Publications
Preprints
Ouyang W,
Bowman R,
Wang H,
Bumke KE,
Collins JT,
Spjuth O,
Carreras-Puigvert J and Diederich B
An Open-Source Modular Framework for Automated Pipetting and Imaging Applications
bioRxiv. 2021.06.24.449732 (2021). DOI: 10.1101/2021.06.24.449732
An Open-Source Modular Framework for Automated Pipetting and Imaging Applications
bioRxiv. 2021.06.24.449732 (2021). DOI: 10.1101/2021.06.24.449732
Ekmefjord M,
Ait-Mlouk A,
Alawadi S,
Åkesson M,
Stoyanova D,
Spjuth O,
Toor S,
Hellander A
Scalable federated machine learning with FEDn
arXiv. 2103.00148 (2021). URL: arxiv.org/abs/2103.00148
Scalable federated machine learning with FEDn
arXiv. 2103.00148 (2021). URL: arxiv.org/abs/2103.00148
Wieslander H,
Gupta A,
Bergman E,
Hallström E,
Harrison PJ
Learning to see colours: generating biologically relevant fluorescent labels from bright-field images
bioRxiv. 2021.01.18.427121 (2021). DOI: 10.1101/2021.01.18.427121
Learning to see colours: generating biologically relevant fluorescent labels from bright-field images
bioRxiv. 2021.01.18.427121 (2021). DOI: 10.1101/2021.01.18.427121
Rietdijk J,
Tampere M,
Pettke A,
Georgieva P,
Lapins M,
Warpman Berglund U,
Spjuth O,
Puumalainen MR,
Carreras-Puigvert J
A rapid phenomics workflow for the in vitro identification of antiviral drugs
bioRxiv. 2021.01.13.423947 (2021). DOI: 10.1101/2021.01.13.423947
A rapid phenomics workflow for the in vitro identification of antiviral drugs
bioRxiv. 2021.01.13.423947 (2021). DOI: 10.1101/2021.01.13.423947
Lapins M,
Spjuth O
Evaluation of gene expression and phenotypic profiling data as quantitative descriptors for predicting drug targets and mechanisms of action
bioRxiv. (2019). DOI: 10.1101/580654
Evaluation of gene expression and phenotypic profiling data as quantitative descriptors for predicting drug targets and mechanisms of action
bioRxiv. (2019). DOI: 10.1101/580654
Moreno P,
Pireddu L,
Roger P,
Goonasekera N,
Afgan E,
Beek Mvd,
He S,
Larsson A,
Ruttkies C,
Schober D,
Johnson D,
Rocca-Serra P,
Weber RJM,
Gruening B,
Salek B,
Kale N,
Perez-Riverol Y,
Papatheodorou I,
Spjuth O,
Neumann D
Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud
bioRxiv. 488643 (2018). DOI: 10.1101/488643
Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud
bioRxiv. 488643 (2018). DOI: 10.1101/488643
Gauraha,
N. and Spjuth,
O.
Synergy Conformal Prediction
DiVA preprint. 360504 (2018). URL: urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-360504
Synergy Conformal Prediction
DiVA preprint. 360504 (2018). URL: urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-360504
Spjuth O,
Carlsson L.,
Gauraha N.
Aggregating Predictions on Multiple Non-disclosed Datasets using Conformal Prediction
arXiv. 1806.04000 (2018). URL: arxiv.org/abs/1806.04000
Aggregating Predictions on Multiple Non-disclosed Datasets using Conformal Prediction
arXiv. 1806.04000 (2018). URL: arxiv.org/abs/1806.04000
Gauraha N,
Spjuth O.
conformalClassification: A Conformal Prediction R Package for Classification.
arXiv:1804.05494. (2018). URL: arxiv.org/abs/1804.05494
conformalClassification: A Conformal Prediction R Package for Classification.
arXiv:1804.05494. (2018). URL: arxiv.org/abs/1804.05494
2021
Spjuth O,
Capuccini M,
Carone M,
Larsson A,
Schaal W,
Novella J,
Stein JA,
Ekmefjord M,
Di Tommaso P,
Floden E,
Notredame C,
Moreno P,
Khoonsari PE,
Herman S,
Kultima K,
Lampa S
Approaches for containerized scientific workflows in cloud environments with applications in life science
F1000Research. 10, 513 (2021). DOI: 10.12688/f1000research.53698.1
Approaches for containerized scientific workflows in cloud environments with applications in life science
F1000Research. 10, 513 (2021). DOI: 10.12688/f1000research.53698.1
Arvidsson McShane S,
Ahlberg E,
Noeske T,
and Spjuth O.
Machine learning strategies when transitioning between biological assays
Journal of Chemical Information and Modeling. Online ahead of print (2021). DOI: 10.1021/acs.jcim.1c00293
Machine learning strategies when transitioning between biological assays
Journal of Chemical Information and Modeling. Online ahead of print (2021). DOI: 10.1021/acs.jcim.1c00293
Spjuth O,
Frid J,
and Hellander A.
The Machine Learning Life Cycle and the Cloud: Implications for Drug Discovery
Expert Opinion On Drug Discovery. Accepted (2021). DOI: 10.1080/17460441.2021.1932812
The Machine Learning Life Cycle and the Cloud: Implications for Drug Discovery
Expert Opinion On Drug Discovery. Accepted (2021). DOI: 10.1080/17460441.2021.1932812
Fagerholm U,
Hellberg S,
and Spjuth O.
Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology
Molecules. 6, 29 (2021). DOI: 10.3390/molecules26092572
Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology
Molecules. 6, 29 (2021). DOI: 10.3390/molecules26092572
Morger M,
Svensson F,
Arvidsson McShane S,
Gauraha N,
Norinder U,
Spjuth O,
Volkamer A.
Assessing the Calibration in Toxicological in Vitro Models with Conformal Prediction
Journal of Cheminformatics. 13, 35 (2021). DOI: 10.1186/s13321-021-00511-5
Assessing the Calibration in Toxicological in Vitro Models with Conformal Prediction
Journal of Cheminformatics. 13, 35 (2021). DOI: 10.1186/s13321-021-00511-5
Jakobsson J,
Spjuth O,
Lagerström M.
scConnect: a method for exploratory analysis of cell-cell communication based on single cell RNA sequencing data
Bioinformatics. btab245 (2021). DOI: 10.1093/bioinformatics/btab245
scConnect: a method for exploratory analysis of cell-cell communication based on single cell RNA sequencing data
Bioinformatics. btab245 (2021). DOI: 10.1093/bioinformatics/btab245
Harrison P,
Wieslander H,
Sabirsh A,
Karlsson J,
Malmsjö V,
Hellander A,
Wählby C,
Spjuth O
Deep learning models for lipid-nanoparticle-based drug delivery
Nanomedicine. Ahead of print (2021). DOI: 10.2217/nnm-2020-0461
Deep learning models for lipid-nanoparticle-based drug delivery
Nanomedicine. Ahead of print (2021). DOI: 10.2217/nnm-2020-0461
Blamey B,
Toor S,
Dahlö M,
Wieslander H,
Harrison PJ,
Sintorn IM,
Sabirsh A,
Wählby C,
Spjuth O,
Hellander A
Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit
Gigascience. 10, 3 (2021). DOI: 10.1093/gigascience/giab018
Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit
Gigascience. 10, 3 (2021). DOI: 10.1093/gigascience/giab018
Gauraha,
N. and Spjuth,
O.
