH sequence, representing a promising approach for identifying novel protein biomarkers and enhancing diagnostic capabilities in reproductive health.