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Deep learning-based data imputation and missing modality prediction for single-cell multi-omics data

   Computational Systems Biology Group

   Applications accepted all year round  Funded PhD Project (Students Worldwide)

About the Project

Recent advancements in single-cell multimodal profiling technologies enable various molecular programs such as gene expression, protein abundance, and chromatin accessibility to be profiled in individual cells. While the single-cell multimodal omics data generated from these technologies hold promise to model and understand cell types and cell fate decisions at an unprecedented resolution, such data often contain large amounts of missing values and missing modalities.

A promising application of machine learning methods, in particular deep learning models, is to impute missing values and data modalities for such data so as to significantly improve the utility of single-cell multimodal omics data for understanding complex biological and cellular systems. This project aims to design, evaluate, and benchmark machine/deep learning methods for data imputation and for predicting missing modalities in single-cell multimodal omics data.

Funding Notes

Students will be enrolled at the University of Sydney. Applicants will be assessed in a competitive process involving an interview. Successful applicants will be awarded a CMRI Ph.D. Scholarship Award (View Website), consisting of a generous top-up over the value of a university scholarship. Successful applicants will also be expected to apply for external scholarships.
There is also direct Ph.D. scholarship funding for this project from the Computational Systems Biology Group, but candidates are expected to first apply for a CMRI Ph.D. Scholarship award and other external scholarships prior to the consideration of lab scholarship.


1. Cao, Y., Geddes, T., Yang, J. & Yang, P. (2020) Ensemble deep learning in bioinformatics. Nature Machine Intelligence, 2, 500-508.
2. Yang, P., Hao, H. & Liu, C. (2021) Feature selection revisited in the single-cell era. Genome Biology, 22, 321.
3. Liu. C., Hao, H. & Yang, P. (2022) Multi-task learning from single-cell multimodal omics with Matilda, Biorxiv.
4. Geddes, T., Kim, T., Nan, L., Burchfield, J., Yang, J., Tao, D. & Yang, P. (2019) Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis. BMC Bioinformatics, 20, 660.

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