Deoxyribonucleic Acid Based Oryza Sativa Phenotype Prediction Using Molecular Neural Network

Precise phenotypic trait prediction in Oryza sativa (rice) is essential for breeding initiatives, agricultural innovations, and maintaining food security. In this work, we present a unique method for phenotype prediction based on the examination of Oryza sativa chromosomes 1, 2, 4, 7, 11, and 12 deoxyribonucleic acid (DNA) sequences. In order to capture the complex genetic properties linked to phenotypic variances, we created a Molecular Neural Network (MNN) architecture specifically designed for processing molecular data. This architecture integrates both recurrent and convolutional layers. We preprocess the DNA sequences using one-hot encoding and data partitioning for testing, validation, and training as part of our technique. A dataset of DNA sequences and associated phenotypic labels is used to train the MNN. The effectiveness of the model is assessed using common measures like accuracy. The model's performance on a test set is being measured in the proposed study by computing Pearson correlation coefficients between expected and actual trait values. It also calculates the (PCC) for each feature in the model, and in 152 seconds, we obtained an accuracy of 98%.