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Dive into the research topics where Jonathan Alvarsson is active.

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Featured researches published by Jonathan Alvarsson.


BMC Bioinformatics | 2009

Bioclipse 2: A scriptable integration platform for the life sciences

Ola Spjuth; Jonathan Alvarsson; Arvid Berg; Martin Eklund; Stefan Kuhn; Carl Mäsak; gilleain torrance; Johannes Wagener; Egon Willighagen; Christoph Steinbeck; Jarl E. S. Wikberg

BackgroundContemporary biological research integrates neighboring scientific domains to answer complex questions in fields such as systems biology and drug discovery. This calls for tools that are intuitive to use, yet flexible to adapt to new tasks.ResultsBioclipse is a free, open source workbench with advanced features for the life sciences. Version 2.0 constitutes a complete rewrite of Bioclipse, and delivers a stable, scalable integration platform for developers and an intuitive workbench for end users. All functionality is available both from the graphical user interface and from a built-in novel domain-specific language, supporting the scientist in interdisciplinary research and reproducible analyses through advanced visualization of the inputs and the results. New components for Bioclipse 2 include a rewritten editor for chemical structures, a table for multiple molecules that supports gigabyte-sized files, as well as a graphical editor for sequences and alignments.ConclusionBioclipse 2 is equipped with advanced tools required to carry out complex analysis in the fields of bio- and cheminformatics. Developed as a Rich Client based on Eclipse, Bioclipse 2 leverages on todays powerful desktop computers for providing a responsive user interface, but also takes full advantage of the Web and networked (Web/Cloud) services for more demanding calculations or retrieval of data. The fact that Bioclipse 2 is based on an advanced and widely used service platform ensures wide extensibility, making it easy to add new algorithms, visualizations, as well as scripting commands. The intuitive tools for end users and the extensible architecture make Bioclipse 2 ideal for interdisciplinary and integrative research.Bioclipse 2 is released under the Eclipse Public License (EPL), a flexible open source license that allows additional plugins to be of any license. Bioclipse 2 is implemented in Java and supported on all major platforms; Source code and binaries are freely available at http://www.bioclipse.net.


Journal of Cheminformatics | 2011

Open Data, Open Source and Open Standards in chemistry: The Blue Obelisk five years on

Noel M. O'Boyle; Rajarshi Guha; Egon Willighagen; Samuel E. Adams; Jonathan Alvarsson; Jean-Claude Bradley; Igor V. Filippov; Robert M. Hanson; Marcus D. Hanwell; Geoffrey R. Hutchison; Craig A James James; Nina Jeliazkova; Andrew S. I. D. Lang; Karol M. Langner; David C. Lonie; Daniel M. Lowe; Jérôme Pansanel; Dmitry Pavlov; Ola Spjuth; Christoph Steinbeck; Kevin J. Theisen; Peter Murray-Rust

BackgroundThe Blue Obelisk movement was established in 2005 as a response to the lack of Open Data, Open Standards and Open Source (ODOSOS) in chemistry. It aims to make it easier to carry out chemistry research by promoting interoperability between chemistry software, encouraging cooperation between Open Source developers, and developing community resources and Open Standards.ResultsThis contribution looks back on the work carried out by the Blue Obelisk in the past 5 years and surveys progress and remaining challenges in the areas of Open Data, Open Standards, and Open Source in chemistry.ConclusionsWe show that the Blue Obelisk has been very successful in bringing together researchers and developers with common interests in ODOSOS, leading to development of many useful resources freely available to the chemistry community.


Journal of Biomedical Semantics | 2011

Linking the Resource Description Framework to cheminformatics and proteochemometrics.

Egon Willighagen; Jonathan Alvarsson; A. Andersson; Martin Eklund; Samuel Lampa; Maris Lapins; Ola Spjuth; Jarl E. S. Wikberg

BackgroundSemantic web technologies are finding their way into the life sciences. Ontologies and semantic markup have already been used for more than a decade in molecular sciences, but have not found widespread use yet. The semantic web technology Resource Description Framework (RDF) and related methods show to be sufficiently versatile to change that situation.ResultsThe work presented here focuses on linking RDF approaches to existing molecular chemometrics fields, including cheminformatics, QSAR modeling and proteochemometrics. Applications are presented that link RDF technologies to methods from statistics and cheminformatics, including data aggregation, visualization, chemical identification, and property prediction. They demonstrate how this can be done using various existing RDF standards and cheminformatics libraries. For example, we show how IC50 and Ki values are modeled for a number of biological targets using data from the ChEMBL database.ConclusionsWe have shown that existing RDF standards can suitably be integrated into existing molecular chemometrics methods. Platforms that unite these technologies, like Bioclipse, makes this even simpler and more transparent. Being able to create and share workflows that integrate data aggregation and analysis (visual and statistical) is beneficial to interoperability and reproducibility. The current work shows that RDF approaches are sufficiently powerful to support molecular chemometrics workflows.


