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Items: 1 to 50 of 67

1.

SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines.

Lampa S, Dahlö M, Alvarsson J, Spjuth O.

Gigascience. 2019 May 1;8(5). pii: giz044. doi: 10.1093/gigascience/giz044.

2.

Alterations in the tyrosine and phenylalanine pathways revealed by biochemical profiling in cerebrospinal fluid of Huntington's disease subjects.

Herman S, Niemelä V, Emami Khoonsari P, Sundblom J, Burman J, Landtblom AM, Spjuth O, Nyholm D, Kultima K.

Sci Rep. 2019 Mar 11;9(1):4129. doi: 10.1038/s41598-019-40186-5.

3.

Interoperable and scalable data analysis with microservices: applications in metabolomics.

Emami Khoonsari P, Moreno P, Bergmann S, Burman J, Capuccini M, Carone M, Cascante M, de Atauri P, Foguet C, Gonzalez-Beltran AN, Hankemeier T, Haug K, He S, Herman S, Johnson D, Kale N, Larsson A, Neumann S, Peters K, Pireddu L, Rocca-Serra P, Roger P, Rueedi R, Ruttkies C, Sadawi N, Salek RM, Sansone SA, Schober D, Selivanov V, Thévenot EA, van Vliet M, Zanetti G, Steinbeck C, Kultima K, Spjuth O.

Bioinformatics. 2019 Oct 1;35(19):3752-3760. doi: 10.1093/bioinformatics/btz160.

4.

Biochemical Differences in Cerebrospinal Fluid between Secondary Progressive and Relapsing⁻Remitting Multiple Sclerosis.

Herman S, Åkerfeldt T, Spjuth O, Burman J, Kultima K.

Cells. 2019 Jan 24;8(2). pii: E84. doi: 10.3390/cells8020084.

5.

Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes.

Kensert A, Harrison PJ, Spjuth O.

SLAS Discov. 2019 Apr;24(4):466-475. doi: 10.1177/2472555218818756. Epub 2019 Jan 14.

6.

Deep Learning in Image Cytometry: A Review.

Gupta A, Harrison PJ, Wieslander H, Pielawski N, Kartasalo K, Partel G, Solorzano L, Suveer A, Klemm AH, Spjuth O, Sintorn IM, Wählby C.

Cytometry A. 2019 Apr;95(4):366-380. doi: 10.1002/cyto.a.23701. Epub 2018 Dec 19. Review.

7.

PhenoMeNal: processing and analysis of metabolomics data in the cloud.

Peters K, Bradbury J, Bergmann S, Capuccini M, Cascante M, de Atauri P, Ebbels TMD, Foguet C, Glen R, Gonzalez-Beltran A, Günther UL, Handakas E, Hankemeier T, Haug K, Herman S, Holub P, Izzo M, Jacob D, Johnson D, Jourdan F, Kale N, Karaman I, Khalili B, Emami Khonsari P, Kultima K, Lampa S, Larsson A, Ludwig C, Moreno P, Neumann S, Novella JA, O'Donovan C, Pearce JTM, Peluso A, Piras ME, Pireddu L, Reed MAC, Rocca-Serra P, Roger P, Rosato A, Rueedi R, Ruttkies C, Sadawi N, Salek RM, Sansone SA, Selivanov V, Spjuth O, Schober D, Thévenot EA, Tomasoni M, van Rijswijk M, van Vliet M, Viant MR, Weber RJM, Zanetti G, Steinbeck C.

Gigascience. 2019 Feb 1;8(2). pii: giy149. doi: 10.1093/gigascience/giy149.

8.

Predicting Off-Target Binding Profiles With Confidence Using Conformal Prediction.

Lampa S, Alvarsson J, Arvidsson Mc Shane S, Berg A, Ahlberg E, Spjuth O.

Front Pharmacol. 2018 Nov 6;9:1256. doi: 10.3389/fphar.2018.01256. eCollection 2018.

9.

Evaluating parameters for ligand-based modeling with random forest on sparse data sets.

Kensert A, Alvarsson J, Norinder U, Spjuth O.

J Cheminform. 2018 Oct 11;10(1):49. doi: 10.1186/s13321-018-0304-9.

10.

Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis.

Herman S, Khoonsari PE, Tolf A, Steinmetz J, Zetterberg H, Åkerfeldt T, Jakobsson PJ, Larsson A, Spjuth O, Burman J, Kultima K.

