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  • 1.
    Alvarsson, Jonathan
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Andersson, Claes
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Carlsson, Lars
    AstraZeneca R&D.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wikberg, Jarl E. S.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Benchmarking Study of Parameter Variation When Using Signature Fingerprints Together with Support Vector Machines2014In: Journal of Chemical Information and Modeling, ISSN 1549-9596, Vol. 54, no 11, p. 3211-3217Article in journal (Refereed)
    Abstract [en]

    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, ?, and signature height. C is a penalty parameter that limits overfitting, ? 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 gamma 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.

  • 2.
    Alvarsson, Jonathan
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Engkvist, Ola
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Carlsson, Lars
    Wikberg, Jarl E. S.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Noeske, Tobias
    Ligand-Based Target Prediction with Signature Fingerprints2014In: Journal of Chemical Information and Modeling, ISSN 1549-9596, Vol. 54, no 10, p. 2647-2653Article in journal (Refereed)
    Abstract [en]

    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.

  • 3.
    Carlsson, Lars
    et al.
    AstraZeneca R&D.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Boyer, Scott
    AstraZeneca R&D.
    Model building in Bioclipse Decision Support applied to open datasets2012In: Toxicology Letters, ISSN 0378-4274, E-ISSN 1879-3169, Vol. 211, no Suppl., p. S62-Article in journal (Refereed)
    Abstract [en]

    Bioclipse Decision Support (DS) is a system capable of building predictive models of any collection of SAR data, and making them available in a simple user interface based on Bioclipse (www.bioclipse.net).

    The method is fast and uses Faulon Signatures as chemical descriptors together with a Support Vector Machine algorithm for QSAR model building. A key feature is the capability to visualize and interpret results by highlighting the substructures which contributed most to the prediction. This, together with very fast predictions, allows for editing chemical structures with instantly updated results.

    We here present the results from applying Bioclipse Decision Support to several open QSAR data sets, including endpoints from OpenTox and PubChem. The results show how to extract data from the sources and to build models which can be integrated with user specific models.

  • 4.
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    eScience Approaches to Model Selection and Assessment: Applications in Bioinformatics2009Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    High-throughput experimental methods, such as DNA and protein microarrays, have become ubiquitous and indispensable tools in biology and biomedicine, and the number of high-throughput technologies is constantly increasing. They provide the power to measure thousands of properties of a biological system in a single experiment and have the potential to revolutionize our understanding of biology and medicine. However, the high expectations on high-throughput methods are challenged by the problem to statistically model the wealth of data in order to translate it into concrete biological knowledge, new drugs, and clinical practices. In particular, the huge number of properties measured in high-throughput experiments makes statistical model selection and assessment exigent. To use high-throughput data in critical applications, it must be warranted that the models we construct reflect the underlying biology and are not just hypotheses suggested by the data. We must furthermore have a clear picture of the risk of making incorrect decisions based on the models.

    The rapid improvements of computers and information technology have opened up new ways of how the problem of model selection and assessment can be approached. Specifically, eScience, i.e. computationally intensive science that is carried out in distributed network envi- ronments, provides computational power and means to efficiently access previously acquired scientific knowledge. This thesis investigates how we can use eScience to improve our chances of constructing biologically relevant models from high-throughput data. Novel methods for model selection and assessment that leverage on computational power and on prior scientific information to "guide" the model selection to models that a priori are likely to be relevant are proposed. In addition, a software system for deploying new methods and make them easily accessible to end users is presented.

    Download full text (pdf)
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  • 5.
    Eklund, Martin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Norinder, U.
    Boyer, S.
    Carlsson, L.
    Application of conformal prediction in QSAR2012In: Artificial Intelligence Applications and Innovations: AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Halkidiki, Greece, September 27-30, 2012, Proceedings, Part II / [ed] Lazaros Iliadis, Ilias Maglogiannis, Harris Papadopoulos, Kostas Karatzas, Spyros Sioutas, 2012, no PART 2, p. 166-175Conference paper (Refereed)
    Abstract [en]

    QSAR modeling is a method for predicting properties, e.g. the solubility or toxicity, of chemical compounds using statistical learning techniques. QSAR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testing or to alert for potential toxicity. However, predictions from a QSAR model are difficult to assess if their prediction intervals are unknown. In this paper we introduce conformal prediction into the QSAR field to address this issue. We apply support vector machine regression in combination with two nonconformity measures to five datasets of different sizes to demonstrate the usefulness of conformal prediction in QSAR modeling. One of the nonconformity measures provides prediction intervals with almost the same width as the size of the QSAR models' prediction errors, showing that the prediction intervals obtained by conformal prediction are efficient and useful.