Synergy Conformal Prediction for Regression
Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods. vol 1: ICPRAM, 212-221. (2021). DOI: 10.5220/0010229402120221
Synergy Conformal Prediction for Regression
Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods. vol 1: ICPRAM, 212-221. (2021). DOI: 10.5220/0010229402120221
Alvarsson J,
Arvidsson McShane S,
Norinder U and Spjuth O.
Predicting with confidence: Using conformal prediction in drug discovery
Journal of Pharmaceutical Sciences. 110, 1, 42-49. (2021). DOI: 10.1016/j.xphs.2020.09.055
Predicting with confidence: Using conformal prediction in drug discovery
Journal of Pharmaceutical Sciences. 110, 1, 42-49. (2021). DOI: 10.1016/j.xphs.2020.09.055
Wieslander H.,
Harrison P,
Skogberg G,
Jackson S,
Fridén M,
Karlsson J,
Spjuth O,
and Wählby C.
Deep learning with conformal prediction for hierarchical analysis of large-scale whole-slide tissue images
IEEE Journal of Biomedical and Health Informatics. 25, 2, 371-380. (2021). DOI: 10.1109/JBHI.2020.2996300
Deep learning with conformal prediction for hierarchical analysis of large-scale whole-slide tissue images
IEEE Journal of Biomedical and Health Informatics. 25, 2, 371-380. (2021). DOI: 10.1109/JBHI.2020.2996300
2020
Ashrafian H,
Glen R,
Ebbels T,
Blaise B,
Kalra D,
Kultima K,
Spjuth O,
Tenori L,
Sounderajah V,
Salek RM,
Kale N,
Haug K,
Schober D,
Rocca-Serra P,
O'Donovan C,
Steinbeck C,
Cano I,
de Atauri P,
Cascante M
Metabolomics - the stethoscope for the 21st century
Medical Principles and Practice. Online ahead of print (2020). DOI: 10.1159/000513545
Metabolomics - the stethoscope for the 21st century
Medical Principles and Practice. Online ahead of print (2020). DOI: 10.1159/000513545
Stein O,
Blamey B,
Karlsson J,
Sabirsh A,
Spjuth O,
Hellander A,
and Toor S.
Smart Resource Management for Data Streaming using an Online Bin-packing Strategy
2020 IEEE International Conference on Big Data (Big Data). Accepted (2020). DOI: 10.1109/BigData50022.2020.937824
Smart Resource Management for Data Streaming using an Online Bin-packing Strategy
2020 IEEE International Conference on Big Data (Big Data). Accepted (2020). DOI: 10.1109/BigData50022.2020.937824
Ahmed L,
Alogheli H,
Arvidsson McShane S,
Alvarsson J,
Berg A,
Larsson A,
Schaal W,
Laure E and Spjuth O.
Predicting Target Profiles with Confidence as a Service using Docking Scores
Journal of Cheminformatics. 12, 62 (2020). DOI: 10.1186/s13321-020-00464-1
Predicting Target Profiles with Confidence as a Service using Docking Scores
Journal of Cheminformatics. 12, 62 (2020). DOI: 10.1186/s13321-020-00464-1
Norinder U,
Spjuth O,
Svensson F
Using Predicted Bioactivity Profiles to Improve Predictive Modelling
Journal of Chemical Information and Modeling. 60, 6, 2830-2837. (2020). DOI: 10.1021/acs.jcim.0c00250
Using Predicted Bioactivity Profiles to Improve Predictive Modelling
Journal of Chemical Information and Modeling. 60, 6, 2830-2837. (2020). DOI: 10.1021/acs.jcim.0c00250
Capuccini M,
Dahlö M,
Toor S,
and Spjuth O
MaRe: Container-Based Parallel Computing with Data Locality
Gigascience. 9, 5, giaa042. (2020). DOI: 10.1093/gigascience/giaa042
MaRe: Container-Based Parallel Computing with Data Locality
Gigascience. 9, 5, giaa042. (2020). DOI: 10.1093/gigascience/giaa042
Schaduangrat N,
Lampa S,
Simeon S,
Gleeson MP,
Spjuth O,
Nantasenamat C
Towards reproducible computational drug discovery
Journal of Cheminformatics. 12, 9 (2020). DOI: 10.1186/s13321-020-0408-x
Towards reproducible computational drug discovery
Journal of Cheminformatics. 12, 9 (2020). DOI: 10.1186/s13321-020-0408-x
2019
Capuccini M,
Larsson A,
Carone M,
Novella JA,
Sadawi N,
Gao J,
Toor S,
Spjuth O
On-demand virtual research environments using microservices
PeerJ Computer Science. 5, e232 (2019). DOI: 10.7717/peerj-cs.232
On-demand virtual research environments using microservices
PeerJ Computer Science. 5, e232 (2019). DOI: 10.7717/peerj-cs.232
One Thousand Plant Transcriptomes Initiative
One thousand plant transcriptomes and the phylogenomics of green plants
Nature. 574, 679–685. (2019). DOI: 10.1038/s41586-019-1693-2
One thousand plant transcriptomes and the phylogenomics of green plants
Nature. 574, 679–685. (2019). DOI: 10.1038/s41586-019-1693-2
Gauraha,
N.,
Söderdahl,
F. and Spjuth,
O.
Split Knowledge Transfer in Learning Under Privileged Information Framework
Proceedings of Machine Learning Research (PMLR). 105, 43-52. (2019). URL: proceedings.mlr.press/v105/gauraha19a.html
Split Knowledge Transfer in Learning Under Privileged Information Framework
Proceedings of Machine Learning Research (PMLR). 105, 43-52. (2019). URL: proceedings.mlr.press/v105/gauraha19a.html
Spjuth O.,
Brännström R.C.,
Carlsson L. and Gauraha,
N.
Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets
Proceedings of Machine Learning Research (PMLR). 105, 53-65. (2019). URL: proceedings.mlr.press/v105/spjuth19a.html
Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets
Proceedings of Machine Learning Research (PMLR). 105, 53-65. (2019). URL: proceedings.mlr.press/v105/spjuth19a.html
Grüning BA,
Lampa S,
Vaudel M,
Blankenberg D
Software engineering for scientific big data analysis
Gigascience. 8, 5 (2019). DOI: 10.1093/gigascience/giz054
Software engineering for scientific big data analysis
Gigascience. 8, 5 (2019). DOI: 10.1093/gigascience/giz054
Lampa S,
Dahlö M,
Alvarsson J,
Spjuth O
SciPipe-Turning Scientific Workflows into Computer Programs
IEEE Computing in Science & Engineering. 21, 3, 109-113. (2019). DOI: 10.1109/MCSE.2019.2907814
SciPipe-Turning Scientific Workflows into Computer Programs
IEEE Computing in Science & Engineering. 21, 3, 109-113. (2019). DOI: 10.1109/MCSE.2019.2907814
Gupta A,
Harrison PJ,
Wieslander H,
Pielawski N,
Kartasalo K,
Partel G,
Solorzano L,
Suveer A,
Klemm AH,
Spjuth O,
Sintorn I,
Wählby C
Deep Learning in Image Cytometry: A Review
Cytometry. 95, 4 (2019). DOI: 10.1002/cyto.a.23701
Deep Learning in Image Cytometry: A Review
Cytometry. 95, 4 (2019). DOI: 10.1002/cyto.a.23701
Lampa S,
Dahlö M,
Alvarsson J,
Spjuth O
SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines
Gigascience. 8, 5 (2019). DOI: 10.1093/gigascience/giz044
SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines
Gigascience. 8, 5 (2019). DOI: 10.1093/gigascience/giz044
Herman S,
Niemelä V,
Khoonsari PE,
Sundblom J,
Burman J,
Landtblom AM,
Spjuth O,
Nyholm D,
Kultima K
Alterations in the tyrosine and phenylalanine pathways revealed by biochemical profiling in cerebrospinal fluid of Huntington’s disease subjects
Scientific Reports. 9, 4129 (2019). DOI: 10.1038/s41598-019-40186-5
Alterations in the tyrosine and phenylalanine pathways revealed by biochemical profiling in cerebrospinal fluid of Huntington’s disease subjects
Scientific Reports. 9, 4129 (2019). DOI: 10.1038/s41598-019-40186-5
Khoonsari PE,
Moreno P,
Bergmann S,
Burman J,
Capuccini M,
Carone M,
Cascante M,
de Atauri P,
Foguet C,
Gonzalez-Beltran A,
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,
and Spjuth O
Interoperable and scalable data analysis with microservices: Applications in Metabolomics
Bioinformatics. 35, 19, 3752-3760. (2019). DOI: 10.1093/bioinformatics/btz160
Interoperable and scalable data analysis with microservices: Applications in Metabolomics
Bioinformatics. 35, 19, 3752-3760. (2019). DOI: 10.1093/bioinformatics/btz160
Novella JA,
Khoonsari PE,
Herman S,
Whitenack D,
Capuccini M,
Burman J,
Kultima K,
Spjuth O
Container-based bioinformatics with Pachyderm
Bioinformatics. 35, 5, 839-846. (2019). DOI: 10.1093/bioinformatics/bty699
Container-based bioinformatics with Pachyderm
Bioinformatics. 35, 5, 839-846. (2019). DOI: 10.1093/bioinformatics/bty699
K. Peters,
J. Bradbury,
S. Bergmann,
M. Capuccini,
M. Cascante,
P. de Atauri,
T. M. D. Ebbels,
C. Foguet,
R. Glen,
A. Gonzalez-Beltran,
U. L. Gu ̈nther,
E. Handakas,
T. Hankemeier,
K. Haug,
S. Her- man,
P. Holub,
M. Izzo,
D. Jacob,
D. Johnson,
F. Jourdan,
N. Kale,
I. Karaman,
B. Khalili,
P. E. Khon- sari,
K. Kultima,
S. Lampa,
A. Larsson,
C. Ludwig,
P. Moreno,
S. Neumann,
J. A. Novella,
C. O’Donovan,
J. T. M. Pearce,
A. Peluso,
M. E. Piras,
L. Pireddu,
M. A. C. Reed,
P. Rocca-Serra,
P. Roger,
A. Rosato,
R. Rueedi,
C. Ruttkies,
N. Sadawi,
R. M. Salek,
S.-A. Sansone,
V. Selivanov,
O. Spjuth,
D. Schober,
E. A. Th ́evenot,
M. Tomasoni,
M. van Rijswijk,
M. van Vliet,
M. R. Viant,
R. J. M. Weber,
G. Zanetti,
and C. Steinbeck
PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud
Gigascience. 8, 2, giy149. (2019). DOI: 10.1093/gigascience/giy149
PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud
Gigascience. 8, 2, giy149. (2019). DOI: 10.1093/gigascience/giy149
Herman S,
Åkerfeldt T,
Spjuth O,
Burman J,
Kultima K
Biochemical differences in cerebrospinal fluid between secondary progressive and relapsing-remitting multiple sclerosis
Cells. 8, 2, 84. (2019). DOI: 10.3390/cells8020084
Biochemical differences in cerebrospinal fluid between secondary progressive and relapsing-remitting multiple sclerosis
Cells. 8, 2, 84. (2019). DOI: 10.3390/cells8020084
Kensert A,
Harrison PJ,
Spjuth O
Transfer learning with deep convolutional neural network for classifying cellular morphological changes
SLAS DISCOVERY: Advancing Life Sciences R&D. 24, 4 (2019). DOI: 10.1177/2472555218818756
Transfer learning with deep convolutional neural network for classifying cellular morphological changes
SLAS DISCOVERY: Advancing Life Sciences R&D. 24, 4 (2019). DOI: 10.1177/2472555218818756
2018
Lampa S,
Alvarsson J,
Arvidsson Mc Shane S,
Berg A,
Ahlberg E,
Spjuth O
Predicting off-target binding profiles with confidence using Conformal Prediction
Frontiers in Pharmacology. 9, 1256. (2018). DOI: 10.3389/fphar.2018.01256
Predicting off-target binding profiles with confidence using Conformal Prediction
Frontiers in Pharmacology. 9, 1256. (2018). DOI: 10.3389/fphar.2018.01256
Kensert A,
Alvarsson J,
Norinder U,
Spjuth O.
Evaluating parameters for ligand-based modeling with random forest on sparse data sets
Journal of Cheminformatics. 10, 49 (2018). DOI: 10.1186/s13321-018-0304-9
Evaluating parameters for ligand-based modeling with random forest on sparse data sets
Journal of Cheminformatics. 10, 49 (2018). DOI: 10.1186/s13321-018-0304-9
Spjuth O
Novel applications of Machine Learning in cheminformatics
Journal of Cheminformatics. 10, 46 (2018). DOI: 10.1186/s13321-018-0301-z
Novel applications of Machine Learning in cheminformatics
Journal of Cheminformatics. 10, 46 (2018). DOI: 10.1186/s13321-018-0301-z
Spjuth O. and Larsson A.
Deploying PhenoMeNal virtual research environments on the EGI Federated Cloud
Inspired. 31 (2018). URL: www.egi.eu/wp-content/uploads/2018/06/Inspired-Issue-31.pdf
Deploying PhenoMeNal virtual research environments on the EGI Federated Cloud
Inspired. 31 (2018). URL: www.egi.eu/wp-content/uploads/2018/06/Inspired-Issue-31.pdf
Herman S,
Khoonsari PE,
Tolf A,
Steinmetz J,
Zetterberg H,
Åkerfeldt T,
Jakobsson P-J,
Larsson A,
Spjuth O,
Burman J,
Kultima K
Integration of Magnetic Resonance Imaging and Protein and Metabolite CSF Measurements to Enable Early Diagnosis of Secondary Progressive Multiple Sclerosis
Theranostics. 8, 16, 4477-4490. (2018). DOI: 10.7150/thno.26249
Integration of Magnetic Resonance Imaging and Protein and Metabolite CSF Measurements to Enable Early Diagnosis of Secondary Progressive Multiple Sclerosis
Theranostics. 8, 16, 4477-4490. (2018). DOI: 10.7150/thno.26249
Gauraha N,
Carlsson L,
Spjuth O.
