Farmaceutisk Bioinformatik

Data management of scientific applications in a reinforcement learning-based hierarchical storage system

Zhang, Tianru; Gupta, Ankit; Francisco Rodríguez, María Andreína; Spjuth, Ola et al.

Ingår i Expert systems with applications, 2024

• DOI för Data management of scientific applications in a reinforcement learning-based hierarchical storage system
• Ladda ner fulltext (pdf) av Data management of scientific applications in a reinforcement learning-based hierarchical storage system

Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data

Seal, Srijit; Yang, Hongbin; Trapotsi, Maria-Anna; Singh, Satvik et al.

Ingår i Journal of Cheminformatics, 2023

• DOI för Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data
• Ladda ner fulltext (pdf) av Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data

In Silico Prediction of Human Clinical Pharmacokinetics with ANDROMEDA by Prosilico: Predictions for an Established Benchmarking Data Set, a Modern Small Drug Data Set, and a Comparison with Laboratory Methods

Fagerholm, Urban; Hellberg, Sven; Alvarsson, Jonathan; Spjuth, Ola

Ingår i ATLA (Alternatives to Laboratory Animals), s. 39-54, 2023

• DOI för In Silico Prediction of Human Clinical Pharmacokinetics with ANDROMEDA by Prosilico: Predictions for an Established Benchmarking Data Set, a Modern Small Drug Data Set, and a Comparison with Laboratory Methods

Development of new approach methods for the identification and characterization of endocrine metabolic disruptors: a PARC project

Braeuning, Albert; Balaguer, Patrick; Bourguet, William; Carreras-Puigvert, Jordi et al.

Ingår i Frontiers in Toxicology, 2023

• DOI för Development of new approach methods for the identification and characterization of endocrine metabolic disruptors: a PARC project
• Ladda ner fulltext (pdf) av Development of new approach methods for the identification and characterization of endocrine metabolic disruptors: a PARC project

Evaluating the utility of brightfield image data for mechanism of action prediction

Harrison, Philip John; Gupta, Ankit; Rietdijk, Jonne; Wieslander, Håkan et al.

Ingår i PloS Computational Biology, 2023

• DOI för Evaluating the utility of brightfield image data for mechanism of action prediction
• Ladda ner fulltext (pdf) av Evaluating the utility of brightfield image data for mechanism of action prediction

Designing microplate layouts using artificial intelligence

Francisco Rodríguez, María Andreína; Carreras-Puigvert, Jordi; Spjuth, Ola

Ingår i Artificial Intelligence in the Life Sciences, 2023

• DOI för Designing microplate layouts using artificial intelligence
• Ladda ner fulltext (pdf) av Designing microplate layouts using artificial intelligence

Disease phenotype prediction in multiple sclerosis

Herman, Stephanie; Arvidsson McShane, Staffan; Zhukovsky, Christina; Emami, Payam et al.

Ingår i iScience, 2023

• DOI för Disease phenotype prediction in multiple sclerosis
• Ladda ner fulltext (pdf) av Disease phenotype prediction in multiple sclerosis

Predicting protein network topology clusters from chemical structure using deep learning

Sreenivasan, Akshai P.; Harrison, Philip J; Schaal, Wesley; Matuszewski, Damian J. et al.

Ingår i Journal of Cheminformatics, 2022

• DOI för Predicting protein network topology clusters from chemical structure using deep learning
• Ladda ner fulltext (pdf) av Predicting protein network topology clusters from chemical structure using deep learning

In silico predictions of the human pharmacokinetics/toxicokinetics of 65 chemicals from various classes using conformal prediction methodology

Fagerholm, Urban; Hellberg, Sven; Alvarsson, Jonathan; Spjuth, Ola

Ingår i Xenobiotica, s. 113-118, 2022

• DOI för In silico predictions of the human pharmacokinetics/toxicokinetics of 65 chemicals from various classes using conformal prediction methodology

From biomedical cloud platforms to microservices: next steps in FAIR data and analysis

Sheffield, Nathan C.; Bonazzi, Vivien R.; Bourne, Philip E.; Burdett, Tony et al.