  • 6.
    Eklund, Martin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Norinder, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Boyer, Scott
    Carlsson, Lars
    Benchmarking Variable Selection in QSAR2012In: Molecular Informatics, ISSN 1868-1743, Vol. 31, no 2, p. 173-179Article in journal (Refereed)
    Abstract [en]

    Variable selection is important in QSAR modeling since it can improve model performance and transparency, as well as reduce the computational cost of model fitting and predictions. Which variable selection methods that perform well in QSAR settings is largely unknown. To address this question we, in a total of 1728 benchmarking experiments, rigorously investigated how eight variable selection methods affect the predictive performance and transparency of random forest models fitted to seven QSAR datasets covering different endpoints, descriptors sets, types of response variables, and number of chemical compounds. The results show that univariate variable selection methods are suboptimal and that the number of variables in the benchmarked datasets can be reduced with about 60?% without significant loss in model performance when using multivariate adaptive regression splines MARS and forward selection.

  • 7.
    Eklund, Martin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Norinder, Ulf
    Boyer, Scott
    Carlsson, Lars
    Choosing Feature Selection and Learning Algorithms in QSAR2014In: J CHEM INF MODEL, ISSN 1549-9596, Vol. 54, no 3, p. 837-843Article in journal (Refereed)
    Abstract [en]

    Feature selection is an important part of contemporary QSAR analysis. In a recently published paper, we investigated the performance of different feature selection methods in a large number of in silico experiments conducted using real QSAR datasets. However, an interesting question that we did not address is whether certain feature selection methods are better than others in combination with certain learning methods, in terms of producing models with high prediction accuracy. In this report we extend our work from the previous investigation by using four different feature selection methods (wrapper, ReliefF, MARS, and elastic nets), together with eight learners (MARS, elastic net, random forest, SVM, neural networks, multiple linear regression, PLS, kNN) in an empirical investigation to address this question. The results indicate that state-of-the-art learners (random forest, SVM, and neural networks) do not gain prediction accuracy from feature selection, and we found no evidence that a certain feature selection is particularly well-suited for use in combination with a certain learner.

  • 8.
    Eklund, Martin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Norinder, Ulf
    Boyer, Scott
    Carlsson, Lars
    The application of conformal prediction to the drug discovery process2015In: Annals of Mathematics and Artificial Intelligence, ISSN 1012-2443, E-ISSN 1573-7470, Vol. 74, no 1-2, p. 117-132Article in journal (Refereed)
    Abstract [en]

    QSAR modeling is a method for predicting properties, e.g. the solubility or toxicity, of chemical compounds using machine learning techniques. QSAR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testing or to alert for potential toxicity during the drug discovery process. However, the confidence or reliability of predictions from a QSAR model are difficult to accurately assess. We frame the application of QSAR to preclinical drug development in an off-line inductive conformal prediction framework and apply it prospectively to historical data collected from four different assays within AstraZeneca over a time course of about five years. The results indicate weakened validity of the conformal predictor due to violations of the randomness assumption. The validity can be strengthen by adopting semi-off-line conformal prediction. The non-randomness of the data prevents exactly valid predictions but comparisons to the results of a traditional QSAR procedure applied to the same data indicate that conformal predictions are highly useful in the drug discovery process.

  • 9.
    Eklund, Martin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wikberg, Jarl
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    An eScience-Bayes strategy for analyzing omics data2010In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 11, p. 282-Article in journal (Refereed)
    Abstract [en]