Conformal Prediction in Learning Under Privileged Information Paradigm with Applications in Drug Discovery.
Proceedings of Machine Learning Research. 91, 147-156 (2018). URL: proceedings.mlr.press/v91/gauraha18a.html
Conformal Prediction in Learning Under Privileged Information Paradigm with Applications in Drug Discovery.
Proceedings of Machine Learning Research. 91, 147-156 (2018). URL: proceedings.mlr.press/v91/gauraha18a.html
Svensson F,
Aniceto N,
Norinder U,
Cortes I,
Spjuth O,
Carlsson L,
Bender A.
Conformal Regression for QSAR Modelling - Quantifying Prediction Uncertainty.
Journal of Chemical Information and Modeling. 58, 5, 1132–1140. (2018). DOI: 10.1021/acs.jcim.8b00054
Conformal Regression for QSAR Modelling - Quantifying Prediction Uncertainty.
Journal of Chemical Information and Modeling. 58, 5, 1132–1140. (2018). DOI: 10.1021/acs.jcim.8b00054
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.
Journal of Cheminformatics. 10, 18 (2018). DOI: 10.1186/s13321-018-0271-1
A confidence predictor for logD using conformal regression and a support-vector machine.
Journal of Cheminformatics. 10, 18 (2018). DOI: 10.1186/s13321-018-0271-1
Ahmed L,
Georgiev V,
Capuccini M,
Toor S,
Schaal W,
Laure E,
Spjuth O.
Efficient Iterative Virtual Screening with Apache Spark and Conformal Prediction
Journal of Cheminformatics. 10, 8 (2018). DOI: 10.1186/s13321-018-0265-z
Efficient Iterative Virtual Screening with Apache Spark and Conformal Prediction
Journal of Cheminformatics. 10, 8 (2018). DOI: 10.1186/s13321-018-0265-z
Dahlö M,
Scofield DG,
Schaal W,
Spjuth O.
Tracking the NGS revolution: Managing life science research on shared high-performance computing clusters.
GigaScience. 7, 5 (2018). DOI: 10.1093/gigascience/giy028
Tracking the NGS revolution: Managing life science research on shared high-performance computing clusters.
GigaScience. 7, 5 (2018). DOI: 10.1093/gigascience/giy028
2017
van Rijswijk M,
Beirnaert C,
Caron C,
Cascante M,
Dominguez V,
Dunn W,
Ebbels T,
Giacomoni F,
Gonzalez-Beltran A,
Hankemeier T,
Haug K,
Izquierdo-Garcia J,
Jimenez R,
Jourdan F,
Kale N,
Klapa M,
Kohlbacher O,
Koort K,
Kultima K,
Le Corguill G,
Moschonas N,
Neumann S,
O'Donovan C,
Reczko M,
Rocca-Serra P,
Rosato A,
Salek R,
Sansone S,
Satagopam V,
Schober D,
Shimmo R,
Spicer R,
Spjuth O,
Thevenot E,
Viant M,
Weber R,
Willighagen E,
Zanetti G,
and Steinbeck C
The future of metabolomics in ELIXIR
F1000Research. 6(ELIXIR), 1649 (2017). DOI: 10.12688/f1000research.12342.2
The future of metabolomics in ELIXIR
F1000Research. 6(ELIXIR), 1649 (2017). DOI: 10.12688/f1000research.12342.2
Lampa S,
Willighagen E,
Kohonen P,
King A,
Vrandečić D,
Grafström R,
Spjuth O
RDFIO: extending Semantic MediaWiki for interoperable biomedical data management
Journal of Biomedical Semantics. 8, 35 (2017). DOI: 10.1186/s13326-017-0136-y
RDFIO: extending Semantic MediaWiki for interoperable biomedical data management
Journal of Biomedical Semantics. 8, 35 (2017). DOI: 10.1186/s13326-017-0136-y
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 J-E,
Palmgren J
E-Science technologies in a workflow for personalized medicine using cancer screening as a case study
Journal of the American Medical Informatics Association. 24, 5, 950-957. (2017). DOI: 10.1093/jamia/ocx038
E-Science technologies in a workflow for personalized medicine using cancer screening as a case study
Journal of the American Medical Informatics Association. 24, 5, 950-957. (2017). DOI: 10.1093/jamia/ocx038
Toor S,
Lindberg M,
Fallman I,
Vallin A,
Mohill O,
Freyhult P,
Nilsson L,
Agback M,
Viklund L,
Zazzi H,
Spjuth O,
Capuccini M,
Moller J,
Murtagh D,
Hellander A
SNIC Science Cloud (SSC): A National-scale Cloud Infrastructure for Swedish Academia
e-Science (e-Science), 2017 IEEE 13th International Conference on. 10, 219-227. (2017). DOI: 10.1109/eScience.2017.35
SNIC Science Cloud (SSC): A National-scale Cloud Infrastructure for Swedish Academia
e-Science (e-Science), 2017 IEEE 13th International Conference on. 10, 219-227. (2017). DOI: 10.1109/eScience.2017.35
Sütterlin S,
Dahlö M,
Tellgren-Roth C,
Schaal W,
Melhus Å
High frequency of silver resistance genes in invasive isolates of Enterobacter and Klebsiella species
J Hosp Infect. 96, 3, 256-261. (2017). DOI: 10.1016/j.jhin.2017.04.017
High frequency of silver resistance genes in invasive isolates of Enterobacter and Klebsiella species
J Hosp Infect. 96, 3, 256-261. (2017). DOI: 10.1016/j.jhin.2017.04.017
Arvidsson S,
Carlsson L,
Toccaceli P,
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Prediction of Metabolic Transformations using Cross Venn-ABERS Predictors
Proceedings of Machine Learning Research. 60, 1-14. (2017). URL: proceedings.mlr.press/v60/arvidsson17a.html
Prediction of Metabolic Transformations using Cross Venn-ABERS Predictors
Proceedings of Machine Learning Research. 60, 1-14. (2017). URL: proceedings.mlr.press/v60/arvidsson17a.html
Willighagen E,
Mayfield J,
Alvarsson J,
Berg A,
Carlsson L,
Jeliazkova N,
Kuhn S,
Pluskal T,
Rojas-Chertó M,
Spjuth O,
