Michael Dausmann
A5052421956- Advanced Proteomics Techniques and Applications
- Bioinformatics and Genomic Networks
- Spectroscopy and Chemometric Analyses
- Metabolomics and Mass Spectrometry Studies
- Advanced Chemical Sensor Technologies
- Mass Spectrometry Techniques and Applications
- Machine Learning in Bioinformatics
- Cancer Genomics and Diagnostics
- Genetics, Bioinformatics, and Biomedical Research
- Ubiquitin and proteasome pathways
- AI in cancer detection
- Cancer Research and Treatments
- Artificial Intelligence in Healthcare and Education
The University of Sydney
2020-2024
Children's Medical Research Institute
2020-2024
Westmead Institute
2022
The proteome provides unique insights into disease biology beyond the genome and transcriptome. A lack of large proteomic datasets has restricted identification new cancer biomarkers. Here, proteomes 949 cell lines across 28 tissue types are analyzed by mass spectrometry. Deploying a workflow to quantify 8,498 proteins, these data capture evidence cell-type post-transcriptional modifications. Integrating multi-omics, drug response, CRISPR-Cas9 gene essentiality screens with deep...
Abstract Reproducible research is the bedrock of experimental science. To enable deployment large-scale proteomics, we assess reproducibility mass spectrometry (MS) over time and across instruments develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs eight samples containing known proportions ovarian prostate cancer tissue yeast, or control HEK293T cells. Replicates are run on six spectrometers operating continuously...
Abstract Motivation The output of electrospray ionization–liquid chromatography mass spectrometry (ESI-LC-MS) is influenced by multiple sources noise and major contributors can be broadly categorized as baseline, random chemical noise. Noise has a negative impact on the identification quantification peptides, which influences reliability reproducibility MS-based proteomics data. Most attempts at denoising have been made either spectra or chromatograms independently, thus, important 2D...
Summary The proteome provides unique insights into biology and disease beyond the genome transcriptome. Lack of large proteomic datasets has restricted identification new cancer biomarkers. Here, proteomes 949 cell lines across 28 tissue types were analyzed by mass spectrometry. Deploying a clinically-relevant workflow to quantify 8,498 proteins, these data capture evidence type post-transcriptional modifications. Integrating multi-omics, drug response CRISPR-Cas9 gene essentiality screens...
Abstract Artificial intelligence applications in biomedicine face major challenges from data privacy requirements. To address this issue for clinically annotated tissue proteomic data, we developed a Federated Deep Learning (FDL) approach (ProCanFDL), training local models on simulated sites containing pan-cancer cohort (n=1,260) and 29 cohorts held behind private firewalls (n=6,265), representing 19,930 replicate data-independent acquisition mass spectrometry (DIA-MS) runs. Local parameter...