    Background: The omics fields promise to revolutionize our understanding of biology and biomedicine. However, their potential is compromised by the challenge to analyze the huge datasets produced. Analysis of omics data is plagued by the curse of dimensionality, resulting in imprecise estimates of model parameters and performance. Moreover, the integration of omics data with other data sources is difficult to shoehorn into classical statistical models. This has resulted in ad hoc approaches to address specific problems. Results: We present a general approach to omics data analysis that alleviates these problems. By combining eScience and Bayesian methods, we retrieve scientific information and data from multiple sources and coherently incorporate them into large models. These models improve the accuracy of predictions and offer new insights into the underlying mechanisms. This "eScience-Bayes" approach is demonstrated in two proof-of-principle applications, one for breast cancer prognosis prediction from transcriptomic data and one for protein-protein interaction studies based on proteomic data. Conclusions: Bayesian statistics provide the flexibility to tailor statistical models to the complex data structures in omics biology as well as permitting coherent integration of multiple data sources. However, Bayesian methods are in general computationally demanding and require specification of possibly thousands of prior distributions. eScience can help us overcome these difficulties. The eScience-Bayes thus approach permits us to fully leverage on the advantages of Bayesian methods, resulting in models with improved predictive performance that gives more information about the underlying biological system.

  • 10.
    Eklund, Martin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Zwanzig, Silvelyn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics.
    Ridge-SimSel: A generalization of the variable selection method SimSel to multicollinear data setsArticle in journal (Refereed)
  • 11.
    Eklund, Martin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Zwanzig, Silvelyn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics.
    SimSel: a new simulation method for variable selection2012In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 82, no 4, p. 515-527Article in journal (Refereed)
    Abstract [en]

    We propose a new simulation method, SimSel, for variable selection in linear and nonlinear modelling problems. SimSel works by disturbing the input data with pseudo-errors. We then study how this disturbance affects the quality of an approximative model fitted to the data. The main idea is that disturbing unimportant variables does not affect the quality of the model fit. The use of an approximative model has the advantage that the true underlying function does not need to be known and that the method becomes insensitive to model misspecifications. We demonstrate SimSel on simulated data from linear and nonlinear models and on two real data sets. The simulation studies suggest that SimSel works well in complicated situations, such as nonlinear errors-in-variable models.

  • 12.
    Junaid, Muhammad
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lapins, Maris
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wikberg, Jarl
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Proteochemometric Modeling of the Susceptibility of Mutated Variants of the HIV-1 Virus to Reverse Transcriptase Inhibitors2010In: PLoS ONE, ISSN eISSN-1932-6203, Vol. 5, no 12, p. e14353-Article in journal (Refereed)
    Abstract [en]

    Background

    Reverse transcriptase is a major drug target in highly active antiretroviral therapy (HAART) against HIV, which typically comprises two nucleoside/nucleotide analog reverse transcriptase (RT) inhibitors (NRTIs) in combination with a non-nucleoside RT inhibitor or a protease inhibitor. Unfortunately, HIV is capable of escaping the therapy by mutating into drug-resistant variants. Computational models that correlate HIV drug susceptibilities to the virus genotype and to drug molecular properties might facilitate selection of improved combination treatment regimens.

    Methodology/Principal Findings

    We applied our earlier developed proteochemometric modeling technology to analyze HIV mutant susceptibility to the eight clinically approved NRTIs. The data set used covered 728 virus variants genotyped for 240 sequence residues of the DNA polymerase domain of the RT; 165 of these residues contained mutations; totally the data-set covered susceptibility data for 4,495 inhibitor-RT combinations. Inhibitors and RT sequences were represented numerically by 3D-structural and physicochemical property descriptors, respectively. The two sets of descriptors and their derived cross-terms were correlated to the susceptibility data by partial least-squares projections to latent structures. The model identified more than ten frequently occurring mutations, each conferring more than two-fold loss of susceptibility for one or several NRTIs. The most deleterious mutations were K65R, Q151M, M184V/I, and T215Y/F, each of them decreasing susceptibility to most of the NRTIs. The predictive ability of the model was estimated by cross-validation and by external predictions for new HIV variants; both procedures showed very high correlation between the predicted and actual susceptibility values (Q2 = 0.89 and Q2ext = 0.86). The model is available at www.hivdrc.org as a free web service for the prediction of the susceptibility to any of the clinically used NRTIs for any HIV-1 mutant variant.

    Conclusions/Significance

    Our results give directions how to develop approaches for selection of genome-based optimum combination therapy for patients harboring mutated HIV variants.