Torrance G,
Evelo C,
Guha R,
Steinbeck C
The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching
Journal of Cheminformatics. 9, 33 (2017). DOI: 10.1186/s13321-017-0220-4
The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching
Journal of Cheminformatics. 9, 33 (2017). DOI: 10.1186/s13321-017-0220-4
Herman S,
Khoonsari PE,
Aftab O,
Krishnan S,
Strömbom E,
Larsson R,
Hammerling U,
Spjuth O,
Kultima M,
Gustafsson M
Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions
Metabolomics. 13, 79 (2017). DOI: 10.1007/s11306-017-1213-z
Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions
Metabolomics. 13, 79 (2017). DOI: 10.1007/s11306-017-1213-z
Shoombuatong W,
Prathipati P,
Prachayasittikul V,
Schaduangrat N,
Malik AA,
Pratiwi R,
Wanwimolruk S,
Wikberg JES,
Gleeson MP,
Spjuth O,
Nantasenamat C
Towards Predicting the Cytochrome P450 Modulation: From QSAR to proteochemometric modeling
Current Drug Metabolism. 18, 6, 540-555. (2017). DOI: 10.2174/1389200218666170320121932
Towards Predicting the Cytochrome P450 Modulation: From QSAR to proteochemometric modeling
Current Drug Metabolism. 18, 6, 540-555. (2017). DOI: 10.2174/1389200218666170320121932
Capuccini M,
Ahmed L,
Schaal W,
Laure E,
Spjuth O
Large-scale virtual screening on public cloud resources with Apache Spark
Journal of Cheminformatics. 9, 15. (2017). DOI: 10.1186/s13321-017-0204-4
Large-scale virtual screening on public cloud resources with Apache Spark
Journal of Cheminformatics. 9, 15. (2017). DOI: 10.1186/s13321-017-0204-4
Alogheli H,
Olanders G,
Schaal W,
Brandt P,
Karlén A
Docking of Macrocycles: Comparing Rigid and Flexible Docking in Glide
J Chem Inf Model. 57, 2, 190-202. (2017). DOI: 10.1021/acs.jcim.6b00443
Docking of Macrocycles: Comparing Rigid and Flexible Docking in Glide
J Chem Inf Model. 57, 2, 190-202. (2017). DOI: 10.1021/acs.jcim.6b00443
2016
Lampa S,
Alvarsson J,
Spjuth O
Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles
Journal of Cheminformatics. 8, 67. (2016). DOI: 10.1186/s13321-016-0179-6
Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles
Journal of Cheminformatics. 8, 67. (2016). DOI: 10.1186/s13321-016-0179-6
Spjuth O,
Rydberg P,
Willighagen EL,
Evelo CT,
Jeliazkova N
XMetDB: an open access database for xenobiotic metabolism
Journal of Cheminformatics. 8, 47. (2016). DOI: 10.1186/s13321-016-0161-3
XMetDB: an open access database for xenobiotic metabolism
Journal of Cheminformatics. 8, 47. (2016). DOI: 10.1186/s13321-016-0161-3
Alvarsson J,
Lampa S,
Schaal W,
Andersson C,
Wikberg JES,
Spjuth O
Large-scale ligand-based predictive modelling using support vector machines
Journal of Cheminformatics. 8, 39. (2016). DOI: 10.1186/s13321-016-0151-5
Large-scale ligand-based predictive modelling using support vector machines
Journal of Cheminformatics. 8, 39. (2016). DOI: 10.1186/s13321-016-0151-5
Spjuth O,
Bongcam-Rudloff E,
Dahlberg J,
Dahlö M,
Kallio A,
Pireddu L,
Vezzi F,
Korpelainen E
Recommendations on e-infrastructures for next-generation sequencing
GigaScience. 5, 1 (2016). DOI: 10.1186/s13742-016-0132-7
Recommendations on e-infrastructures for next-generation sequencing
GigaScience. 5, 1 (2016). DOI: 10.1186/s13742-016-0132-7
Simeon S,
Spjuth O,
Lapins M,
Nabu S,
Anuwongcharoen N,
Prachayasittikul V,
Wikberg JES,
Nantasenamat C
Origin of aromatase inhibitory activity via proteochemometric modeling
PeerJ. 4, e1979. (2016). DOI: 10.7717/peerj.1979
Origin of aromatase inhibitory activity via proteochemometric modeling
PeerJ. 4, e1979. (2016). DOI: 10.7717/peerj.1979
Spjuth O,
Krestyaninova M,
Hastings J,
Shen H-Y,
Heikkinen J,
Waldenberger M,
Langhammer A,
Ladenvall C,
Esko T,
Persson M-Å,
Heggland J,
Dietrich J,
Ose S,
Gieger C,
Ried JS,
Peters A,
Fortier I,
de Geus EJC,
Klovins J,
Zaharenko L,
Willemsen G,
Hottenga J-J,
Litton J-E,
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
Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research
Eur J Hum Genet. 24, 4, 521--528. (2016). DOI: 10.1038/ejhg.2015.165
Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research
Eur J Hum Genet. 24, 4, 521--528. (2016). DOI: 10.1038/ejhg.2015.165
2015
Capuccini M,
Carlsson L,
Norinder U,
Spjuth O
Conformal Prediction in Spark: Large-Scale Machine Learning with Confidence
Proceedings - 2015 2nd IEEE/ACM International Symposium on Big Data Computing, BDC 2015. , 61-67. (2015). DOI: 10.1109/BDC.2015.35
Conformal Prediction in Spark: Large-Scale Machine Learning with Confidence
Proceedings - 2015 2nd IEEE/ACM International Symposium on Big Data Computing, BDC 2015. , 61-67. (2015). DOI: 10.1109/BDC.2015.35
Grafström RC,
Nymark P,
Hongisto V,
Spjuth O,
Ceder R,
Willighagen E,
Hardy B,
Kaski S,
Kohonen P
Toward the Replacement of Animal Experiments through the Bioinformatics-driven Analysis of 'Omics' Data from Human Cell Cultures
Altern. Lab. Anim.. 43, 5, 325--332. (2015).
Toward the Replacement of Animal Experiments through the Bioinformatics-driven Analysis of 'Omics' Data from Human Cell Cultures
Altern. Lab. Anim.. 43, 5, 325--332. (2015).
