Grain protein function prediction based on self-attention mechanism and bidirectional LSTM

Abstract

With the development of genome sequencing technology, using computing technology to predict grain protein function has become one of the important tasks of bioinformatics. The protein data of four grains, soybean, maize, indica and japonica are selected in this experimental dataset. In this paper, a novel neural network algorithm Chemical-SA-BiLSTM is proposed for grain protein function prediction. The Chemical-SA-BiLSTM algorithm fuses the chemical properties of proteins on the basis of amino acid sequences, and combines the self-attention mechanism with the bidirectional Long Short-Term Memory network. The experimental results show that the Chemical-SA-BiLSTM algorithm is superior to other classical neural network algorithms, and can more accurately predict the protein function, which proves the effectiveness of the Chemical-SA-BiLSTM algorithm in the prediction of grain protein function. The source code of our method is available at https://github.com/HwaTong/Chemical-SA-BiLSTM.

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

“…Plant diseases [2] such as downy mildew and common rust, as well as pests such as corn armyworm [3][4][5] can significantly affect maize yields, leading to global supply and price fluctuations. Prediction of grain yield [6] is a critical process to estimate the amount of grain that a specific crop will produce. It assists in optimizing crop production, aiding decisions related to planting, fertilization, and harvest.…”