Thematic area: Bioinformatics:
Precision medicine
Precise patient stratification is a prerequisite for successful use of targeted therapies and immune therapies. An increasing number of clinical studies aim to select targeted drugs for cancer patients based primarily on the molecular characteristics of the tumours, and improved molecular stratifications are greatly needed. Leveraging of prior knowledge resources (databases and ontologies) is frequently critical for successful results. A combination of unsupervised and supervised analyses is further typically required to improve classifications, and a major challenge is the limited number of cases available for training compared to the number of explanatory variables available. Over the last decade, a range of new statistical methods has been developed for class prediction based on high-dimensional covariates. Deep learning and novel machine learning methods in general are central keywords in this context. The research focus for this area is the adaptation and further development of such methodologies to facilitate clinical applications in the cancer field. This thematic area is based on existing strong interactions with Norways foremost cancer research institute (at Oslo University Hospital).
Keywords: Deep learning, cancer bioinformatics
Contact: Eivind Hovig (ehovig@ifi.uio.no) or Ole Christian Lingjærde (ole@ifi.uio.no)
3D genome modeling
Recent advances in high-throughput sequencing technologies allow for unprecedented characterization of the genome. Yet, most genomic studies ignore how DNA is dynamically organized in 3D space inside the nucleus. Such information is, however, crucial to understand gene (dys)-regulation in healthy and pathological states. Computational modelling and simulation have proven extremely fruitful to characterize 3D genome dynamics at multiple levels. Dynamic structural 3D models of whole genomes have revealed spatial and temporal regulation of Topologically Associated Domain (TAD) positioning during cell differentiation. At more local scales, loop-extrusion modelling can predict with high accuracy the effect of mutational alterations of boundaries between TADs. The research focus for this position will be to develop new computational methodology to explore the dynamics of 3D genomes in time – in essence providing a four-dimensional (4D) view of the genome. The research will synergize with existing efforts to explore how the 3D genome relates to cancer development and immune regulation across departments and groups at UiO.
Contact: Jonas Paulsen (jonaspau@ibv.uio.no)
Keywords: 3D genome, Hi-C, modeling, programming, simulations.
This is a funding mechanism offered in association with the Medical Faculty.For more information on the funding mechanism, visit the Scientia Fellows II programme - Call 2.