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Dr Chung Kok

Adjunct Senior Research Fellow Centre for Cancer Biology

email Chung.Kok@unisa.edu.au

Research Degree Supervisor

Research

Research focus

Cardiovascular Medicine and Haematology
Clinical Sciences
Genetics
Medical Microbiology
Other Biological Sciences

Research

Outputs

Research outputs for the last seven years are shown below.

Open access indicates that an output is open access.

Journal Articles

Year	Output
2023	Hiwase, D, Hahn, C, Tran, ENH, Chhetri, R, Wang, P, Kumar, S, Brown, A, Scott, H, Kok, CH & Shah, MV and 41 other authors 2023, 'TP53 mutation in therapy-related myeloid neoplasm defines a distinct molecular subtype', Blood, vol. 141, no. 9, pp. 1087-1091. Open access
2023	Kutyna, MM, Loone, S, Saunders, VA, White, DL, Kok, CH & Hiwase, DK 2023, 'Solute carrier family 29A1 mediates in vitro resistance to Azacitidine in Acute Myeloid Leukemia cell lines', International Journal of Molecular Sciences, vol. 24, no. 4, article no. 3553, pp. 1-14. Open access
2022	Shanmuganathan, N, Wadham, C, Thomson, D, Shahrin, NH, Vignaud, C, Obourn, V, Chaturvedi, S, Yang, F, Feng, J, Saunders, V, Kok, CH, Yeung, D, King, RM, Kenyon, RR, Lin, M, Wang, P, Scott, H, Hughes, T, Schreiber, AW & Branford, S 2022, 'RNA-based targeted gene sequencing improves the diagnostic yield of mutant detection in chronic myeloid leukemia', Journal of Molecular Diagnostics, vol. 24, no. 7, pp. 803-822.

External engagement & recognition

Collaborations

External engagement & recognition

Collaborations

Organisation	Country
Mayo Clinic	UNITED STATES
Novartis Institutes for Biomedical Research	UNITED STATES
Novartis Pharmaceuticals	UNITED STATES
Royal Adelaide Hospital	AUSTRALIA
SA Pathology	AUSTRALIA
South Australian Health and Medical Research Institute (SAHMRI)	AUSTRALIA
University of Adelaide	AUSTRALIA
University of South Australia	AUSTRALIA

Teaching & student supervision

Teaching & student supervision

Research degree supervision

Supervisions from 2010 shown

Thesis title	Student status
Enhanced bioinformatics and machine learning for improved risk stratification and outcome prediction in chronic myeloid leukaemia	Current

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