Dahlö M,
Haziza F,
Kallio A,
Korpelainen E,
Bongcam-Rudloff E,
Spjuth O
BioImg.org: A catalog of virtual machine images for the life sciences
Bioinformatics and Biology Insights. 9, 125-128. (2015). DOI: 10.4137/BBI.S28636
BioImg.org: A catalog of virtual machine images for the life sciences
Bioinformatics and Biology Insights. 9, 125-128. (2015). DOI: 10.4137/BBI.S28636
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
Experiences with workflows for automating data-intensive bioinformatics
Biology Direct. 10, 1 (2015). DOI: 10.1186/s13062-015-0071-8
Experiences with workflows for automating data-intensive bioinformatics
Biology Direct. 10, 1 (2015). DOI: 10.1186/s13062-015-0071-8
Blom K,
Nygren P,
Alvarsson J,
Larsson R,
Andersson CR
Ex Vivo Assessment of Drug Activity in Patient Tumor Cells as a Basis for Tailored Cancer Therapy
Journal of Laboratory Automation. 21, 1, 178-187. (2015). DOI: 10.1177/2211068215598117
Ex Vivo Assessment of Drug Activity in Patient Tumor Cells as a Basis for Tailored Cancer Therapy
Journal of Laboratory Automation. 21, 1, 178-187. (2015). DOI: 10.1177/2211068215598117
Siretskiy A,
Sundqvist T,
Voznesenskiy M,
Spjuth O
A quantitative assessment of the Hadoop framework for analyzing massively parallel DNA sequencing data
GigaScience. 4, 1 (2015). DOI: 10.1186/s13742-015-0058-5
A quantitative assessment of the Hadoop framework for analyzing massively parallel DNA sequencing data
GigaScience. 4, 1 (2015). DOI: 10.1186/s13742-015-0058-5
Gholami A,
Laure E,
Somogyi P,
Spjuth O,
Niazi S,
Dowling J
Privacy-Preservation for Publishing Sample Availability Data with Personal Identifiers
JOMB. 4, 2, 117--125. (2015). DOI: 10.12720/jomb.4.2.117-125
Privacy-Preservation for Publishing Sample Availability Data with Personal Identifiers
JOMB. 4, 2, 117--125. (2015). DOI: 10.12720/jomb.4.2.117-125
Ahlberg E,
Spjuth O,
Hasselgren C,
Carlsson L
Interpretation of conformal prediction classification models
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9047, 323-334. (2015). DOI: 10.1007/978-3-319-17091-6_27
Interpretation of conformal prediction classification models
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9047, 323-334. (2015). DOI: 10.1007/978-3-319-17091-6_27
Lindh M,
Svensson F,
Schaal W,
Zhang J,
Sköld C,
Brandt P,
Karlén A
Toward a benchmarking data set able to evaluate ligand- and structure-based virtual screening using public HTS data
J Chem Inf Model. 55, 2, 343-353. (2015). DOI: 10.1021/ci5005465
Toward a benchmarking data set able to evaluate ligand- and structure-based virtual screening using public HTS data
J Chem Inf Model. 55, 2, 343-353. (2015). DOI: 10.1021/ci5005465
Moghadam BT,
Alvarsson J,
Holm M,
Eklund M,
Carlsson L,
Spjuth O
Scaling predictive modeling in drug development with cloud computing
Journal of Chemical Information and Modeling. 55, 1, 19-25. (2015). DOI: 10.1021/ci500580y
Scaling predictive modeling in drug development with cloud computing
Journal of Chemical Information and Modeling. 55, 1, 19-25. (2015). DOI: 10.1021/ci500580y
2014
Siretskiy A,
Spjuth O
HTSeq-Hadoop: Extending HTSeq for massively parallel sequencing data analysis using Hadoop
Proceedings - 2014 IEEE 10th International Conference on eScience, eScience 2014. 1, 317-323. (2014). DOI: 10.1109/eScience.2014.27
HTSeq-Hadoop: Extending HTSeq for massively parallel sequencing data analysis using Hadoop
Proceedings - 2014 IEEE 10th International Conference on eScience, eScience 2014. 1, 317-323. (2014). DOI: 10.1109/eScience.2014.27
Alvarsson J,
Eklund M,
Andersson C,
Carlsson L,
Spjuth O,
Wikberg JES
Benchmarking Study of Parameter Variation When Using Signature Fingerprints Together with Support Vector Machines
Journal of Chemical Information and Modeling. 54, 11, 3211--3217. (2014). DOI: 10.1021/ci500344v
Benchmarking Study of Parameter Variation When Using Signature Fingerprints Together with Support Vector Machines
Journal of Chemical Information and Modeling. 54, 11, 3211--3217. (2014). DOI: 10.1021/ci500344v
Alvarsson J,
Eklund M,
Engkvist O,
Spjuth O,
Carlsson L,
Wikberg JES,
Noeske T
Ligand-based target prediction with signature fingerprints
Journal of Chemical Information and Modeling. 54, 10, 2647–2653. (2014). DOI: 10.1021/ci500361u
Ligand-based target prediction with signature fingerprints
Journal of Chemical Information and Modeling. 54, 10, 2647–2653. (2014). DOI: 10.1021/ci500361u
Spjuth O,
Heikkinen J,
Litton J-E,
Palmgren J,
Krestyaninova M
Data integration between Swedish national clinical health registries and biobanks using an availability system
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8574 LNBI, 32-40. (2014). DOI: 10.1007/978-3-319-08590-6_3
Data integration between Swedish national clinical health registries and biobanks using an availability system
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8574 LNBI, 32-40. (2014). DOI: 10.1007/978-3-319-08590-6_3
Spjuth O
NGS data management and analysis for hundreds of projects: Experiences from Sweden
EMBnet.journal. 20, A, 761. (2014). DOI: 10.14806/ej.20.a.761
NGS data management and analysis for hundreds of projects: Experiences from Sweden
EMBnet.journal. 20, A, 761. (2014). DOI: 10.14806/ej.20.a.761
Kohonen P,
Ceder R,
Smit I,
Hongisto V,
Myatt G,
Hardy B,
Spjuth O,
Grafström R
Cancer biology, toxicology and alternative methods development go hand-in-hand.
Basic & Clinical Pharmacology & Toxicology. 15, 1, 50-58. (2014). DOI: 10.1111/bcpt.12257
Cancer biology, toxicology and alternative methods development go hand-in-hand.
Basic & Clinical Pharmacology & Toxicology. 15, 1, 50-58. (2014). DOI: 10.1111/bcpt.12257
2013
Ahmed L,
Edlund A,
Laure E,
Spjuth O
Using iterative MapReduce for parallel virtual screening
Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom. 2, 27-32. (2013). DOI: 10.1109/CloudCom.2013.99
Using iterative MapReduce for parallel virtual screening
Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom. 2, 27-32. (2013). DOI: 10.1109/CloudCom.2013.99
Spjuth O,
Carlsson L,
Alvarsson J,
Georgiev V,
Willighagen E,
Eklund M
Open Source Drug Discovery with Bioclipse
Current Topics in Medicinal Chemistry. 12, 18 (2013). DOI: 10.2174/1568026611212180005
Open Source Drug Discovery with Bioclipse
Current Topics in Medicinal Chemistry. 12, 18 (2013). DOI: 10.2174/1568026611212180005
Schaal W,
Hammerling U,
Gustafsson MG,
Spjuth O
Automated QuantMap for rapid quantitative molecular network topology analysis
Bioinformatics. 29, 18, 2369-2370. (2013). DOI: 10.1093/bioinformatics/btt390
Automated QuantMap for rapid quantitative molecular network topology analysis
Bioinformatics. 29, 18, 2369-2370. (2013). DOI: 10.1093/bioinformatics/btt390
Lampa S,
Dahlö M,
Olason PI,
Hagberg J,
Spjuth O
Lessons learned from implementing a national infrastructure in Sweden for storage and analysis of next-generation sequencing data
GigaScience. 2, 1 (2013). DOI: 10.1186/2047-217x-2-9
Lessons learned from implementing a national infrastructure in Sweden for storage and analysis of next-generation sequencing data
GigaScience. 2, 1 (2013). DOI: 10.1186/2047-217x-2-9
Lapins M,
Worachartcheewan A,
Spjuth O,
Georgiev V,
Prachayasittikul V,
Nantasenamat C,
Wikberg JES
A Unified Proteochemometric Model for Prediction of Inhibition of Cytochrome P450 Isoforms
PLoS One. 8, 6, e66566. (2013). DOI: 10.1371/journal.pone.0066566
A Unified Proteochemometric Model for Prediction of Inhibition of Cytochrome P450 Isoforms
PLoS One. 8, 6, e66566. (2013). DOI: 10.1371/journal.pone.0066566
Rostkowski M,
Spjuth O,
Rydberg P
WhichCyp: prediction of cytochromes P450 inhibition.