Ingår i Scientific Data, 2022

• DOI för From biomedical cloud platforms to microservices: next steps in FAIR data and analysis
• Ladda ner fulltext (pdf) av From biomedical cloud platforms to microservices: next steps in FAIR data and analysis

Migrating to Long-Read Sequencing for Clinical Routine BCR-ABL1 TKI Resistance Mutation Screening

Schaal, Wesley; Ameur, Adam; Olsson-Strömberg, Ulla; Hermansson, Monica et al.

Ingår i Cancer Informatics, s. 1-8, 2022

• DOI för Migrating to Long-Read Sequencing for Clinical Routine BCR-ABL1 TKI Resistance Mutation Screening
• Ladda ner fulltext (pdf) av Migrating to Long-Read Sequencing for Clinical Routine BCR-ABL1 TKI Resistance Mutation Screening

Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction

Olsson, Henrik; Kartasalo, Kimmo; Mulliqi, Nita; Capuccini, Marco et al.

Ingår i Nature Communications, 2022

• DOI för Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction
• Ladda ner fulltext (pdf) av Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction

A method for Boolean analysis of protein interactions at a molecular level

Raykova, Doroteya; Kermpatsou, Despoina; Malmqvist, Tony; Harrison, Philip J et al.

Ingår i Nature Communications, 2022

• DOI för A method for Boolean analysis of protein interactions at a molecular level
• Ladda ner fulltext (pdf) av A method for Boolean analysis of protein interactions at a molecular level

Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects

Rietdijk, Jonne; Aggarwal, Tanya; Georgiev, Polina; Lapins, Maris et al.

Ingår i Science of the Total Environment, 2022

• DOI för Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects
• Ladda ner fulltext (pdf) av Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects

In Silico Predictions of the Gastrointestinal Uptake of Macrocycles in Man Using Conformal Prediction Methodology

Fagerholm, Urban; Hellberg, Sven; Alvarsson, Jonathan; Spjuth, Ola

Ingår i Journal of Pharmaceutical Sciences, s. 2614-2619, 2022

• DOI för In Silico Predictions of the Gastrointestinal Uptake of Macrocycles in Man Using Conformal Prediction Methodology

The Impact of Reference Data Selection for the Prediction Accuracy of Intrinsic Hepatic Metabolic Clearance

Fagerholm, Urban; Spjuth, Ola; Hellberg, Sven

Ingår i Journal of Pharmaceutical Sciences, s. 2645-2649, 2022

• DOI för The Impact of Reference Data Selection for the Prediction Accuracy of Intrinsic Hepatic Metabolic Clearance

An Open-Source Modular Framework for Automated Pipetting and Imaging Applications

Ouyang, Wei; Bowman, Richard W; Wang, Haoran; Bumke, Kaspar E et al.

Ingår i Advanced biology, 2022

• DOI för An Open-Source Modular Framework for Automated Pipetting and Imaging Applications
• Ladda ner fulltext (pdf) av An Open-Source Modular Framework for Automated Pipetting and Imaging Applications

Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection

Seal, Srijit; Carreras-Puigvert, Jordi; Trapotsi, Maria-Anna; Yang, Hongbin et al.

Ingår i Communications Biology, 2022

• DOI för Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection
• Ladda ner fulltext (pdf) av Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection

SimVec: predicting polypharmacy side effects for new drugs

Lukashina, Nina; Kartysheva, Elena; Spjuth, Ola; Virko, Elizaveta et al.

Ingår i Journal of Cheminformatics, 2022

• DOI för SimVec: predicting polypharmacy side effects for new drugs
• Ladda ner fulltext (pdf) av SimVec: predicting polypharmacy side effects for new drugs

Synergy Conformal Prediction

Gauraha, Niharika; Spjuth, Ola

Ingår i Proceedings of Machine Learning Research, s. 91-119, 2021

Deep learning and conformal prediction for hierarchical analysis of large-scale whole-slide tissue images

Wieslander, Håkan; Harrison, Philip J.; Skogberg, Gabriel; Jackson, Sonya et al.

Ingår i IEEE journal of biomedical and health informatics, s. 371-380, 2021

• DOI för Deep learning and conformal prediction for hierarchical analysis of large-scale whole-slide tissue images
• Ladda ner fulltext (pdf) av Deep learning and conformal prediction for hierarchical analysis of large-scale whole-slide tissue images

Approaches for containerized scientific workflows in cloud environments with applications in life science

Spjuth, Ola; Capuccini, Marco; Carone, Matteo; Larsson, Anders et al.

