A5046387257- Single-cell and spatial transcriptomics
- Acute Myeloid Leukemia Research
- Gene expression and cancer classification
- Cell Image Analysis Techniques
- Cancer Immunotherapy and Biomarkers
- Histone Deacetylase Inhibitors Research
- Multiple Myeloma Research and Treatments
- Immune cells in cancer
- Ubiquitin and proteasome pathways
- T-cell and B-cell Immunology
- Evolution and Genetic Dynamics
- Immune Cell Function and Interaction
- Genomics and Phylogenetic Studies
- Vector-borne infectious diseases
- Tuberculosis Research and Epidemiology
- Diverse Scientific Research Studies
- Chronic Lymphocytic Leukemia Research
- Lung Cancer Treatments and Mutations
- Immunotherapy and Immune Responses
- vaccines and immunoinformatics approaches
- Malaria Research and Control
- Computational Drug Discovery Methods
- IL-33, ST2, and ILC Pathways
- Mycobacterium research and diagnosis
The University of Sydney
2022-2024
Canada's Michael Smith Genome Sciences Centre
2021-2022
University of British Columbia
2020-2021
Simon Fraser University
2019-2020
Abstract The recent emergence of multi-sample multi-condition single-cell multi-cohort studies allows researchers to investigate different cell states. effective integration multiple large-cohort promises biological insights into cells under conditions that individual cannot provide. Here, we present scMerge2, a scalable algorithm data atlas-scale studies. We have generalized scMerge2 enable the merging millions from generated by various technologies. Using large COVID-19 collection with...
Abstract The recent emergence of multi-sample multi-condition single-cell multi-cohort studies allow researchers to investigate different cell states. effective integration multiple large-cohort promises biological insights into cells under conditions that individual cannot provide. Here, we present scMerge2, a scalable algorithm allows data atlas-scale studies. We have generalised scMerge2 enable the merging millions from generated by various technologies. Using large COVID-19 collection...
Abstract Survival in recurrent/metastatic head and neck mucosal squamous cell carcinoma (HNmSCC) remains poor. Anti-programmed death (PD)-1 therapies have demonstrated improved survival with lower toxicity when compared to standard chemotherapy. However, response anti-PD-1 therapy modest, at 13-17%. We evaluated the tumor microenvironment (TME) using Imaging Mass Cytometry (IMC) on 27 specimens from 24 advanced HNmSCC patients prior receiving based treatment. show significantly increased...
6048 Background: Survival in recurrent/metastatic HNmSCC remain poor. PD-1 inhibitors have become standard of care, demonstrating improved overall survival and toxicity when compared to chemotherapy targeted therapy. Biomarkers such as PD-Ligand(L)1 combined proportion score (CPS) rudimentary, with CPS >20 showing a response rate only 23% pembrolizumab (KEYNOTE-048). We used high-dimensional imaging mass cytometry (IMC) explore predictive biomarkers pts receiving inhibitor-based Methods:...
Abstract Background Bacterial pathogens exhibit an impressive amount of genomic diversity. This diversity can be informative evolutionary adaptations, host-pathogen interactions, and disease transmission patterns. However, capturing this directly from biological samples is challenging. Results We introduce a framework for understanding the within-host pathogen using multi-locus sequence types (MLST) whole-genome sequencing (WGS) data. Our approach consists two stages. First we process each...
Abstract The efficacy of antibiotic drug treatments in tuberculosis (TB) is significantly threatened by the development resistance. There a need for robust diagnostic system that can accurately predict resistance patients. In recent years, researchers have been taking advantage whole-genome sequencing (WGS) data to infer this work we investigate power machine learning tools inferring from WGS on three distinct datasets differing their geographical diversity. We analyzed Relational Sequencing...
The advent of highly multiplexed in situ imaging cytometry assays has revolutionized the study cellular systems, offering unparalleled detail observing activities and characteristics. These provide comprehensive insights by concurrently profiling spatial distribution molecular features numerous cells. In navigating this complex data landscape, unsupervised machine learning techniques, particularly clustering algorithms, have become essential tools. They enable identification categorization...
Summary Acute myeloid leukemia (AML) is an aggressive cancer with very poor outcomes. To identify additional drivers of leukemogenesis, we analyzed sequence data from 1,727 unique individual AML patients, which revealed mutations in ubiquitin ligase family genes 11.2% adult samples mutual exclusivity. The Skp1/Cul1/Fbox (SCF) E3 complex gene FBXO11 was the most significantly downregulated SCF AML. catalyzes K63-linked ubiquitination a novel target, LONP1, promotes entry into mitochondria,...
Abstract Highly multiplexed in situ imaging cytometry assays have enabled researchers to scru-tinize cellular systems at an unprecedented level. With the capability of these simultaneously profile spatial distribution and molecular features many cells, unsuper-vised machine learning, particular clustering algorithms, become indispensable for identifying cell types subsets based on features. However, most widely used approaches applied novel technologies were developed suspension may not be...