Update README.md

README.md

@@ -11,7 +11,7 @@ This study, while focusing on head and neck squamous cell carcinoma (HNSCC) due
 
 ## Dataset
 
-The dataset used in this study is available at [MultiOmics-Gene-Network-HNSCC](https://huggingface.co/datasets/VatsalPatel18/MultiOmics-Gene-Network-HNSCC) and includes the following data types:
+The dataset used in this study is available at [HNSCC-MultiOmics-Gene-Cancer-Hallmark10-Network](https://huggingface.co/datasets/VatsalPatel18/HNSCC-MultiOmics-Gene-Cancer-Hallmark10-Network) and includes the following data types:
 - Gene expression
 - Mutations
 - Methylation
@@ -63,4 +63,21 @@ ga.visualize_clusters()
 ga.cluster_data2(6)
 ga.plot_kaplan_meier()
 ga.perform_log_rank_test()
-ga.generate_summary_table()
+ga.generate_summary_table()
+
+## Conclusion
+
+This model and the associated dataset provide a robust framework for integrating and analyzing multi-omics data using Graph Attention Networks. The ability to identify distinct survival groups and potential novel biomarkers underscores its potential application in precision oncology. The dynamic nature of the attention mechanism in the GATv2Conv layer ensures that the model effectively captures intricate relationships within the graph-structured data. 
+
+By leveraging this model, researchers can gain deeper insights into the multi-dimensional interactions between different omic features and their impact on clinical outcomes. The Graph Attention Autoencoder's ability to distill complex multi-omics data into meaningful latent representations facilitates the discovery of new biomarkers and enhances the understanding of cancer biology.
+
+### Future Work
+
+Future work can explore the extension of this model to other types of cancers and additional omic data types. Incorporating temporal data and longitudinal studies could further enhance the model's predictive capabilities and provide a more comprehensive view of tumor progression and treatment response.
+
+### References
+
+- Brody, S., Alon, U., & Yahav, E. (2021). How Attentive are Graph Attention Networks?. arXiv preprint arXiv:2105.14491.
+- Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2018). Graph Attention Networks. arXiv preprint arXiv:1710.10903.
+- Fey, M., & Lenssen, J. E. (2019). Fast Graph Representation Learning with PyTorch Geometric. arXiv preprint arXiv:1903.02428.
+- Kipf, T. N., & Welling, M. (2016). Variational Graph Auto-Encoders. arXiv preprint arXiv:1611.07308.