Theranostics. 2018 Aug 7;8(16):4477-4490. doi: 10.7150/thno.26249. eCollection 2018.

11.

Novel applications of Machine Learning in cheminformatics.

Spjuth O.

J Cheminform. 2018 Sep 6;10(1):46. doi: 10.1186/s13321-018-0301-z. No abstract available.

12.

Container-based bioinformatics with Pachyderm.

Novella JA, Emami Khoonsari P, Herman S, Whitenack D, Capuccini M, Burman J, Kultima K, Spjuth O.

Bioinformatics. 2019 Mar 1;35(5):839-846. doi: 10.1093/bioinformatics/bty699.

13.

Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty.

Svensson F, Aniceto N, Norinder U, Cortes-Ciriano I, Spjuth O, Carlsson L, Bender A.

J Chem Inf Model. 2018 May 29;58(5):1132-1140. doi: 10.1021/acs.jcim.8b00054. Epub 2018 May 10.

PMID:
29701973
14.

Tracking the NGS revolution: managing life science research on shared high-performance computing clusters.

Dahlö M, Scofield DG, Schaal W, Spjuth O.

Gigascience. 2018 May 1;7(5). doi: 10.1093/gigascience/giy028.

15.

A confidence predictor for logD using conformal regression and a support-vector machine.

Lapins M, Arvidsson S, Lampa S, Berg A, Schaal W, Alvarsson J, Spjuth O.

J Cheminform. 2018 Apr 3;10(1):17. doi: 10.1186/s13321-018-0271-1.

16.

Efficient iterative virtual screening with Apache Spark and conformal prediction.

Ahmed L, Georgiev V, Capuccini M, Toor S, Schaal W, Laure E, Spjuth O.

J Cheminform. 2018 Mar 1;10(1):8. doi: 10.1186/s13321-018-0265-z.

17.

The future of metabolomics in ELIXIR.

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.

Version 2. F1000Res. 2017 Sep 6 [revised 2017 Jan 1];6. pii: ELIXIR-1649. doi: 10.12688/f1000research.12342.2. eCollection 2017.

18.

Erratum to: The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching.

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.

J Cheminform. 2017 Sep 20;9(1):53. doi: 10.1186/s13321-017-0231-1. No abstract available.

19.

The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching.

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.

J Cheminform. 2017 Jun 6;9(1):33. doi: 10.1186/s13321-017-0220-4. Erratum in: J Cheminform. 2017 Sep 20;9(1):53.

20.

RDFIO: extending Semantic MediaWiki for interoperable biomedical data management.

Lampa S, Willighagen E, Kohonen P, King A, Vrandečić D, Grafström R, Spjuth O.

J Biomed Semantics. 2017 Sep 4;8(1):35. doi: 10.1186/s13326-017-0136-y.

21.

Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions.

Herman S, Emami Khoonsari P, Aftab O, Krishnan S, Strömbom E, Larsson R, Hammerling U, Spjuth O, Kultima K, Gustafsson M.

Metabolomics. 2017;13(7):79. doi: 10.1007/s11306-017-1213-z. Epub 2017 May 19.

22.

E-Science technologies in a workflow for personalized medicine using cancer screening as a case study.

Spjuth O, Karlsson A, Clements M, Humphreys K, Ivansson E, Dowling J, Eklund M, Jauhiainen A, Czene K, Grönberg H, Sparén P, Wiklund F, Cheddad A, Pálsdóttir Þ, Rantalainen M, Abrahamsson L, Laure E, Litton JE, Palmgren J.

J Am Med Inform Assoc. 2017 Sep 1;24(5):950-957. doi: 10.1093/jamia/ocx038.

PMID:
28444384
23.

Towards Predicting the Cytochrome P450 Modulation: From QSAR to Proteochemometric Modeling.

Shoombuatong W, Prathipati P, Prachayasittikul V, Schaduangrat N, Malik AA, Pratiwi R, Wanwimolruk S, Wikberg JES, Gleeson MP, Spjuth O, Nantasenamat C.

Curr Drug Metab. 2017 Jul 21;18(6):540-555. doi: 10.2174/1389200218666170320121932. Review.

PMID:
28322159
24.

Large-scale virtual screening on public cloud resources with Apache Spark.

Capuccini M, Ahmed L, Schaal W, Laure E, Spjuth O.

J Cheminform. 2017 Mar 6;9:15. doi: 10.1186/s13321-017-0204-4. eCollection 2017.

