Dharmesh D. Bhuva
Walter and Eliza Hall Institute of Medical Research
24 Papers
11 Citations
Dharmesh D. Bhuva is an academic researcher from Walter and Eliza Hall Institute of Medical Research. The author has contributed to research in topics: Biology & Transcriptome. The author has an hindex of 4, co-authored 7 publications. Previous affiliations of Dharmesh D. Bhuva include University of Melbourne.
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Papers
Single sample scoring of molecular phenotypes
Momeneh Foroutan,Momeneh Foroutan,Dharmesh D. Bhuva,Dharmesh D. Bhuva,Ruqian Lyu,Kristy A. Horan,Joseph Cursons,Joseph Cursons,Melissa J. Davis,Melissa J. Davis +9 more
TL;DR: The singscore method functions independent of sample composition in gene expression data and thus it provides stable scores, which are particularly useful for small data sets or data integration, and includes a suite of powerful visualization functions to assist in the interpretation of results.
Transcriptomic profiling of cardiac tissues from SARS‐CoV‐2 patients identifies DNA damage
Arutha Kulasinghe,Ning Liu,Chin Wee Godwin Tan,James Monkman,Jane E Sinclair,Dharmesh D. Bhuva,David Godbolt,Liuliu Pan,Andrew Nam,H. Sadeghirad,Kei Sato,Gianluigi Li Bassi,Kenneth J. O'Byrne,Camila Hartmann,Anna Flavia Ribeiro dos Santos Miggiolaro,Gustavo Lenci Marques,Lidia Moura,Derek J. Richard,Mark A. Adams,Lucia de Noronha,Cristina Pellegrino Baena,Jacky Y. Suen,Rakesh C. Arora,Gabrielle T. Belz,Kirsty R. Short,Melissa J. Davis,F. Souza-Fonseca Guimaraes,John F. Fraser +27 more
TL;DR: The emergence of distinct transcriptomic profiles in cardiac tissues of SARS-CoV-2 and pH1N1 influenza infection supporting the need for a greater understanding of the effects on extra-pulmonary organs, including the cardiovascular system of COVID-19 patients, and long term impact on health is demonstrated.
24
Discovery of a highly potent, selective, orally bioavailable inhibitor of KAT6A/B histone acetyltransferases with efficacy against KAT6A-high ER+ breast cancer.
Shikhar Sharma,Chi-Yeh Chung,S. Uryu,Jelena D. Petrović,Joan Q. Cao,Amanda Rickard,N. Nady,S.E. Greasley,Eric D. Johnson,O. Brodsky,Showkhin Khan,Hui Wang,Zhenxiong Wang,Yong Zhang,Konstantinos E. Tsaparikos,Lei Chen,Anthony Mazurek,John D. Lapek,Pei-Pei Kung,S. Sutton,Paul F. Richardson,Eric Greenwald,Shin Yamazaki,Rhys Jones,K. Maegley,P. Bingham,Hieu Lam,Alexandra E Stupple,Aileen Kamal,A. Chueh,A. Cuzzupe,B. J. Morrow,B. Ren,Catalina Carrasco-Pozo,Chin Wee Godwin Tan,Dharmesh D. Bhuva,E. Allan,Elliot Surgenor,F. Vaillant,Havva Pehlivanoglu,Hendrik Falk,James R. Whittle,Janet Newman,Joseph Cursons,Judy P. Doherty,K. White,Laura MacPherson,Mark G. Devlin,M. Dennis,M. Hattarki,M. de Silva,M. Camerino,Miriam S. Butler,Olan Dolezal,P. Pilling,R. Foitzik,P. Stupple,H. Rachel Lagiakos,Scott E. Walker,Soroor Hediyeh-Zadeh,S. Nuttall,S. K. Spall,Susan A. Charman,T. Connor,Thomas S. Peat,Vicky M. Avery,Y. E. Bozikis,Yuqing Yang,Ming Zhang,Brendon J. Monahan,Anne K. Voss,Tim Thomas,I. Street,Sarah-Jane Dawson,Mark A. Dawson,Geoffrey J. Lindeman,Melissa J. Davis,Jane E. Visvader,Thomas A Paul +78 more
TL;DR: Researchers identified CTx-648, a potent and selective KAT6A/B inhibitor, which shows anti-tumor activity in ER+ breast cancer by inhibiting H3K23Ac and downregulating estrogen signaling, cell cycle, and stem cell pathways, offering a potential therapeutic strategy.
22
standR: spatial transcriptomic analysis for GeoMx DSP data.
Dharmesh D. Bhuva,Micah Bokelund,Arutha Kulasinghe +2 more
TL;DR: StandR, an R/Bioconductor package that enables an end-to-end analysis of GeoMx DSP data and how the application of standR enables scientists to develop in-depth insights into the biology of interest is presented.
17
Using singscore to predict mutation status in acute myeloid leukemia from transcriptomic signatures
Dharmesh D. Bhuva,Dharmesh D. Bhuva,Momeneh Foroutan,Yi Xie,Ruqian Lyu,Joseph Cursons,Joseph Cursons,Melissa J. Davis,Melissa J. Davis +8 more
TL;DR: The singscore method is believed to be particularly useful for studying heterogeneity within a specific subsets of cancers, and the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs is demonstrated.