AI-guided automated laboratory
The purpose of this project is to improve studies of toxicity mechanisms and pathways for environmental contaminants with intelligent data generation, using an automated cell lab controlled by an AI system.
Overview of the iterative, AI-controlled process. Based on a scientific question or hypothesis to test, the AI makes predictions on existing data (external data integrated with in-house data) and designs new experiments to improve the hypothesis testing. New data is obtained, models are improved, and the process is iterated until desired confidence reached or termination criteria fulfilled. Complementing the system is human-controlled confirmatory or exploratory experiments. The produced images, data and models will all be made available online.
As the number of potential harmful...
Long-read sequencing for clinical applications
Long-read sequencing is a new technology that offers important advantages for medical diagnostics as compared to the short-read sequencing technologies that are currently dominating the market. We have together with clinicians developed a data analysis product (CLAMP) and implemented the world’s first clinical routine diagnostics of leukemia mutations using long-read sequencing, and are now expanding to other applications.
The project aims to provide a complete informatics system for working with long-read sequencing in clinical diagnostics, including an automated analysis module, a searchable database, and a user interface. All components were developed in collaboration with clinicians.
Long-read single molecule sequencing (LR-SMS) is often called the “third generation” of DNA...
OpenRiskNet EU-H2020 project
OpenRiskNet is a 3 year project funded under the Horizon 2020 EINFRA-22-2016 Programme. The main objective is to provide an open e-Infrastructure providing resources and services to a variety of communities requiring risk assessment, including chemicals, cosmetic ingredients, therapeutic agents and nanomaterials. OpenRiskNet will work with a network of partners, organized within an Associated Partners Programme.
Toxicology and risk assessment are undergoing a paradigm shift, from a phenomenological to a mechanistic discipline based on in vitro and in silico approaches that represent an important alternative to classical animal testing applied to the evaluation of chronic and systemic toxicity risks. Large databases and highly sophisticated methods, algorithms and tools are available...
HASTE: Hierarchical Analysis of Spatial and TEmporal image data
From intelligent data acquisition via smart data-management to confident predictions
The HASTE project takes a hierarchical approach to acquisition, analysis, and interpretation of image data. We develop computationally efficient measurements for data description, confidence-driven machine learning for determination of interestingness, and a theory and framework to apply intelligent spatial and temporal information hierarchies, distributing data to computational resources and storage options based on low-level image features.
HASTE is a collaboration between the Wählby lab (PI), Hellander lab (co-PI), both at the Department of Information Technology, Uppsala University, the Spjuth lab (co-PI) at the Department of Pharmaceutical Biosciences, Uppsala University, the Nilsson lab at the...
Large-scale Predictive Modelling in Drug Discovery
This project aims at developing computational methods, tools and predictive models to aid the drug discovery process on large data sets. Methods include ligand-based and structure-based methods such as QSAR (machine learning) and docking, with applications including prediction of drug safety, toxicology, interactions, target profiles and secondary pharmacology. In order to analyze large-scale data we make use of modern e-infrastructure such as high-performance computing clusters, cloud computing resources, containerized microservice environments such as Kubernetes, and data analytics platforms such as Apache Spark.
Figure: Data is extracted from various data sources, and we use high performance computing, cloud computing, workflows and big data frameworks to train predictive models...
Prediction of metabolism
This project aims at developing methods for predicting site-of-metabolism and metabolites based on chemical structure. Using data mining techniques we have developed the tool MetaPrint2D for site-of-metabolism prediction. The project aims at improving these models and also to predict putative metabolites. The work is carried out in close collaboration with AstraZeneca R&D and models and tools are available from the Bioclipse workbench.
Figure: Prediction of site-of-metabolism with the MetaPrint2D method in Bioclipse.
PhenoMeNal EU-H2020 project
PhenoMeNal is a 3-year EU Horizon 2020 project starting on September 1st 2015 and will develop a standardised e-infrastructure for analysing medical metabolic phenotype data. This comprises development of standards for data exchange, pipelines, computational frameworks and resources for the processing, analysis and information-mining of the massive amount of medical molecular phenotyping and genotyping data that will be generated by metabolomics applications now entering research and clinic.
Ola Spjuth leads Work Package 5: “Maintenance and Operation of PhenoMeNal grid/cloud e-Infrastructure”.
Project website: http://phenomenal-h2020.eu
e-Science for Cancer Prevention and Control
The SeRC flagship project e-Science for Cancer Prevention and Control (eCPC) will set up a modular system for prediction of cancer initiation and progression. It will be based on computational models that integrate data from different sources, including molecular (e.g. genomic, proteomic), environmental and life-style factors. By superimposing screening and prevention strategies on the models, reduced over-treatment, morbidity, mortality and cost can be quantified.
Ola Spjuth leads WP1 (data management and integration) and is also member of the management group.
eCPC Website: http://ecpc.e-science.se