Ola Spjuth
Professor at Department of Pharmaceutical Biosciences; Research; Pharmaceutical Bioinformatics
- Telephone:
- +46 18 471 46 81
- Mobile phone:
- +46 70 425 06 28
- E-mail:
- ola.spjuth@uu.se
- Visiting address:
- Biomedicinskt centrum BMC, Husargatan 3
- Postal address:
- Box 591
751 24 UPPSALA
- ORCID:
- 0000-0002-8083-2864
More information is available to staff who log in.
Short presentation
Research interests includes data-intensive and translational bioinformatics with a particular focus on how modern e-infrastructures enables the studying of complex phenomena, and predictive modeling in pharmacology, toxicology, and metabolism.
Keywords
- artificial intelligence
- bioinformatics
- cell profiling
- cheminformatics
- high-content imaging
- machine learning
- pharmaceutical bioinformatics
- predictive metabolism
- predictive modeling
- predictive toxicology
Biography
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.
Research
The Pharmaceutical Bioinformatics research group focuses on mathematical and statistical modeling, informatics and quantitative analysis of pharmacological systems. We develop methods, algorithms and software to study and model pharmaceutical interactions, and a key focus in the group is how predictive modeling, large-scale calculations and modern e-infrastructure (such as high-performance and cloud computing) can aid the drug discovery process; e.g. when studying drug toxicity, metabolism and resistance. We are involved in several national and international consortia and have a tight connection to the pharmaceutical industry, Uppsala University Hospital, and Science for Life Laboratory. See my research group website for more information ab
Publications
Selection of publications
- Designing microplate layouts using artificial intelligence (2023)
- Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction (2022)
- Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects (2022)
- Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection (2022)
- Predicting With Confidence (2021)
- Machine Learning Strategies When Transitioning between Biological Assays (2021)
- Deep-learning models for lipid nanoparticle-based drug delivery (2021)
- A phenomics approach for antiviral drug discovery (2021)
Recent publications
- CPSign (2024)
- Artificial intelligence for high content imaging in drug discovery (2024)
- Federated learning for predicting compound mechanism of action based on image-data from cell painting (2024)
- From pixels to phenotypes (2024)
- Insights into Drug Cardiotoxicity from Biological and Chemical Data (2024)
All publications
Articles
- CPSign (2024)
- Artificial intelligence for high content imaging in drug discovery (2024)
- Federated learning for predicting compound mechanism of action based on image-data from cell painting (2024)
- From pixels to phenotypes (2024)
- Insights into Drug Cardiotoxicity from Biological and Chemical Data (2024)
- Improved Detection of Drug-Induced Liver Injury by Integrating Predicted In Vivo and In Vitro Data (2024)
- New approach methods to assess developmental and adult neurotoxicity for regulatory use (2024)
- Data management of scientific applications in a reinforcement learning-based hierarchical storage system (2024)
- Development of new approach methods for the identification and characterization of endocrine metabolic disruptors (2023)
- In Silico Prediction of Human Clinical Pharmacokinetics with ANDROMEDA by Prosilico (2023)
- Designing microplate layouts using artificial intelligence (2023)
- Evaluating the utility of brightfield image data for mechanism of action prediction (2023)
- Disease phenotype prediction in multiple sclerosis (2023)
- Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data (2023)
- Combining molecular and cell painting image data for mechanism of action prediction (2023)
- In silico predictions of the human pharmacokinetics/toxicokinetics of 65 chemicals from various classes using conformal prediction methodology (2022)
- In Silico Predictions of the Gastrointestinal Uptake of Macrocycles in Man Using Conformal Prediction Methodology (2022)
- The Impact of Reference Data Selection for the Prediction Accuracy of Intrinsic Hepatic Metabolic Clearance (2022)
- SimVec (2022)
- Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction (2022)
- An Open-Source Modular Framework for Automated Pipetting and Imaging Applications (2022)
- A method for Boolean analysis of protein interactions at a molecular level (2022)
- Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects (2022)
- Migrating to Long-Read Sequencing for Clinical Routine BCR-ABL1 TKI Resistance Mutation Screening (2022)
- Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection (2022)
- From biomedical cloud platforms to microservices (2022)
- Predicting protein network topology clusters from chemical structure using deep learning (2022)
- Predicting With Confidence (2021)
- Machine Learning Strategies When Transitioning between Biological Assays (2021)
- Metabolomics (2021)
- Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit (2021)
- In silico prediction of volume of distribution of drugs in man using conformal prediction performs on par with animal data-based models (2021)
- Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology (2021)
- Comparison between lab variability and in silico prediction errors for the unbound fraction of drugs in human plasma (2021)
- Synergy Conformal Prediction (2021)
- Deep-learning models for lipid nanoparticle-based drug delivery (2021)
- scConnect (2021)
- Integrating Statistical and Machine-Learning Approach for Meta-Analysis of Bisphenol A-Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival (2021)
- ELIXIR and Toxicology (2021)
- Assessing the calibration in toxicological in vitro models with conformal prediction (2021)
- Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning (2021)
- A phenomics approach for antiviral drug discovery (2021)
- Approaches for containerized scientific workflows in cloud environments with applications in life science (2021)
- The machine learning life cycle and the cloud (2021)
- Deep learning and conformal prediction for hierarchical analysis of large-scale whole-slide tissue images (2021)
- Predicting target profiles with confidence as a service using docking scores (2020)
- MaRe (2020)
- Using Predicted Bioactivity Profiles to Improve Predictive Modeling (2020)
- Towards reproducible computational drug discovery (2020)
- On-demand virtual research environments using microservices (2019)
- Interoperable and scalable data analysis with microservices (2019)
- Deep Learning in Image Cytometry (2019)
- Alterations in the tyrosine and phenylalanine pathways revealed by biochemical profiling in cerebrospinal fluid of Huntington's disease subjects (2019)
- Biochemical Differences in Cerebrospinal Fluid between Secondary Progressive and Relapsing-Remitting Multiple Sclerosis (2019)
- Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes (2019)
- SciPipe - Turning Scientific Workflows into Computer Programs (2019)
- SciPipe (2019)
- Container-based bioinformatics with Pachyderm (2019)
- PhenoMeNal (2019)
- Efficient iterative virtual screening with Apache Spark and conformal prediction (2018)
- Tracking the NGS revolution (2018)
- OpenRiskNet, an open e-infrastructure to support data sharing, knowledge integration and in silico analysis and modelling in risk assessment (2018)
- Exploring the usefulness of morphological profiling of cells to study toxicity mechanisms (2018)
- Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis. (2018)
- Evaluating parameters for ligand-based modeling with random forest on sparse data sets (2018)
- Predicting off-target binding profiles with confidence using Conformal Prediction (2018)
- A confidence predictor for logD using conformal regression and a support-vector machine (2018)
- OpenRiskNet, an open e-infrastructure to support data sharing, knowledge integration, in silico analysis and modelling in risk assessment (2018)
- Novel applications of Machine Learning in cheminformatics (2018)
- Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty (2018)
- Large-scale virtual screening on public cloud resources with Apache Spark (2017)
- Mass spectrometry based metabolomics for in vitro systems pharmacology (2017)
- RDFIO (2017)
- Towards Predicting the Cytochrome P450 Modulation (2017)
- E-Science technologies in a workflow for personalized medicine using cancer screening as a case study (2017)
- The future of metabolomics in ELIXIR. (2017)
- The Chemistry Development Kit (CDK) v2.0 (2017)
- Large-scale ligand-based predictive modelling using support vector machines (2016)
- Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles (2016)
- Origin of aromatase inhibitory activity via proteochemometric modeling (2016)
- Recommendations on e-infrastructures for next-generation sequencing (2016)
- Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research (2016)
- XMetDB (2016)
- BioImg.org (2015)
- Privacy-Preservation for Publishing Sample Availability Data with Personal Identifiers (2015)
- Toward the Replacement of Animal Experiments through the Bioinformatics-driven Analysis of 'Omics' Data from Human Cell Cultures (2015)