Journal of Chemical Information and Modeling | 2014

Ligand-Based Target Prediction with Signature Fingerprints

Jonathan Alvarsson; Martin Eklund; Ola Engkvist; Ola Spjuth; Lars Carlsson; Jarl E. S. Wikberg; Tobias Noeske

When evaluating a potential drug candidate it is desirable to predict target interactions in silico prior to synthesis in order to assess, e.g., secondary pharmacology. This can be done by looking at known target binding profiles of similar compounds using chemical similarity searching. The purpose of this study was to construct and evaluate the performance of chemical fingerprints based on the molecular signature descriptor for performing target binding predictions. For the comparison we used the area under the receiver operating characteristics curve (AUC) complemented with net reclassification improvement (NRI). We created two open source signature fingerprints, a bit and a count version, and evaluated their performance compared to a set of established fingerprints with regards to predictions of binding targets using Tanimoto-based similarity searching on publicly available data sets extracted from ChEMBL. The results showed that the count version of the signature fingerprint performed on par with well-established fingerprints such as ECFP. The count version outperformed the bit version slightly; however, the count version is more complex and takes more computing time and memory to run so its usage should probably be evaluated on a case-by-case basis. The NRI based tests complemented the AUC based ones and showed signs of higher power.


Journal of Chemical Information and Modeling | 2014

Benchmarking study of parameter variation when using signature fingerprints together with support vector machines.

Jonathan Alvarsson; Martin Eklund; Claes Andersson; Lars Carlsson; Ola Spjuth; Jarl E. S. Wikberg

QSAR modeling using molecular signatures and support vector machines with a radial basis function is increasingly used for virtual screening in the drug discovery field. This method has three free parameters: C, γ, and signature height. C is a penalty parameter that limits overfitting, γ controls the width of the radial basis function kernel, and the signature height determines how much of the molecule is described by each atom signature. Determination of optimal values for these parameters is time-consuming. Good default values could therefore save considerable computational cost. The goal of this project was to investigate whether such default values could be found by using seven public QSAR data sets spanning a wide range of end points and using both a bit version and a count version of the molecular signatures. On the basis of the experiments performed, we recommend a parameter set of heights 0 to 2 for the count version of the signature fingerprints and heights 0 to 3 for the bit version. These are in combination with a support vector machine using C in the range of 1 to 100 and γ in the range of 0.001 to 0.1. When data sets are small or longer run times are not a problem, then there is reason to consider the addition of height 3 to the count fingerprint and a wider grid search. However, marked improvements should not be expected.


Journal of Laboratory Automation | 2016

Ex Vivo Assessment of Drug Activity in Patient Tumor Cells as a Basis for Tailored Cancer Therapy.

Kristin Blom; Peter Nygren; Jonathan Alvarsson; Rolf Larsson; Claes Andersson

Although medical cancer treatment has improved during the past decades, it is difficult to choose between several first-line treatments supposed to be equally active in the diagnostic group. It is even more difficult to select a treatment after the standard protocols have failed. Any guidance for selection of the most effective treatment is valuable at these critical stages. We describe the principles and procedures for ex vivo assessment of drug activity in tumor cells from patients as a basis for tailored cancer treatment. Patient tumor cells are assayed for cytotoxicity with a panel of drugs. Acoustic drug dispensing provides great flexibility in the selection of drugs for testing; currently, up to 80 compounds and/or combinations thereof may be tested for each patient. Drug response predictions are obtained by classification using an empirical model based on historical responses for the diagnosis. The laboratory workflow is supported by an integrated system that enables rapid analysis and automatic generation of the clinical referral response.


Current Topics in Medicinal Chemistry | 2012

Open Source Drug Discovery with Bioclipse

Ola Spjuth; Lars Carlsson; Jonathan Alvarsson; Valentin Georgiev; Egon Willighagen; Martin Eklund

We present the open source components for drug discovery that has been developed and integrated into the graphical workbench Bioclipse. Building on a solid open source cheminformatics core, Bioclipse has advanced functionality for managing and visualizing chemical structures and related information. The features presented here include QSAR/QSPR modeling, various predictive solutions such as decision support for chemical liability assessment, site-ofmetabolism prediction, virtual screening, and knowledge discovery and integration. We demonstrate the utility of the described tools with examples from computational pharmacology, toxicology, and ADME. Bioclipse is used in both academia and industry, and is a good example of open source leading to new solutions for drug discovery.


Journal of Cheminformatics | 2016

Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles

Samuel Lampa; Jonathan Alvarsson; Ola Spjuth

Abstract Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.Graphical abstract.


Journal of Chemical Information and Modeling | 2015

Scaling predictive modeling in drug development with cloud computing

Behrooz Torabi Moghadam; Jonathan Alvarsson; Marcus Holm; Martin Eklund; Lars Carlsson; Ola Spjuth

Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.


BMC Bioinformatics | 2011

Brunn: an open source laboratory information system for microplates with a graphical plate layout design process.

Jonathan Alvarsson; Claes Andersson; Ola Spjuth; Rolf Larsson; Jarl E. S. Wikberg

BackgroundCompound profiling and drug screening generates large amounts of data and is generally based on microplate assays. Current information systems used for handling this are mainly commercial, closed source, expensive, and heavyweight and there is a need for a flexible lightweight open system for handling plate design, and validation and preparation of data.ResultsA Bioclipse plugin consisting of a client part and a relational database was constructed. A multiple-step plate layout point-and-click interface was implemented inside Bioclipse. The system contains a data validation step, where outliers can be removed, and finally a plate report with all relevant calculated data, including dose-response curves.ConclusionsBrunn is capable of handling the data from microplate assays. It can create dose-response curves and calculate IC50 values. Using a system of this sort facilitates work in the laboratory. Being able to reuse already constructed plates and plate layouts by starting out from an earlier step in the plate layout design process saves time and cuts down on error sources.

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Samuel Lampa

Science for Life Laboratory

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