  • 13.
    Lapins, Maris
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Pharmaceutical Pharmacology.
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Pharmaceutical Pharmacology.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Pharmaceutical Pharmacology.
    Prusis, Peteris
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Pharmaceutical Pharmacology.
    Wikberg, Jarl E S
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Pharmaceutical Pharmacology.
    Proteochemometric modeling of HIV protease susceptibility2008In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 9, p. 181-Article in journal (Refereed)
    Abstract [en]

    BACKGROUND

    A major obstacle in treatment of HIV is the ability of the virus to mutate rapidly into drug-resistant variants. A method for predicting the susceptibility of mutated HIV strains to antiviral agents would provide substantial clinical benefit as well as facilitate the development of new candidate drugs. Therefore, we used proteochemometrics to model the susceptibility of HIV to protease inhibitors in current use, utilizing descriptions of the physico-chemical properties of mutated HIV proteases and 3D structural property descriptions for the protease inhibitors. The descriptions were correlated to the susceptibility data of 828 unique HIV protease variants for seven protease inhibitors in current use; the data set comprised 4792 protease-inhibitor combinations.

    RESULTS

    The model provided excellent predictability (R2 = 0.92, Q2 = 0.87) and identified general and specific features of drug resistance. The model's predictive ability was verified by external prediction in which the susceptibilities to each one of the seven inhibitors were omitted from the data set, one inhibitor at a time, and the data for the six remaining compounds were used to create new models. This analysis showed that the over all predictive ability for the omitted inhibitors was Q2 inhibitors = 0.72.

    CONCLUSION

    Our results show that a proteochemometric approach can provide generalized susceptibility predictions for new inhibitors. Our proteochemometric model can directly analyze inhibitor-protease interactions and facilitate treatment selection based on viral genotype. The model is available for public use, and is located at HIV Drug Research Centre.

  • 14.
    Rogell, Björn
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Evolution, Population and Conservation Biology.
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Thörngren, Hanna
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Evolution, Animal Ecology.
    Laurila, Anssi
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Evolution, Population and Conservation Biology.
    Höglund, Jacob
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Evolution, Population and Conservation Biology.
    The effects of selection, drift and genetic variation on life-history trait divergence among insular populations of natterjack toad, Bufo calamita2010In: Molecular Ecology, ISSN 0962-1083, E-ISSN 1365-294X, Vol. 19, no 11, p. 2229-2240Article in journal (Refereed)
    Abstract [en]

    Although loss of genetic variation is frequently assumed to be associated with loss of adaptive potential, only few studies have examined adaptation in populations with little genetic variation. On the Swedish west coast, the northern fringe populations of the natterjack toad Bufo calamita inhabit an atypical habitat consisting of offshore rock islands. There are strong among-population differences in the amount of neutral genetic variation, making this system suitable for studies on mechanisms of trait divergence along a gradient of within-population genetic variation. In this study, we examined the mechanisms of population divergence using Q(ST)-F-ST comparisons and correlations between quantitative and neutral genetic variation. Our results suggest drift or weak stabilizing selection across the six populations included in this study, as indicated by low Q(ST)-F-ST values, lack of significant population x temperature interactions and lack of significant differences among the islands in breeding pond size. The six populations included in this study differed in both neutral and quantitative genetic variation. Also, the correlations between neutral and quantitative genetic variation tended to be positive, however, the relatively small number of populations prevents any strong conclusions based on these correlations. Contrary to the majority of Q(ST)-F-ST comparisons, our results suggest drift or weak stabilizing selection across the examined populations. Furthermore, the low heritability of fitness-related traits may limit evolutionary responses in some of the populations.

  • 15.
    Rogell, Björn
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Evolution, Population and Conservation Biology.
    Hofman, Maarten
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Evolution, Population and Conservation Biology.
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Laurila, Anssi
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Evolution, Population and Conservation Biology.
    Höglund, Jacob
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Evolution, Population and Conservation Biology.
    The interaction of multiple environmental stressors affects adaptation to a novel habitat in the natterjack toad Bufo calamita2009In: Journal of Evolutionary Biology, ISSN 1010-061X, E-ISSN 1420-9101, Vol. 22, no 11, p. 2267-2277Article in journal (Refereed)
    Abstract [en]

