Core Insights - The research team, including Shandong University of Technology and several international institutions, developed the PlantLncBoost model to address the challenges of identifying plant lncRNA [1][3][24] - The model achieved an average prediction accuracy of 91.7% across 12 different plant datasets, outperforming existing tools by 18.2% [3][17] - The study highlights the importance of lncRNA in plant growth, development, and environmental adaptation, emphasizing its role in regulating flowering time and responding to climate change [1][2] Model Development - The PlantLncBoost model incorporates 219 novel sequence descriptors based on mathematical theories such as Fourier transform and Shannon entropy [3][6] - The model was trained using a dataset of 24,152 lncRNA sequences from nine angiosperm species, ensuring high reliability through strict quality control [4][7] - Feature selection involved recursive feature elimination (RFE) to identify three core parameters with cross-species discrimination capability [3][11] Performance Evaluation - The model was validated using two key test sets: a comprehensive test set covering 20 diverse plant species and a high-confidence experimental validation set [5][19] - PlantLncBoost demonstrated superior performance with sensitivity at 98.42%, specificity at 94.93%, and accuracy at 96.63%, significantly surpassing other mainstream models [22][21] - The model's ROC curve achieved an AUC of 98.35%, indicating its effectiveness in prediction [19][22] Feature Engineering - A total of 1,662 features were extracted, including traditional sequence-based metrics and innovative mathematical features, enhancing the model's ability to identify lncRNA [6][15] - The model's performance peaked with a lightweight feature set, confirming the effectiveness of using a minimal number of key features [13][15] Collaborative Efforts - The research reflects a growing trend of collaboration between academic institutions and enterprises in advancing plant lncRNA research and applications [24][26] - Innovations in lncRNA research are expected to contribute to sustainable agricultural practices and ecological balance [26][27]