Bioinformatics. 29, 16, 2051-2052. (2013). DOI: 10.1093/bioinformatics/btt325
WhichCyp: prediction of cytochromes P450 inhibition.
Bioinformatics. 29, 16, 2051-2052. (2013). DOI: 10.1093/bioinformatics/btt325
Willighagen EL,
Waagmeester A,
Spjuth O,
Ansell P,
Williams AJ,
Tkachenko V,
Hastings J,
Chen B,
Wild DJ
The ChEMBL database as linked open data
Journal of Cheminformatics. 5, 23 (2013). DOI: 10.1186/1758-2946-5-23
The ChEMBL database as linked open data
Journal of Cheminformatics. 5, 23 (2013). DOI: 10.1186/1758-2946-5-23
Claesson A,
Spjuth O
On mechanisms of reactive metabolite formation from drugs.
Mini Rev Med Chem. 13, 5, 720-9. (2013).
On mechanisms of reactive metabolite formation from drugs.
Mini Rev Med Chem. 13, 5, 720-9. (2013).
Spjuth O,
Berg A,
Adams S,
Willighagen EL
Applications of the InChI in cheminformatics with the CDK and Bioclipse
Journal of Cheminformatics. 5, 3 (2013). DOI: 10.1186/1758-2946-5-14
Applications of the InChI in cheminformatics with the CDK and Bioclipse
Journal of Cheminformatics. 5, 3 (2013). DOI: 10.1186/1758-2946-5-14
2012
Spjuth O,
Georgiev V,
Carlsson L,
Alvarsson J,
Berg A,
Willighagen E,
Wikberg JES,
Eklund M
Bioclipse-R: integrating management and visualization of life science data with statistical analysis.
Bioinformatics. 29, 2, 286-289. (2012). DOI: 10.1093/bioinformatics/bts681
Bioclipse-R: integrating management and visualization of life science data with statistical analysis.
Bioinformatics. 29, 2, 286-289. (2012). DOI: 10.1093/bioinformatics/bts681
Spjuth O,
Carlsson L,
Alvarsson J,
Georgiev V,
Willighagen E,
Eklund M
Open source drug discovery with Bioclipse
Current Topics in Medicinal Chemistry. 12, 18, 1980-1986. (2012). DOI: 10.2174/156802612804910287
Open source drug discovery with Bioclipse
Current Topics in Medicinal Chemistry. 12, 18, 1980-1986. (2012). DOI: 10.2174/156802612804910287
Williams AJ,
Ekins S,
Spjuth O,
Willighagen EL
Accessing, using, and creating chemical property databases for computational toxicology modeling
Computational Toxicology, Methods in Molecular Biology. 929, 221-241. (2012). DOI: 10.1007/978-1-62703-050-2_10
Accessing, using, and creating chemical property databases for computational toxicology modeling
Computational Toxicology, Methods in Molecular Biology. 929, 221-241. (2012). DOI: 10.1007/978-1-62703-050-2_10
Willighagen E,
Affentranger R,
Grafström R,
Hardy B,
Jeliazkova N,
Spjuth O
Interactive predictive toxicology with Bioclipse and OpenTox
Open Source Software in Life Science Research. , 35--61. (2012). DOI: 10.1533/9781908818249.35
Interactive predictive toxicology with Bioclipse and OpenTox
Open Source Software in Life Science Research. , 35--61. (2012). DOI: 10.1533/9781908818249.35
Hardy B,
Apic G,
Carthew P,
Clark D,
Cook D,
Escher S,
Dix I,
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
Food for thought ... A toxicology ontology roadmap
ALTEX. 29, 2, 127-138. (2012). DOI: 10.14573/altex.2012.2.129
Food for thought ... A toxicology ontology roadmap
ALTEX. 29, 2, 127-138. (2012). DOI: 10.14573/altex.2012.2.129
Carlsson L,
Spjuth O,
Eklund M,
Boyer S
Model building in Bioclipse Decision Support applied to open datasets
Toxicology Letters. 211, S62. (2012). DOI: 10.1016/j.toxlet.2012.03.243
Model building in Bioclipse Decision Support applied to open datasets
Toxicology Letters. 211, S62. (2012). DOI: 10.1016/j.toxlet.2012.03.243
Spjuth O,
Willighagen E,
Hammerling U,
Dencker L,
Grafström R
A novel infrastructure for chemical safety predictions with focus on human health
Toxicology Letters. 211, S59. (2012). DOI: 10.1016/j.toxlet.2012.03.234
A novel infrastructure for chemical safety predictions with focus on human health
Toxicology Letters. 211, S59. (2012). DOI: 10.1016/j.toxlet.2012.03.234
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
Toxicology Ontology Perspectives
ALTEX. 29, 2, 139-156. (2012). DOI: 10.14573/altex.2012.2.139
Toxicology Ontology Perspectives
ALTEX. 29, 2, 139-156. (2012). DOI: 10.14573/altex.2012.2.139
Lampa S,
Hagberg J,
Spjuth O
UPPNEX - A solution for Next Generation Sequencing data management and analysis
EMBnet journal. 17, B, 44. (2012). DOI: 10.14806/ej.17.b.274
UPPNEX - A solution for Next Generation Sequencing data management and analysis
EMBnet journal. 17, B, 44. (2012). DOI: 10.14806/ej.17.b.274
Krestyaninova M,
Spjuth O,
Hastings J,
Dietrich J,
Rebholz-Schuhmann D
Biobank Metaportal to Enhance Collaborative Research: sail.simbioms.org
Journal of Systemics, Cybernetics and Informatics. 10 (2012). URL: www.iiisci.org/journal/sci/Abstract.asp?var=&id=HCT875XS
Biobank Metaportal to Enhance Collaborative Research: sail.simbioms.org
Journal of Systemics, Cybernetics and Informatics. 10 (2012). URL: www.iiisci.org/journal/sci/Abstract.asp?var=&id=HCT875XS
2011
Wikberg JES,
Spjuth O,
Eklund M,
Lapins M
Chemoinformatics Taking Biology into Account: Proteochemometrics
Computational Approaches in Cheminformatics and Bioinformatics. , 57-92. (2011). DOI: 10.1002/9781118131411.ch3
Chemoinformatics Taking Biology into Account: Proteochemometrics
Computational Approaches in Cheminformatics and Bioinformatics. , 57-92. (2011). DOI: 10.1002/9781118131411.ch3
Willighagen EL,
Jeliazkova N,
Hardy B,
Grafström RC,
Spjuth O
Computational toxicology using the OpenTox application programming interface and Bioclipse
BMC Research Notes. 4 (2011). DOI: 10.1186/1756-0500-4-487
Computational toxicology using the OpenTox application programming interface and Bioclipse
BMC Research Notes. 4 (2011). DOI: 10.1186/1756-0500-4-487
O'Boyle NM,
Guha R,
Willighagen EL,
Adams SE,
Alvarsson J,
Bradley,
J-C,
Filippov IV,
Hanson RM,
Hanwell MD,
Hutchison GR,
James CA,
Jeliazkova N,
Lang ASID,
Langner KM,
Lonie DC,
Lowe DM,
Pansanel,
Jérôme,
Pavlov D,
Spjuth O,
Steinbeck C,
Tenderholt AL,
Theisen KJ,
Murray-Rust P
Open Data, Open Source and Open Standards in chemistry: The Blue Obelisk five years on.
