Keynote Talk by Marta MILO, University of Sheffield (UK)
<Bioinformatics, Computational Biology, gene expression analysis, microRNA, Next Generation Sequencing>
Title: Bring Mathematics into Biology: past, present and future impact on Heath
Abstract: Last decade has seen a massive increase of data production in science. Particularly in the biomedical field, data has grown exponentially thanks to the development of technologies like next generation sequencing and high-throughput quantitative assays. The information that this data contains is only partially uncovered to this date, but the impact that it has on human progression and well being is already very clear.
Despite the ability to process large amount of data and to quantify fine details of biological processes, the costs, the time to perform such experiments and mainly the complexity of the systems remain in some cases still very prohibitive. For this reasons the use of mathematics to study complex systems in its entirety, looking at how they interacts, is having a great impact in current biology and healthcare. A variety of statistical, probabilistic and optimization techniques methods, like machine learning techniques, that allows to “learn” from the available data, to detect hidden patterns from large, noisy and complex datasets, is particularly suitable for application in medicine.
In this talk I will present examples of using machine learning techniques for a variety datasets from medical and biological problems and what are the advantages and disadvantages of this approach. I will also give examples when these techniques enabled to discover informative knowledge from a large complex system in the presence of small number of samples. Finally I will discuss how we use Machine Learning today for analysis of single-cell sequencing data and how we can use it for future more complex datasets generated integrating data from different sources.
Bio: Marta Milo is Lecturer in Computational Biology at the Department of Biomedical Science and is group leader at the Centre for Stem Cell Biology at the University of Sheffield. She was a Bioinformatics research fellow at the Sheffield Teaching Hospitals NHS Trust. She holds a PhD in Applied Mathematics and Computer Science from the University of Naples. The main focus of her professional career has been to develop truly interdisciplinary skills, complementing and refining her bioinformatics skills with a deep understanding of the biological nature of the data collected. This is to better identify limitations in the experimental designs and better quantify variations in the data collection and validation. Her work has been concentrating on the analysis and interpretation of high-throughput biological data, with the aim to produce feasible and robust hypotheses for a deeper understanding of the biological systems under study. In quantitative sciences numerical knowledge is not enough to understand and predict systems behaviours that are only partially observed. Since the beginning of 20th century it was clear that predictions of data required an additional “knowledge” to become meaningful. This knowledge needed to be quantified in a way that reflects our prior knowledge of the systems and what we were able to measure. It signed the start of introducing the concept of quantified uncertainty. Marta’s research interests focus on developing computational tools, pipelines, appropriate experimental designs and protocols to assist in improving accuracy and sensitivity in the analysis of biological data.