Simon Cai
Simon Cai
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Proteomics
Machine learning of cancer type and tissue of origin from proteomes of 1,277 human tissue samples and 975 cancer cell lines
Cancer type is determined via assessment of tumour morphology, aided by immunohistochemical staining patterns. The development of machine learning (ML) models using histology slides has powered the image-based prediction of the site of origin in cancer of unknown primary (CUP).
Opportunities for pharmacoproteomics in biomarker discovery
Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact …
Rebecca C Poulos
,
Zhaoxiang (Simon) Cai
,
Phillip J Robinson
,
Roger R Reddel
,
Qing Zhong
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DOI
Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities
Proteomic data can reveal novel associations between genotype and phenotype, beyond what is apparent from genomics or transcriptomics alone. However, a lack of large proteomic datasets across a range of cancer types has limited our understanding of proteome network organisation and regulation.
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