Area of research
Molecular Biology · Cancer Research
Research interest
Research interests include Single-cell and spatial transcriptomics, Cancer Genomics and Diagnostics, Gene expression and cancer classification, and Bioinformatics and Genomic Networks.
Interpretable Differential Abundance Signature (iDAS).
Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies.
The microbiome landscape of oral cancer in young patients.
Longitudinal Assessment of Health-Related Quality of Life in Adults with CKD.
CCIDeconv: Hierarchical model for deconvolution of subcellular cell-cell interactions in single-cell data
Benchmarking the translational potential of spatial gene expression prediction from histology
Benchmarking the translational potential of spatial gene expression prediction from histology.
International Consensus Recommendations of Diagnostic Criteria and Terminologies for Extranodal Extension in Head and Neck Squamous Cell Carcinoma: An HN CLEAR Initiative (Update 1)
Spatial gene expression at single-cell resolution from histology using deep learning with GHIST
Spatial gene expression at single-cell resolution from histology using deep learning with GHIST.
Pathway metabolite ratios reveal distinctive glutamine metabolism in a subset of proliferating cells.
The current landscape and emerging challenges of benchmarking single-cell methods.
Left ventricular myocardial molecular profile of human diabetic ischaemic cardiomyopathy.
Interpretable Differential Abundance Signature (iDAS)
Endothelial Colony-Forming Cell Transcriptomic Profiling in CT-defined Coronary Artery Disease from the BioHEART-CT Study Implicate CCBE1 in Mitochondrial Dysfunction-associated Atherosclerosis
BenchHub enables an inclusive and transparent ecosystem for community-focused benchmarking in computational biology
Multi-view gene panel characterization for spatially resolved omics
CLUEY enables knowledge-guided clustering and cell type detection from single-cell omics data.
Multi-view gene panel characterization for spatially resolved omics.
A message passing framework for precise cell state identification with scClassify2.
dioscRi enables transferable prediction of clinical outcomes in multi-parameter cytometry data
Digging Deeper Into Cardiovascular Plasma Proteomics: Opportunities and Limitations of Current Platforms.
Integration of Whole-Genome Sequencing Analysis with Unique Patient-Derived Models Reveals Clinically Relevant Drug Targets in TFCP2 Fusion-Defined Rhabdomyosarcoma.
Supplementary Fig. S3 from Integration of Whole-Genome Sequencing Analysis with Unique Patient-Derived Models Reveals Clinically Relevant Drug Targets in <i>TFCP2</i> Fusion–Defined Rhabdomyosarcoma
Supplementary Table S6 from Integration of Whole-Genome Sequencing Analysis with Unique Patient-Derived Models Reveals Clinically Relevant Drug Targets in <i>TFCP2</i> Fusion–Defined Rhabdomyosarcoma
Supplementary Table S2 from Integration of Whole-Genome Sequencing Analysis with Unique Patient-Derived Models Reveals Clinically Relevant Drug Targets in <i>TFCP2</i> Fusion–Defined Rhabdomyosarcoma
Supplementary Table S9 from Integration of Whole-Genome Sequencing Analysis with Unique Patient-Derived Models Reveals Clinically Relevant Drug Targets in <i>TFCP2</i> Fusion–Defined Rhabdomyosarcoma
Supplementary Table S5 from Integration of Whole-Genome Sequencing Analysis with Unique Patient-Derived Models Reveals Clinically Relevant Drug Targets in <i>TFCP2</i> Fusion–Defined Rhabdomyosarcoma
Supplementary Table S4 from Integration of Whole-Genome Sequencing Analysis with Unique Patient-Derived Models Reveals Clinically Relevant Drug Targets in <i>TFCP2</i> Fusion–Defined Rhabdomyosarcoma
Supplementary Fig. S5 from Integration of Whole-Genome Sequencing Analysis with Unique Patient-Derived Models Reveals Clinically Relevant Drug Targets in <i>TFCP2</i> Fusion–Defined Rhabdomyosarcoma