    The potential to adapt to novel environmental conditions is a key area of interest for evolutionary biology. However, the role of multiple selection pressures on adaptive responses has rarely been investigated in natural populations. In Sweden, the natterjack toad Bufo calamita inhabits two separate distribution areas, one in southernmost Sweden and one on the west coast. We characterized the larval habitat in terms of pond size and salinity in the two areas, and found that the breeding ponds of the western populations run higher desiccation risk and had higher salinity than the ponds used by the southern populations. In a common garden experiment manipulating salinity and temperature, we found that toads from the west coast populations were locally adapted to shorter pond duration as indicated by their higher development and growth rates. However, despite being subjected to higher salinity stress in nature, west coast toads had a poorer performance in saline treatments. We found that survival in the saline treatments in the west coast populations was positively affected by larger body mass and longer larval period. Furthermore, we found negative genetic correlations between body mass and growth rate and their non-adaptive plastic responses to salinity. These results implicate that the occurrence of multiple environmental stressors needs to be accounted for when assessing the adaptive potential of organisms and suggest that genetic correlations may play a role in constraining adaptation of natural populations.

  • 16.
    Spjuth, Ola
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Carlsson, Lars
    Alvarsson, Jonathan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Georgiev, Valentin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Willighagen, Egon
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Open source drug discovery with Bioclipse2012In: Current Topics in Medicinal Chemistry, ISSN 1568-0266, E-ISSN 1873-4294, Vol. 12, no 18, p. 1980-1986Article, review/survey (Refereed)
    Abstract [en]

    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-of-metabolism 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.

    Download full text (pdf)
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  • 17.
    Spjuth, Ola
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lapins, Maris
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Junaid, Muhammad
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wikberg, Jarl
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Services for prediction of drug susceptibility for HIV proteases and reverse transcriptases at the HIV Drug Research Centre2011In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 27, no 12, p. 1719-1720Article in journal (Refereed)
    Abstract [en]

    Summary: The HIV Drug Research Centre (HIVDRC) has established Web services for prediction of drug susceptibility for HIV proteases and reverse transcriptases. The services are based on two proteochemometric models which accepts a protease or reverse transcriptase sequence in amino acid form, and outputs the predicted drug susceptibility values. The predictions are based on a comprehensive analysis where all the relevant inhibitors are included, resulting in models with excellent predictive capabilities.

    Availability and Implementation: The services are implemented as interoperable Web services (REST and XMPP), with supporting web pages to allow for individual analyses. A set of plugins were also developed which make the services available from the Bioclipse workbench for life science. Services are available athttp://www.hivdrc.org/services.

  • 18.
    Spjuth, Ola
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Georgiev, Valentin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Carlsson, Lars
    Global Safety Assesment, AstraZeneca R&D.
    Alvarsson, Jonathan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Berg, Arvid
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Willighagen, Egon
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wikberg, Jarl E S
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Bioclipse-R: Integrating management and visualization of life science data with statistical analysis2013In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 29, no 2, p. 286-289Article in journal (Refereed)
    Abstract [en]

    Bioclipse, a graphical workbench for the life sciences, provides functionality for managing and visualizing life science data. We introduce Bioclipse-R, which integrates Bioclipse and the statistical programming language R. The synergy between Bioclipse and R is demonstrated by the construction of a decision support system for anticancer drug screening and mutagenicity prediction, which shows how Bioclipse-R can be used to perform complex tasks from within a single software system.

  • 19.
    Torabi Moghadam, Behrooz
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Alvarsson, Jonathan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Holm, Marcus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Carlsson, Lars
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Scaling predictive modeling in drug development with cloud computing2015In: Journal of Chemical Information and Modeling, ISSN 1549-9596, Vol. 55, p. 19-25Article in journal (Refereed)
  • 20.
    Wikberg, Jarl
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Willighagen, Egon
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lapins, Maris
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Engkvist, Ola
    AstraZeneca R&D, Sweden.
    Alvarsson, Jonathan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Introduction to Pharmaceutical Bioinformatics2010 (ed. 2)Book (Other academic)
  • 21.
    Willighagen, Egon
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Alvarsson, Jonathan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Andersson, Annsofie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lampa, Samuel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lapins, Maris
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wikberg, Jarl
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Linking the Resource Description Framework to cheminformatics and proteochemometrics2011In: Journal of Biomedical Semantics, ISSN 2041-1480, E-ISSN 2041-1480, Vol. 2, no Suppl 1, p. 6-Article in journal (Refereed)
    Abstract [en]

    BACKGROUND :

    Semantic 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.

    RESULTS :

    The 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.

    CONCLUSIONS :

    We 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.

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