Journal of Cheminformatics. 3, 37 (2011). DOI: 10.1186/1758-2946-3-37
Open Data, Open Source and Open Standards in chemistry: The Blue Obelisk five years on.
Journal of Cheminformatics. 3, 37 (2011). DOI: 10.1186/1758-2946-3-37
Spjuth O,
Eklund M,
Ahlberg Helgee E,
Boyer S,
Carlsson L
Integrated decision support for assessing chemical liabilities
Journal of Chemical Information and Modeling. 51, 8, 1840-1847. (2011). DOI: 10.1021/ci200242c
Integrated decision support for assessing chemical liabilities
Journal of Chemical Information and Modeling. 51, 8, 1840-1847. (2011). DOI: 10.1021/ci200242c
Alvarsson J,
Andersson C,
Spjuth O,
Larsson R,
Wikberg JES
Brunn: An open source laboratory information system for microplates with a graphical plate layout design process
BMC Bioinformatics. 12 (2011). DOI: 10.1186/1471-2105-12-179
Brunn: An open source laboratory information system for microplates with a graphical plate layout design process
BMC Bioinformatics. 12 (2011). DOI: 10.1186/1471-2105-12-179
Guha R,
Spjuth O,
Willighagen E
Collaborative Cheminformatics Applications
Collaborative Computational Technologies for Biomedical Research. , 399-422. (2011). DOI: 10.1002/9781118026038.ch24
Collaborative Cheminformatics Applications
Collaborative Computational Technologies for Biomedical Research. , 399-422. (2011). DOI: 10.1002/9781118026038.ch24
Spjuth O,
Eklund M,
Lapins M,
Junaid M,
Wikberg JES
Services for prediction of drug susceptibility for HIV proteases and reverse transcriptases at the HIV drug research centre
Bioinformatics. 27, 12, 1719-1720. (2011). DOI: 10.1093/bioinformatics/btr192
Services for prediction of drug susceptibility for HIV proteases and reverse transcriptases at the HIV drug research centre
Bioinformatics. 27, 12, 1719-1720. (2011). DOI: 10.1093/bioinformatics/btr192
Willighagen EL,
Alvarsson J,
Andersson A,
Eklund M,
Lampa S,
Lapins M,
Spjuth O,
Wikberg JES
Linking the Resource Description Framework to cheminformatics and proteochemometrics
Journal of Biomedical Semantics. 2, 1 (2011). DOI: 10.1186/2041-1480-2-S1-S6
Linking the Resource Description Framework to cheminformatics and proteochemometrics
Journal of Biomedical Semantics. 2, 1 (2011). DOI: 10.1186/2041-1480-2-S1-S6
2010
Spjuth O
Proteochemometric modeling of the susceptibility of mutated variants of the HIV-1 virus to reverse transcriptase inhibitors
PLoS One. 5, 12 (2010). DOI: 10.1371/journal.pone.0014353
Proteochemometric modeling of the susceptibility of mutated variants of the HIV-1 virus to reverse transcriptase inhibitors
PLoS One. 5, 12 (2010). DOI: 10.1371/journal.pone.0014353
Carlsson L,
Spjuth O,
Adams S,
Glen RC,
Boyer S
Use of historic metabolic biotransformation data as a means of anticipating metabolic sites using MetaPrint2D and Bioclipse
BMC Bioinformatics. 11 (2010). DOI: 10.1186/1471-2105-11-362
Use of historic metabolic biotransformation data as a means of anticipating metabolic sites using MetaPrint2D and Bioclipse
BMC Bioinformatics. 11 (2010). DOI: 10.1186/1471-2105-11-362
Spjuth O,
Willighagen EL,
Guha R,
Eklund M,
Wikberg JES
Towards interoperable and reproducible QSAR analyses: Exchange of datasets
Journal of Cheminformatics. 2, 1 (2010). DOI: 10.1186/1758-2946-2-5
Towards interoperable and reproducible QSAR analyses: Exchange of datasets
Journal of Cheminformatics. 2, 1 (2010). DOI: 10.1186/1758-2946-2-5
Eklund M,
Spjuth O,
Wikberg JES
An eScience-Bayes strategy for analyzing omics data
BMC Bioinformatics. 11 (2010). DOI: 10.1186/1471-2105-11-282
An eScience-Bayes strategy for analyzing omics data
BMC Bioinformatics. 11 (2010). DOI: 10.1186/1471-2105-11-282
2009
Spjuth O,
Alvarsson J,
Berg A,
Eklund M,
Kuhn S,
Mäsak C,
Torrance G,
Wagener J,
Willighagen EL,
Steinbeck C,
Wikberg JES
Bioclipse 2: A scriptable integration platform for the life sciences
BMC Bioinformatics. 10 (2009). DOI: 10.1186/1471-2105-10-397
Bioclipse 2: A scriptable integration platform for the life sciences
BMC Bioinformatics. 10 (2009). DOI: 10.1186/1471-2105-10-397
Wagener J,
Spjuth O,
Willighagen EL,
Wikberg JES
XMPP for cloud computing in bioinformatics supporting discovery and invocation of asynchronous web services
BMC Bioinformatics. 10, 279. (2009). DOI: 10.1186/1471-2105-10-279
XMPP for cloud computing in bioinformatics supporting discovery and invocation of asynchronous web services
BMC Bioinformatics. 10, 279. (2009). DOI: 10.1186/1471-2105-10-279
2008
Eklund M,
Spjuth O,
Wikberg JES
The C1C2: A framework for simultaneous model selection and assessment
BMC Bioinformatics. 9 (2008). DOI: 10.1186/1471-2105-9-360
The C1C2: A framework for simultaneous model selection and assessment
BMC Bioinformatics. 9 (2008). DOI: 10.1186/1471-2105-9-360
Lapins M,
Eklund M,
Spjuth O,
Prusis P,
Wikberg JES
Proteochemometric modeling of HIV protease susceptibility
BMC Bioinformatics. 9 (2008). DOI: 10.1186/1471-2105-9-181
Proteochemometric modeling of HIV protease susceptibility
BMC Bioinformatics. 9 (2008). DOI: 10.1186/1471-2105-9-181
2007
Spjuth O,
Helmus T,
Willighagen EL,
Kuhn S,
Eklund M,
Wagener J,
Murray-Rust P,
Steinbeck C,
Wikberg JES
Bioclipse: An open source workbench for chemo- and bioinformatics
BMC Bioinformatics. 8 (2007). DOI: 10.1186/1471-2105-8-59
Bioclipse: An open source workbench for chemo- and bioinformatics
BMC Bioinformatics. 8 (2007). DOI: 10.1186/1471-2105-8-59
2006
Ameur A,
Yankovski V,
Enroth S,
Spjuth O,
Komorowski J.
The LCB Data Warehouse
Bioinformatics. 22, 8, 1024-6. (2006). DOI: 10.1093/bioinformatics/btl036
The LCB Data Warehouse
Bioinformatics. 22, 8, 1024-6. (2006). DOI: 10.1093/bioinformatics/btl036
