Ola Spjuth
Professor vid Institutionen för farmaceutisk biovetenskap, Forskning; Farmaceutisk bioinformatik
- E-post:
- ola.spjuth[AT-tecken]farmbio.uu.se
- Telefon:
- 018-471 4681
- Mobiltelefon:
- 070-4250628
- Besöksadress:
- Rum BMC A3:2 Biomedicinskt centrum BMC, Husargatan 3
- Postadress:
- Box 591
751 24 UPPSALA
Kort presentation
Main research interests are in data-intensive bioinformatics and how automated high-throughput and high-content molecular and cell profiling technologies coupled with AI and predictive modeling on modern e-infrastructures can enable us to study complex phenomena in pharmacology, toxicology and metabolism.
Nyckelord: bioinformatics artificial intelligence machine learning pharmaceutical bioinformatics predictive modeling predictive toxicology predictive metabolism high-content imaging cell profiling cheminformatics
PhD in Bioinformatics from Uppsala University, 2009. Postdoctoral fellowships at Karolinska Institutet, Stockholm and Finnish Institute of Molecular Medicine (FIMM), Helsinki. Was co-director at the UPPMAX high performance computing center at Uppsala University (2010-2017), and headed the Bioinformatics Compute and Storage facility at Science for Life Laboratory in Sweden (2010-2017). Currently employed as Senior Lecturer at Department of Pharmaceutical Biosciences leading the research group in pharmaceutical bioinformatics. Main research interests are in data-intensive bioinformatics and how automated high-throughput and high-content molecular and cell profiling technologies coupled with AI and predictive modeling on modern e-infrastructures can enable us to study complex phenomena in pharmacology, toxicology and metabolism.
High-throughput technologies have transformed biomedicine into a data-intensive discipline. This has shifted the focus from traditional data generation and hypothesis testing to more data-driven research, and bioinformatics data analysis has become the bottleneck in many projects. However, the field is characterized by growing data sets and poorly scalable software, threatening to severely constrain many biomedical projects. Our group aims at developing new methods and applications to meet the demands of high-throughput biology and drug discovery, using high-throughput and cloud-based e-infrastructures and Big Data analytics.
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