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Showing 1 to 12 of 48 entries
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Erratum to: A quantitative assessment of the Hadoop framework for analyzing massively parallel DNA sequencing data.

GigaScience

Siretskiy A, Sundqvist T, Voznesenskiy M, Spjuth O.
PMID: 26664721
Gigascience. 2015 Dec 09;4:61. doi: 10.1186/s13742-015-0100-7. eCollection 2015.

[This corrects the article DOI: 10.1186/s13742-015-0058-5.].

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Siretskiy A, Sundqvist T, Voznesenskiy M, et al. Erratum to: A quantitative assessment of the Hadoop framework for analyzing massively parallel DNA sequencing data. Gigascience. 2015;4:61doi: 10.1186/s13742-015-0100-7.
Siretskiy, A., Sundqvist, T., Voznesenskiy, M., & Spjuth, O. (2015). Erratum to: A quantitative assessment of the Hadoop framework for analyzing massively parallel DNA sequencing data. GigaScience, 461. https://doi.org/10.1186/s13742-015-0100-7
Siretskiy, Alexey, et al. "Erratum to: A quantitative assessment of the Hadoop framework for analyzing massively parallel DNA sequencing data." GigaScience vol. 4 (2015): 61. doi: https://doi.org/10.1186/s13742-015-0100-7
Siretskiy A, Sundqvist T, Voznesenskiy M, Spjuth O. Erratum to: A quantitative assessment of the Hadoop framework for analyzing massively parallel DNA sequencing data. Gigascience. 2015 Dec 09;4:61. doi: 10.1186/s13742-015-0100-7. eCollection 2015. PMID: 26664721; PMCID: PMC4674941.
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Large-scale virtual screening on public cloud resources with Apache Spark.

Journal of cheminformatics

Capuccini M, Ahmed L, Schaal W, Laure E, Spjuth O.
PMID: 28316653
J Cheminform. 2017 Mar 06;9:15. doi: 10.1186/s13321-017-0204-4. eCollection 2017.

BACKGROUND: Structure-based virtual screening is an in-silico method to screen a target receptor against a virtual molecular library. Applying docking-based screening to large molecular libraries can be computationally expensive, however it constitutes a trivially parallelizable task. Most of the...

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Capuccini M, Ahmed L, Schaal W, et al. Large-scale virtual screening on public cloud resources with Apache Spark. J Cheminform. 2017;9:15doi: 10.1186/s13321-017-0204-4.
Capuccini, M., Ahmed, L., Schaal, W., Laure, E., & Spjuth, O. (2017). Large-scale virtual screening on public cloud resources with Apache Spark. Journal of cheminformatics, 915. https://doi.org/10.1186/s13321-017-0204-4
Capuccini, Marco, et al. "Large-scale virtual screening on public cloud resources with Apache Spark." Journal of cheminformatics vol. 9 (2017): 15. doi: https://doi.org/10.1186/s13321-017-0204-4
Capuccini M, Ahmed L, Schaal W, Laure E, Spjuth O. Large-scale virtual screening on public cloud resources with Apache Spark. J Cheminform. 2017 Mar 06;9:15. doi: 10.1186/s13321-017-0204-4. eCollection 2017. PMID: 28316653; PMCID: PMC5339264.
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The future of metabolomics in ELIXIR.

F1000Research

van Rijswijk M, Beirnaert C, Caron C, Cascante M, Dominguez V, Dunn WB, Ebbels TMD, Giacomoni F, Gonzalez-Beltran A, Hankemeier T, Haug K, Izquierdo-Garcia JL, Jimenez RC, Jourdan F, Kale N, Klapa MI, Kohlbacher O, Koort K, Kultima K, Le Corguillé G, Moreno P, Moschonas NK, Neumann S, O'Donovan C, Reczko M, Rocca-Serra P, Rosato A, Salek RM, Sansone SA, Satagopam V, Schober D, Shimmo R, Spicer RA, Spjuth O, Thévenot EA, Viant MR, Weber RJM, Willighagen EL, Zanetti G, Steinbeck C.
PMID: 29043062
F1000Res. 2017 Sep 06;6. doi: 10.12688/f1000research.12342.2. eCollection 2017.

Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of...

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van Rijswijk M, Beirnaert C, Caron C, et al. The future of metabolomics in ELIXIR. F1000Res. 2017;6doi: 10.12688/f1000research.12342.2.
van Rijswijk, M., Beirnaert, C., Caron, C., Cascante, M., Dominguez, V., Dunn, W. B., Ebbels, T. M. D., Giacomoni, F., Gonzalez-Beltran, A., Hankemeier, T., Haug, K., Izquierdo-Garcia, J. L., Jimenez, R. C., Jourdan, F., Kale, N., Klapa, M. I., Kohlbacher, O., Koort, K., Kultima, K., Le Corguillé, G., Moreno, P., Moschonas, N. K., Neumann, S., O'Donovan, C., Reczko, M., Rocca-Serra, P., Rosato, A., Salek, R. M., Sansone, S. A., Satagopam, V., Schober, D., Shimmo, R., Spicer, R. A., Spjuth, O., Thévenot, E. A., Viant, M. R., Weber, R. J. M., Willighagen, E. L., Zanetti, G., & Steinbeck, C. (2017). The future of metabolomics in ELIXIR. F1000Research, 6. https://doi.org/10.12688/f1000research.12342.2
van Rijswijk, Merlijn, et al. "The future of metabolomics in ELIXIR." F1000Research vol. 6 (2017). doi: https://doi.org/10.12688/f1000research.12342.2
van Rijswijk M, Beirnaert C, Caron C, Cascante M, Dominguez V, Dunn WB, Ebbels TMD, Giacomoni F, Gonzalez-Beltran A, Hankemeier T, Haug K, Izquierdo-Garcia JL, Jimenez RC, Jourdan F, Kale N, Klapa MI, Kohlbacher O, Koort K, Kultima K, Le Corguillé G, Moreno P, Moschonas NK, Neumann S, O'Donovan C, Reczko M, Rocca-Serra P, Rosato A, Salek RM, Sansone SA, Satagopam V, Schober D, Shimmo R, Spicer RA, Spjuth O, Thévenot EA, Viant MR, Weber RJM, Willighagen EL, Zanetti G, Steinbeck C. The future of metabolomics in ELIXIR. F1000Res. 2017 Sep 06;6. doi: 10.12688/f1000research.12342.2. eCollection 2017. PMID: 29043062; PMCID: PMC5627583.
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Erratum to: The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching.

Journal of cheminformatics

Willighagen EL, Mayfield JW, Alvarsson J, Berg A, Carlsson L, Jeliazkova N, Kuhn S, Pluskal T, Rojas-Chertó M, Spjuth O, Torrance G, Evelo CT, Guha R, Steinbeck C.
PMID: 29086079
J Cheminform. 2017 Sep 20;9(1):53. doi: 10.1186/s13321-017-0231-1.

No abstract available.

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Willighagen EL, Mayfield JW, Alvarsson J, et al. Erratum to: The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J Cheminform. 2017;9(1):53doi: 10.1186/s13321-017-0231-1.
Willighagen, E. L., Mayfield, J. W., Alvarsson, J., Berg, A., Carlsson, L., Jeliazkova, N., Kuhn, S., Pluskal, T., Rojas-Chertó, M., Spjuth, O., Torrance, G., Evelo, C. T., Guha, R., & Steinbeck, C. (2017). Erratum to: The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. Journal of cheminformatics, 9(1), 53. https://doi.org/10.1186/s13321-017-0231-1
Willighagen, Egon L, et al. "Erratum to: The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching." Journal of cheminformatics vol. 9,1 (2017): 53. doi: https://doi.org/10.1186/s13321-017-0231-1
Willighagen EL, Mayfield JW, Alvarsson J, Berg A, Carlsson L, Jeliazkova N, Kuhn S, Pluskal T, Rojas-Chertó M, Spjuth O, Torrance G, Evelo CT, Guha R, Steinbeck C. Erratum to: The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J Cheminform. 2017 Sep 20;9(1):53. doi: 10.1186/s13321-017-0231-1. PMID: 29086079; PMCID: PMC5607058.
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Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.