25.

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

Lampa S, Alvarsson J, Spjuth O.

J Cheminform. 2016 Nov 24;8:67. eCollection 2016.

26.

XMetDB: an open access database for xenobiotic metabolism.

Spjuth O, Rydberg P, Willighagen EL, Evelo CT, Jeliazkova N.

J Cheminform. 2016 Sep 15;8:47. doi: 10.1186/s13321-016-0161-3. eCollection 2016.

27.

Large-scale ligand-based predictive modelling using support vector machines.

Alvarsson J, Lampa S, Schaal W, Andersson C, Wikberg JE, Spjuth O.

J Cheminform. 2016 Aug 10;8:39. doi: 10.1186/s13321-016-0151-5. eCollection 2016.

28.

Recommendations on e-infrastructures for next-generation sequencing.

Spjuth O, Bongcam-Rudloff E, Dahlberg J, Dahlö M, Kallio A, Pireddu L, Vezzi F, Korpelainen E.

Gigascience. 2016 Jun 7;5:26. doi: 10.1186/s13742-016-0132-7. Review.

29.

Origin of aromatase inhibitory activity via proteochemometric modeling.

Simeon S, Spjuth O, Lapins M, Nabu S, Anuwongcharoen N, Prachayasittikul V, Wikberg JE, Nantasenamat C.

PeerJ. 2016 May 12;4:e1979. doi: 10.7717/peerj.1979. eCollection 2016.

30.

Erratum to: A quantitative assessment of the Hadoop framework for analyzing massively parallel DNA sequencing data.

Siretskiy A, Sundqvist T, Voznesenskiy M, Spjuth O.

Gigascience. 2015 Dec 9;4:61. doi: 10.1186/s13742-015-0100-7. eCollection 2015.

31.

Toward the Replacement of Animal Experiments through the Bioinformatics-driven Analysis of 'Omics' Data from Human Cell Cultures.

Grafström RC, Nymark P, Hongisto V, Spjuth O, Ceder R, Willighagen E, Hardy B, Kaski S, Kohonen P.

Altern Lab Anim. 2015 Nov;43(5):325-32.

PMID:
26551289
32.

BioImg.org: A Catalog of Virtual Machine Images for the Life Sciences.

Dahlö M, Haziza F, Kallio A, Korpelainen E, Bongcam-Rudloff E, Spjuth O.

Bioinform Biol Insights. 2015 Sep 10;9:125-8. doi: 10.4137/BBI.S28636. eCollection 2015.

33.

Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research.

Spjuth O, Krestyaninova M, Hastings J, Shen HY, Heikkinen J, Waldenberger M, Langhammer A, Ladenvall C, Esko T, Persson MÅ, Heggland J, Dietrich J, Ose S, Gieger C, Ried JS, Peters A, Fortier I, de Geus EJ, Klovins J, Zaharenko L, Willemsen G, Hottenga JJ, Litton JE, Karvanen J, Boomsma DI, Groop L, Rung J, Palmgren J, Pedersen NL, McCarthy MI, van Duijn CM, Hveem K, Metspalu A, Ripatti S, Prokopenko I, Harris JR.

Eur J Hum Genet. 2016 Apr;24(4):521-8. doi: 10.1038/ejhg.2015.165. Epub 2015 Aug 26.

34.

Experiences with workflows for automating data-intensive bioinformatics.

Spjuth O, Bongcam-Rudloff E, Hernández GC, Forer L, Giovacchini M, Guimera RV, Kallio A, Korpelainen E, Kańduła MM, Krachunov M, Kreil DP, Kulev O, Łabaj PP, Lampa S, Pireddu L, Schönherr S, Siretskiy A, Vassilev D.

Biol Direct. 2015 Aug 19;10:43. doi: 10.1186/s13062-015-0071-8.

35.

A quantitative assessment of the Hadoop framework for analyzing massively parallel DNA sequencing data.

Siretskiy A, Sundqvist T, Voznesenskiy M, Spjuth O.

Gigascience. 2015 Jun 4;4:26. doi: 10.1186/s13742-015-0058-5. eCollection 2015. Erratum in: Gigascience. 2015;4:61.

36.

Scaling predictive modeling in drug development with cloud computing.

Moghadam BT, Alvarsson J, Holm M, Eklund M, Carlsson L, Spjuth O.

J Chem Inf Model. 2015 Jan 26;55(1):19-25. doi: 10.1021/ci500580y. Epub 2015 Jan 8.

