Machine learning

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).

Introduction Proteomic data provide unique insights into the molecular behaviour of cells in both healthy and disease contexts. …

The proteome provides unique insights into disease biology beyond the genome and transcriptome. A lack of large proteomic datasets has …

Introduction Proteomic data provide unique insights into the molecular behaviour of cells in both healthy and disease contexts. …

Multi-omics data analysis is an important aspect of cancer molecular biology studies and has led to ground-breaking discoveries. Many …

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.

Omics data analysis, powered by machine learning, has significantly improved cancer diagnosis and prognosis. However, most machine …

Proteomic data can reveal novel associations between genotype and phenotype, beyond what is apparent from genomics or transcriptomics …

I designed a new method which can be used by embryologists to determine the embryo differentiation propensity, given bulk RNA-seq data or qPCR data. The method is novel in using combined different single-cell datasets as a reference, then mapping the bulk RNA data to the lineage tree that is inferred from the single-cell data.

During the 5 weeks internship at SEEK, together with another three group members and one mentor from SEEK, we developed a model to make prioritizations for the relevant jobs selected by existing algorithms.