Journal of cheminformatics

Lampa S, Alvarsson J, Spjuth O.
PMID: 27942268
J Cheminform. 2016 Nov 24;8:67. doi: 10.1186/s13321-016-0179-6. eCollection 2016.

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,...

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Lampa S, Alvarsson J, Spjuth O. Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles. J Cheminform. 2016;8:67doi: 10.1186/s13321-016-0179-6.
Lampa, S., Alvarsson, J., & Spjuth, O. (2016). Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles. Journal of cheminformatics, 867. https://doi.org/10.1186/s13321-016-0179-6
Lampa, Samuel, et al. "Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles." Journal of cheminformatics vol. 8 (2016): 67. doi: https://doi.org/10.1186/s13321-016-0179-6
Lampa S, Alvarsson J, Spjuth O. Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles. J Cheminform. 2016 Nov 24;8:67. doi: 10.1186/s13321-016-0179-6. eCollection 2016. PMID: 27942268; PMCID: PMC5123367.
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Efficient iterative virtual screening with Apache Spark and conformal prediction.

Journal of cheminformatics

Ahmed L, Georgiev V, Capuccini M, Toor S, Schaal W, Laure E, Spjuth O.
PMID: 29492726
J Cheminform. 2018 Mar 01;10(1):8. doi: 10.1186/s13321-018-0265-z.

BACKGROUND: Docking and scoring large libraries of ligands against target proteins forms the basis of structure-based virtual screening. The problem is trivially parallelizable, and calculations are generally carried out on computer clusters or on large workstations in a brute...

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Ahmed L, Georgiev V, Capuccini M, et al. Efficient iterative virtual screening with Apache Spark and conformal prediction. J Cheminform. 2018;10(1):8doi: 10.1186/s13321-018-0265-z.
Ahmed, L., Georgiev, V., Capuccini, M., Toor, S., Schaal, W., Laure, E., & Spjuth, O. (2018). Efficient iterative virtual screening with Apache Spark and conformal prediction. Journal of cheminformatics, 10(1), 8. https://doi.org/10.1186/s13321-018-0265-z
Ahmed, Laeeq, et al. "Efficient iterative virtual screening with Apache Spark and conformal prediction." Journal of cheminformatics vol. 10,1 (2018): 8. doi: https://doi.org/10.1186/s13321-018-0265-z
Ahmed L, Georgiev V, Capuccini M, Toor S, Schaal W, Laure E, Spjuth O. Efficient iterative virtual screening with Apache Spark and conformal prediction. J Cheminform. 2018 Mar 01;10(1):8. doi: 10.1186/s13321-018-0265-z. PMID: 29492726; PMCID: PMC5833896.
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A confidence predictor for logD using conformal regression and a support-vector machine.

Journal of cheminformatics

Lapins M, Arvidsson S, Lampa S, Berg A, Schaal W, Alvarsson J, Spjuth O.
PMID: 29616425
J Cheminform. 2018 Apr 03;10(1):17. doi: 10.1186/s13321-018-0271-1.

Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water-octanol distribution coefficient (logD) for chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds...

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Lapins M, Arvidsson S, Lampa S, et al. A confidence predictor for logD using conformal regression and a support-vector machine. J Cheminform. 2018;10(1):17doi: 10.1186/s13321-018-0271-1.
Lapins, M., Arvidsson, S., Lampa, S., Berg, A., Schaal, W., Alvarsson, J., & Spjuth, O. (2018). A confidence predictor for logD using conformal regression and a support-vector machine. Journal of cheminformatics, 10(1), 17. https://doi.org/10.1186/s13321-018-0271-1
Lapins, Maris, et al. "A confidence predictor for logD using conformal regression and a support-vector machine." Journal of cheminformatics vol. 10,1 (2018): 17. doi: https://doi.org/10.1186/s13321-018-0271-1
Lapins M, Arvidsson S, Lampa S, Berg A, Schaal W, Alvarsson J, Spjuth O. A confidence predictor for logD using conformal regression and a support-vector machine. J Cheminform. 2018 Apr 03;10(1):17. doi: 10.1186/s13321-018-0271-1. PMID: 29616425; PMCID: PMC5882484.
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Correction to "Using Predicted Bioactivity Profiles to Improve Predictive Modeling".

