Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+## About
+
+RNA-FM (RNA Foundation Model) is a state-of-the-art **pretrained language model for RNA sequences**, serving as the foundation for an integrated RNA research ecosystem. 
+Trained on **23+ million non-coding RNA (ncRNA) sequences** via self-supervised learning, RNA-FM extracts comprehensive structural and functional information from RNA sequences *without* relying on experimental labels.
+**[mRNA‑FM](https://arxiv.org/abs/2204.00300)** is a direct extension of RNA-FM, trained exclusively on 45 million mRNA coding sequences (CDS). 
+It is specifically designed to capture information unique to mRNA and has demonstrated excellent performance in related tasks.
+Consequently, RNA-FM generates **general-purpose RNA embeddings** suitable for a broad range of downstream tasks, including but not limited to secondary and tertiary structure prediction, RNA family clustering, and functional RNA analysis.
+
+
+The full codes are available at GitHub: https://github.com/ml4bio/RNA-FM.
+
+## Citation
+
+If you use the model in your research, please cite our paper with the following.
+
+```
+@article{chen2022interpretable,
+  title={Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions},
+  author={Chen, Jiayang and Hu, Zhihang and Sun, Siqi and Tan, Qingxiong and Wang, Yixuan and Yu, Qinze and Zong, Licheng and Hong, Liang and Xiao, Jin and Shen, Tao and others},
+  journal={arXiv preprint arXiv:2204.00300},
+  year={2022}
+}
+
+@article{shen2024accurate,
+  title={Accurate RNA 3D structure prediction using a language model-based deep learning approach},
+  author={Shen, Tao and Hu, Zhihang and Sun, Siqi and Liu, Di and Wong, Felix and Wang, Jiuming and Chen, Jiayang and Wang, Yixuan and Hong, Liang and Xiao, Jin and others},
+  journal={Nature Methods},
+  pages={1--12},
+  year={2024},
+  publisher={Nature Publishing Group US New York}
+}
+
+@article{chen2020rna,
+  title={RNA secondary structure prediction by learning unrolled algorithms},
+  author={Chen, Xinshi and Li, Yu and Umarov, Ramzan and Gao, Xin and Song, Le},
+  journal={arXiv preprint arXiv:2002.05810},
+  year={2020}
+}
+```