Thesis work
Thesis title: Interpretable deep learning-based approaches for precision medicine
Supervisors: Ellis Patrick, Pengyi Yang
Thesis abstract:
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Publications
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Journals
- Wagle, M., Long, S., Chen, C., Liu, C., Yang, P. (2024). Interpretable deep learning in single-cell omics. Bioinformatics, 40(5), 8 pages. [More Information]
- Sierk, M., Ratnayake, S., Wagle, M., Chen, B., Park, B., Wang, J., Youkharibache, P., Meerzaman, D. (2023). 3DVizSNP: a tool for rapidly visualizing missense mutations identifed in high throughput experiments in iCn3D. BMC Bioinformatics, 24(1), Article 244 - 1-Article 244 - 9. [More Information]
- Huang, H., Liu, C., Wagle, M., Yang, P. (2023). Evaluation of deep learning-based feature selection for single-cell RNA sequencing data analysis. Genome Biology, 24(1), Article 259 - 1-Article 259 - 20. [More Information]
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2024
- Wagle, M., Long, S., Chen, C., Liu, C., Yang, P. (2024). Interpretable deep learning in single-cell omics. Bioinformatics, 40(5), 8 pages. [More Information]
2023
- Sierk, M., Ratnayake, S., Wagle, M., Chen, B., Park, B., Wang, J., Youkharibache, P., Meerzaman, D. (2023). 3DVizSNP: a tool for rapidly visualizing missense mutations identifed in high throughput experiments in iCn3D. BMC Bioinformatics, 24(1), Article 244 - 1-Article 244 - 9. [More Information]
- Huang, H., Liu, C., Wagle, M., Yang, P. (2023). Evaluation of deep learning-based feature selection for single-cell RNA sequencing data analysis. Genome Biology, 24(1), Article 259 - 1-Article 259 - 20. [More Information]