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Machine learning and statistical modelling of trans-omic networks in controlling cell identity, cell-fate decisions, and cancers

   Computational Systems Biology Group

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

About the Project

The advances in mass spectrometry-based proteomics and phosphoproteomics, and next-generation sequencing-based transcriptomics and epigenomics create the opportunity to study cell identity, cell-fate decisions, and complex diseases such as cancers at a system level. However, the availability of a large amount of heterogeneous multi-omic data does not translate itself into biological knowledge and treatments. The development of computational methods that are able to integrate multi-omic data is essential to achieve these goals.

This project aims to leverage the multi-omic data and develop machine learning and statistical models to reconstruct "trans-omic" networks that cut across multiple molecular programs (e.g., cell signalling, transcriptional, translational, and epigenomic regulations) for controlling cell identity and cell-fate decisions and to understand their malfunction in complex diseases such as cancers. In particular, we will use data generated from single-cell omics technologies to investigate intermediate cell types/states to enhance our understanding of cell type/state transitions during stem cell differentiation and organ development and also in cancer progression. Together, this project will establish a global approach to identify, investigate, and characterise transitional cell states.

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. Kim, H., Wang, K., Chen, C., Lin, Y., Tam, PPL., Lin, D., Yang, J. & Yang, P. (2021) Uncovering cell identity through differential stability with Cepo. Nature Computational Science, 1, 784-790.
2. Kim, H., Osteil, P., Humphrey, S., Cinghu, S., Oldfield, A., Patrick, E., Wilkie, E., Peng, G., Suo, S., Jothi, R., Tam, P. & Yang, P. (2020) Transcriptional network dynamics during the progression of pluripotency revealed by integrative statistical learning. Nucleic Acids Research, 48(4), 1828-1842.
3. Yang, P., Humphrey, S., Cinghu, S., Pathania, R., Oldfield, A., Kumar, D., Perera, D., Yang, J., James, D., Mann, M. & Jothi, R. (2019) Multi-omic profiling reveals dynamics of the phased progression of pluripotency. Cell Systems, 8(5), 427-445.
4. Xiao, D., Kim, H., Pang, I. & Yang, P. (2022) Functional analysis of the stable phosphoproteome reveals cancer vulnerabilities. Bioinformatics, 38(7), 1956-1963.
5. Pathania, R., Ramachandran, S., Elangovan, S., Padia, R., Yang, P., Cinghu, S., Veeranan-Karmegam, R., Fulzele, S., Pei, L., Chang, C., Choi, J., Shi, H., Manicassamy, S., Prasad, P., Sharma, S., Ganapathy, V., Jothi, R. & Thangaraju, M. (2015). DNMT1 is essential for mammary and cancer stem cell maintenance and tumorigenesis. Nature Communications, 6, 6910.

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