- A quantitative assessment of the Hadoop framework for analyzing massively parallel DNA sequencing data (2015)
- Experiences with workflows for automating data-intensive bioinformatics (2015)
- Scaling predictive modeling in drug development with cloud computing (2015)
- Benchmarking Study of Parameter Variation When Using Signature Fingerprints Together with Support Vector Machines (2014)
- Ligand-Based Target Prediction with Signature Fingerprints (2014)
- Cancer Biology, Toxicology and Alternative Methods Development Go Hand-in-Hand (2014)
- On Mechanisms of Reactive Metabolite Formation from Drugs (2013)
- Lessons learned from implementing a national infrastructure in Sweden for storage and analysis of next-generation sequencing data (2013)
- A Unified Proteochemometric Model for Prediction of Inhibition of Cytochrome P450 Isoforms (2013)
- WhichCyp (2013)
- Automated QuantMap for rapid quantitative molecular network topology analysis (2013)
- Applications of the InChI in cheminformatics with the CDK and Bioclipse (2013)
- Bioclipse-R (2013)
- The ChEMBL database as linked open data (2013)
- Model building in Bioclipse Decision Support applied to open datasets (2012)
- Toxicology Ontology Perspectives (2012)
- Food for thought... (2012)
- UPPNEX (2012)
- Open source drug discovery with Bioclipse (2012)
- A novel infrastructure for chemical safety predictions with focus on human health (2012)
- Accessing, using, and creating chemical property databases for computational toxicology modeling (2012)
- Computational toxicology using OpenTox & Bioclipse (2012)
- Brunn (2011)
- Open Data, Open Source and Open Standards in chemistry (2011)
- Integrated Decision Support for Assessing Chemical Liabilities (2011)
- Services for prediction of drug susceptibility for HIV proteases and reverse transcriptases at the HIV Drug Research Centre (2011)
- Linking the Resource Description Framework to cheminformatics and proteochemometrics (2011)
- Computational toxicology using the OpenTox application programming interface and Bioclipse (2011)
- Use of Historic Metabolic Biotransformation Data as a Means of Anticipating Metabolic Sites Using MetaPrint2D and Bioclipse (2010)
- An eScience-Bayes strategy for analyzing omics data (2010)
- Proteochemometric Modeling of the Susceptibility of Mutated Variants of the HIV-1 Virus to Reverse Transcriptase Inhibitors (2010)
- Towards interoperable and reproducible QSAR analyses (2010)
- Bioclipse 2.0: Life Science setzt auf die Staerken von Eclipse. (2009)
- Bioclipse 2 (2009)
- Bioclipse 2: A scriptable integration platform for the life sciences (2009)
- XMPP for cloud computing in bioinformatics supporting discovery and invocation of asynchronous web services (2009)
- The C1C2 (2008)
- Proteochemometric modeling of HIV protease susceptibility (2008)
- Bioclipse (2007)
- The LCB Data Warehouse (2006)
- Eine visuelle Open-Source-Platform für Chemo- und Bioinformatik (2006)
- Using Bioclipse to integrate bioinformatics functionality (2005)
- An Information System for Proteochemometrics (2005)
- AROS: Open Source Lab Automation Enables Fully Automated CellPainting
- Synergy Conformal Prediction
- Synergy Conformal Prediction for Regression
- Robust Knowledge Transfer in Learning Under Privileged Information Framework
- Exploring the evolution of cellular morphological changes after drug administration based on brightfield image data
- A biochemical signature of progressive multiple sclerosis
- Using Iterative MapReduce for Parallel Virtual Screening
- Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud
- Pharmaceutical Bioinformatics Primer
Books
Chapters
- Data Integration between Swedish National Clinical Health Registries and Biobanks Using an Availability System (2014)
- Chemoinformatics taking Biology into Account (2012)
- Collaborative Cheminformatics Applications (2011)
Conferences
- Scalable federated machine learning with FEDn (2022)
- Is brightfield all you need for MoA prediction? (2022)
- Synergy Conformal Prediction for Regression (2021)
- A Constraint Programming Approach to Microplate Layout Design (2020)
- Smart Resource Management for Data Streaming using an Online Bin-packing Strategy (2020)
- Split knowledge transfer in learning under privileged information framework (2019)
- Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets (2019)
- Prediction of Metabolic Transformations using Cross Venn-ABERS Predictors (2017)
- SNIC Science Cloud (SSC) (2017)
- Interpretation of Conformal Prediction Classification Models (2015)
- Conformal prediction in Spark (2015)
- HTSeq-Hadoop (2014)
- NGS data management and analysis for hundreds of projects: Experiences from Sweden (2013)