Brian Lane, Molecular & Clinical Cancer 'Mining bioinformatics data sets to improve outcomes.' Host: George Bou-Gharios
12:45pm - 1:45pm /
Monday 25th June 2018
/ Venue: Ground floor, William Henry Duncan Apex Building
- 0151 794 9003
- Brenda Smith
- Suitable for: Staff and students
- Admission: Free to staff and students no need to register.
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Clinical and biological researchers are increasingly accumulating large ‘omics (proteomics, genomics, transcriptomics, metabolomics) data sets with the aim of better understanding disease processes or improving disease outcomes. Appropriate bioinformatics support is needed for the design, processing, analysis and interpretation of such studies. Even when proprietary software is used for platform specific analysis, local bioinformatics support can be necessary to quickly generate useful outcomes. In addition, the application of data science techniques can yield further insight.
With over ten years of experience in clinical bioinformatics at Liverpool University, I will present examples of studies where dedicated bioinformatics support and data mining has significantly improved project outcomes.
A PhD Microbiologist (Manchester, 1990), I graduated from the MSc Bioinformatics course at Manchester in 2002 and have worked as a bioinformatician in Liverpool University since then. My principle experience is with microarray, NGS and proteomics platforms and I have a particular interest in developing machine learning techniques for biomarker development.
With over ten years of experience in clinical bioinformatics at Liverpool University, I will present examples of studies where dedicated bioinformatics support and data mining has significantly improved project outcomes.
A PhD Microbiologist (Manchester, 1990), I graduated from the MSc Bioinformatics course at Manchester in 2002 and have worked as a bioinformatician in Liverpool University since then. My principle experience is with microarray, NGS and proteomics platforms and I have a particular interest in developing machine learning techniques for biomarker development.