PMID:
25493610
37.

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

Alvarsson J, Eklund M, Andersson C, Carlsson L, Spjuth O, Wikberg JE.

J Chem Inf Model. 2014 Nov 24;54(11):3211-7. doi: 10.1021/ci500344v. Epub 2014 Oct 28.

PMID:
25318024
38.

Ligand-based target prediction with signature fingerprints.

Alvarsson J, Eklund M, Engkvist O, Spjuth O, Carlsson L, Wikberg JE, Noeske T.

J Chem Inf Model. 2014 Oct 27;54(10):2647-53. doi: 10.1021/ci500361u. Epub 2014 Oct 3.

PMID:
25230336
39.

Cancer biology, toxicology and alternative methods development go hand-in-hand.

Kohonen P, Ceder R, Smit I, Hongisto V, Myatt G, Hardy B, Spjuth O, Grafström R.

Basic Clin Pharmacol Toxicol. 2014 Jul;115(1):50-8. doi: 10.1111/bcpt.12257. Epub 2014 May 23. Review.

40.

Automated QuantMap for rapid quantitative molecular network topology analysis.

Schaal W, Hammerling U, Gustafsson MG, Spjuth O.

Bioinformatics. 2013 Sep 15;29(18):2369-70. doi: 10.1093/bioinformatics/btt390. Epub 2013 Jul 4.

41.

Lessons learned from implementing a national infrastructure in Sweden for storage and analysis of next-generation sequencing data.

Lampa S, Dahlö M, Olason PI, Hagberg J, Spjuth O.

Gigascience. 2013 Jun 25;2(1):9. doi: 10.1186/2047-217X-2-9.

42.

A unified proteochemometric model for prediction of inhibition of cytochrome p450 isoforms.

Lapins M, Worachartcheewan A, Spjuth O, Georgiev V, Prachayasittikul V, Nantasenamat C, Wikberg JE.

PLoS One. 2013 Jun 17;8(6):e66566. doi: 10.1371/journal.pone.0066566. Print 2013.

43.

WhichCyp: prediction of cytochromes P450 inhibition.

Rostkowski M, Spjuth O, Rydberg P.

Bioinformatics. 2013 Aug 15;29(16):2051-2. doi: 10.1093/bioinformatics/btt325. Epub 2013 Jun 5.

PMID:
23740742
44.

The ChEMBL database as linked open data.

Willighagen EL, Waagmeester A, Spjuth O, Ansell P, Williams AJ, Tkachenko V, Hastings J, Chen B, Wild DJ.

J Cheminform. 2013 May 8;5(1):23. doi: 10.1186/1758-2946-5-23.

45.

Applications of the InChI in cheminformatics with the CDK and Bioclipse.

Spjuth O, Berg A, Adams S, Willighagen EL.

J Cheminform. 2013 Mar 13;5(1):14. doi: 10.1186/1758-2946-5-14.

46.

Bioclipse-R: integrating management and visualization of life science data with statistical analysis.

Spjuth O, Georgiev V, Carlsson L, Alvarsson J, Berg A, Willighagen E, Wikberg JE, Eklund M.

Bioinformatics. 2013 Jan 15;29(2):286-9. doi: 10.1093/bioinformatics/bts681. Epub 2012 Nov 23.

47.

Open source drug discovery with bioclipse.

Spjuth O, Carlsson L, Alvarsson J, Georgiev V, Willighagen E, Eklund M.

Curr Top Med Chem. 2012;12(18):1980-6.

PMID:
23110533
48.

On mechanisms of reactive metabolite formation from drugs.

Claesson A, Spjuth O.

Mini Rev Med Chem. 2013 Apr 1;13(5):720-9. Review.

PMID:
23035789
49.

Accessing, using, and creating chemical property databases for computational toxicology modeling.

Williams AJ, Ekins S, Spjuth O, Willighagen EL.

Methods Mol Biol. 2012;929:221-41.

PMID:
23007432
50.

Toxicology ontology perspectives.

Hardy B, Apic G, Carthew P, Clark D, Cook D, Dix I, Escher S, Hastings J, Heard DJ, Jeliazkova N, Judson P, Matis-Mitchell S, Mitic D, Myatt G, Shah I, Spjuth O, Tcheremenskaia O, Toldo L, Watson D, White A, Yang C.

ALTEX. 2012;29(2):139-56. doi: 10.14573/altex.2012.2.139. Review.

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