Journal of chemical information and modeling

Norinder U, Spjuth O, Svensson F.
PMID: 33231446
J Chem Inf Model. 2020 Dec 28;60(12):6722. doi: 10.1021/acs.jcim.0c01327. Epub 2020 Nov 24.

No abstract available.

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Norinder U, Spjuth O, Svensson F. Correction to "Using Predicted Bioactivity Profiles to Improve Predictive Modeling". J Chem Inf Model. 2020;60(12):6722doi: 10.1021/acs.jcim.0c01327.
Norinder, U., Spjuth, O., & Svensson, F. (2020). Correction to "Using Predicted Bioactivity Profiles to Improve Predictive Modeling". Journal of chemical information and modeling, 60(12), 6722. https://doi.org/10.1021/acs.jcim.0c01327
Norinder, Ulf, et al. "Correction to "Using Predicted Bioactivity Profiles to Improve Predictive Modeling"." Journal of chemical information and modeling vol. 60,12 (2020): 6722. doi: https://doi.org/10.1021/acs.jcim.0c01327
Norinder U, Spjuth O, Svensson F. Correction to "Using Predicted Bioactivity Profiles to Improve Predictive Modeling". J Chem Inf Model. 2020 Dec 28;60(12):6722. doi: 10.1021/acs.jcim.0c01327. Epub 2020 Nov 24. PMID: 33231446.
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Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty.

Journal of chemical information and modeling

Svensson F, Aniceto N, Norinder U, Cortes-Ciriano I, Spjuth O, Carlsson L, Bender A.
PMID: 29701973
J Chem Inf Model. 2018 May 29;58(5):1132-1140. doi: 10.1021/acs.jcim.8b00054. Epub 2018 May 10.

Making predictions with an associated confidence is highly desirable as it facilitates decision making and resource prioritization. Conformal regression is a machine learning framework that allows the user to define the required confidence and delivers predictions that are guaranteed...

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Svensson F, Aniceto N, Norinder U, et al. Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty. J Chem Inf Model. 2018;58(5):1132-1140doi: 10.1021/acs.jcim.8b00054.
Svensson, F., Aniceto, N., Norinder, U., Cortes-Ciriano, I., Spjuth, O., Carlsson, L., & Bender, A. (2018). Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty. Journal of chemical information and modeling, 58(5), 1132-1140. https://doi.org/10.1021/acs.jcim.8b00054
Svensson, Fredrik, et al. "Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty." Journal of chemical information and modeling vol. 58,5 (2018): 1132-1140. doi: https://doi.org/10.1021/acs.jcim.8b00054
Svensson F, Aniceto N, Norinder U, Cortes-Ciriano I, Spjuth O, Carlsson L, Bender A. Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty. J Chem Inf Model. 2018 May 29;58(5):1132-1140. doi: 10.1021/acs.jcim.8b00054. Epub 2018 May 10. PMID: 29701973.
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Toward Standardized Classification of Foveated Displays.

IEEE transactions on visualization and computer graphics

Spjut J, Boudaoud B, Kim J, Greer T, Albert R, Stengel M, Aksit K, Luebke D.
PMID: 32078547
IEEE Trans Vis Comput Graph. 2020 May;26(5):2126-2134. doi: 10.1109/TVCG.2020.2973053. Epub 2020 Feb 14.

Emergent in the field of head mounted display design is a desire to leverage the limitations of the human visual system to reduce the computation, communication, and display workload in power and form-factor constrained systems. Fundamental to this reduced...

