Upcoming Dissertation: Reproducible Data Analysis in Drug Discovery with Scientific Workflows and the Semantic Web
29 Aug, 2018
On September 28, Samuel Lampa from the group will defend his thesis, titled:
“Reproducible Data Analysis in Drug Discovery with Scientific Workflows and the Semantic Web“
- Supervisor: Assoc Prof. Ola Spjuth, Uppsala University
- Co-supervisor: Prof. Roland Grafström, Karolinska Institutet
Time and location
- Time: September 28, 2018, 13:00 CET
- Location: Room B22, Biomedical Center (BMC), Husargatan 3, Uppsala, Sweden
See a map of the room’s location (Disregard the labels in this map)
The pharmaceutical industry is facing a research and development productivity crisis. At the same time we have access to more biological data than ever from recent advancements in high-throughput experimental methods. One suggested explanation for this apparent paradox has been that a crisis in reproducibility has affected also the reliability of datasets providing the basis for drug development. Advanced computing infrastructures can to some extent aid in this situation but also come with their own challenges, including increased technical debt and opaqueness from the many layers of technology required to perform computations and manage data. In this thesis, a number of approaches and methods for dealing with data and computations in early drug discovery in a reproducible way are developed. This has been done while striving for a high level of simplicity in their implementations, to improve understandability of the research done using them. Based on identified problems with existing tools, two workflow tools have been developed with the aim to make writing complex workflows particularly in predictive modelling more agile and flexible. One of the tools is based on the Luigi workflow framework, while the other is written from scratch in the Go language. We have applied these tools on predictive modelling problems in early drug discovery to create reproducible workflows for building predictive models, including for prediction of off-target binding in drug discovery. We have also developed a set of practical tools for working with linked data in a collaborative way, and publishing large-scale datasets in a semantic, machine-readable format on the web. These tools were applied on demonstrator use cases, and used for publishing large-scale chemical data. It is our hope that the developed tools and approaches will contribute towards practical, reproducible and understandable handling of data and computations in early drug discovery.
The primary opponent has gotten scheduling obstacles, and a new opponent is currently being assigned.
- Dr. Pär Matsson, Department of Pharmacy, Uppsala University. (Chair of the thesis ceremony.)
- Dr. Carl Nettelblad, Department of Information Technology, Uppsala University
- Dr. Manfred Grabherr, Department of Medical Biochemistry and Microbiology, Uppsala University
- Dr. Mikael Huss, Peltarion and Department of Learning, Informatics, Management and Ethics, Karolinska Institutet