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Spjut J, Boudaoud B, Kim J, et al. Toward Standardized Classification of Foveated Displays. IEEE Trans Vis Comput Graph. 2020;26(5):2126-2134doi: 10.1109/TVCG.2020.2973053.
Spjut, J., Boudaoud, B., Kim, J., Greer, T., Albert, R., Stengel, M., Aksit, K., & Luebke, D. (2020). Toward Standardized Classification of Foveated Displays. IEEE transactions on visualization and computer graphics, 26(5), 2126-2134. https://doi.org/10.1109/TVCG.2020.2973053
Spjut, Josef, et al. "Toward Standardized Classification of Foveated Displays." IEEE transactions on visualization and computer graphics vol. 26,5 (2020): 2126-2134. doi: https://doi.org/10.1109/TVCG.2020.2973053
Spjut J, Boudaoud B, Kim J, Greer T, Albert R, Stengel M, Aksit K, Luebke D. Toward Standardized Classification of Foveated Displays. IEEE Trans Vis Comput Graph. 2020 May;26(5):2126-2134. doi: 10.1109/TVCG.2020.2973053. Epub 2020 Feb 14. PMID: 32078547.
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Origin of aromatase inhibitory activity via proteochemometric modeling.

PeerJ

Simeon S, Spjuth O, Lapins M, Nabu S, Anuwongcharoen N, Prachayasittikul V, Wikberg JE, Nantasenamat C.
PMID: 27190705
PeerJ. 2016 May 12;4:e1979. doi: 10.7717/peerj.1979. eCollection 2016.

Aromatase, the rate-limiting enzyme that catalyzes the conversion of androgen to estrogen, plays an essential role in the development of estrogen-dependent breast cancer. Side effects due to aromatase inhibitors (AIs) necessitate the pursuit of novel inhibitor candidates with high...

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Simeon S, Spjuth O, Lapins M, et al. Origin of aromatase inhibitory activity via proteochemometric modeling. PeerJ. 2016;4:e1979doi: 10.7717/peerj.1979.
Simeon, S., Spjuth, O., Lapins, M., Nabu, S., Anuwongcharoen, N., Prachayasittikul, V., Wikberg, J. E., & Nantasenamat, C. (2016). Origin of aromatase inhibitory activity via proteochemometric modeling. PeerJ, 4e1979. https://doi.org/10.7717/peerj.1979
Simeon, Saw, et al. "Origin of aromatase inhibitory activity via proteochemometric modeling." PeerJ vol. 4 (2016): e1979. doi: https://doi.org/10.7717/peerj.1979
Simeon S, Spjuth O, Lapins M, Nabu S, Anuwongcharoen N, Prachayasittikul V, Wikberg JE, Nantasenamat C. Origin of aromatase inhibitory activity via proteochemometric modeling. PeerJ. 2016 May 12;4:e1979. doi: 10.7717/peerj.1979. eCollection 2016. PMID: 27190705; PMCID: PMC4868594.
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Large-scale ligand-based predictive modelling using support vector machines.

Journal of cheminformatics

Alvarsson J, Lampa S, Schaal W, Andersson C, Wikberg JE, Spjuth O.
PMID: 27516811
J Cheminform. 2016 Aug 10;8:39. doi: 10.1186/s13321-016-0151-5. eCollection 2016.

The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time,...

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Alvarsson J, Lampa S, Schaal W, et al. Large-scale ligand-based predictive modelling using support vector machines. J Cheminform. 2016;8:39doi: 10.1186/s13321-016-0151-5.
Alvarsson, J., Lampa, S., Schaal, W., Andersson, C., Wikberg, J. E., & Spjuth, O. (2016). Large-scale ligand-based predictive modelling using support vector machines. Journal of cheminformatics, 839. https://doi.org/10.1186/s13321-016-0151-5
Alvarsson, Jonathan, et al. "Large-scale ligand-based predictive modelling using support vector machines." Journal of cheminformatics vol. 8 (2016): 39. doi: https://doi.org/10.1186/s13321-016-0151-5
Alvarsson J, Lampa S, Schaal W, Andersson C, Wikberg JE, Spjuth O. Large-scale ligand-based predictive modelling using support vector machines. J Cheminform. 2016 Aug 10;8:39. doi: 10.1186/s13321-016-0151-5. eCollection 2016. PMID: 27516811; PMCID: PMC4980776.
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Showing 1 to 12 of 48 entries