Ingår i F1000 Research, s. 513-513, 2021

• DOI för Approaches for containerized scientific workflows in cloud environments with applications in life science

ELIXIR and Toxicology: a community in development

Martens, Marvin; Stierum, Rob; Schymanski, Emma L.; Evelo, Chris T. et al.

Ingår i F1000 Research, s. 1129-1129, 2021

• DOI för ELIXIR and Toxicology: a community in development
• Ladda ner fulltext (pdf) av ELIXIR and Toxicology: a community in development

Assessing the calibration in toxicological in vitro models with conformal prediction

Morger, Andrea; Svensson, Fredrik; Arvidsson McShane, Staffan; Gauraha, Niharika et al.

Ingår i Journal of Cheminformatics, 2021

• DOI för Assessing the calibration in toxicological in vitro models with conformal prediction
• Ladda ner fulltext (pdf) av Assessing the calibration in toxicological in vitro models with conformal prediction

Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning

Norinder, Ulf; Spjuth, Ola; Svensson, Fredrik

Ingår i Journal of Cheminformatics, 2021

• DOI för Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning
• Ladda ner fulltext (pdf) av Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning

The machine learning life cycle and the cloud: implications for drug discovery.

Spjuth, Ola; Frid, Jens; Hellander, Andreas

Ingår i Expert Opinion on Drug Discovery, s. 1071-1079, 2021

• DOI för The machine learning life cycle and the cloud: implications for drug discovery.

scConnect: a method for exploratory analysis of cell–cell communication based on single-cell RNA-sequencing data

Jakobsson, Jon E. T.; Spjuth, Ola; Lagerström, Malin C.

Ingår i Bioinformatics, s. 3501-3508, 2021

• DOI för scConnect: a method for exploratory analysis of cell–cell communication based on single-cell RNA-sequencing data
• Ladda ner fulltext (pdf) av scConnect: a method for exploratory analysis of cell–cell communication based on single-cell RNA-sequencing data

Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit

Blamey, Ben; Toor, Salman; Dahlö, Martin; Wieslander, Håkan et al.

Ingår i GigaScience, s. 1-14, 2021

• DOI för Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit
• Ladda ner fulltext (pdf) av Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit

Machine Learning Strategies When Transitioning between Biological Assays

Arvidsson McShane, Staffan; Ahlberg, Ernst; Noeske, Tobias; Spjuth, Ola

Ingår i Journal of Chemical Information and Modeling, s. 3722-3733, 2021

• DOI för Machine Learning Strategies When Transitioning between Biological Assays
• Ladda ner fulltext (pdf) av Machine Learning Strategies When Transitioning between Biological Assays

In silico prediction of volume of distribution of drugs in man using conformal prediction performs on par with animal data-based models

Fagerholm, Urban; Hellberg, Sven; Alvarsson, Jonathan; Arvidsson McShane, Staffan et al.

Ingår i Xenobiotica, s. 1366-1371, 2021

• DOI för In silico prediction of volume of distribution of drugs in man using conformal prediction performs on par with animal data-based models

Comparison between lab variability and in silico prediction errors for the unbound fraction of drugs in human plasma

Fagerholm, Urban; Spjuth, Ola; Hellberg, Sven

Ingår i Xenobiotica, s. 1095-1100, 2021

• DOI för Comparison between lab variability and in silico prediction errors for the unbound fraction of drugs in human plasma
• Ladda ner fulltext (pdf) av Comparison between lab variability and in silico prediction errors for the unbound fraction of drugs in human plasma

Integrating Statistical and Machine-Learning Approach for Meta-Analysis of Bisphenol A-Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival

Lukashina, Nina; Williams, Michael J.; Kartysheva, Elena; Virko, Elizaveta et al.

Ingår i International Journal of Molecular Sciences, 2021

• DOI för Integrating Statistical and Machine-Learning Approach for Meta-Analysis of Bisphenol A-Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival
• Ladda ner fulltext (pdf) av Integrating Statistical and Machine-Learning Approach for Meta-Analysis of Bisphenol A-Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival

Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology

Fagerholm, Urban; Hellberg, Sven; Spjuth, Ola

Ingår i Molecules, 2021

• DOI för Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology
• Ladda ner fulltext (pdf) av Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology

Metabolomics: The Stethoscope for the Twenty-First Century

Ashrafian, Hutan; Sounderajah, Viknesh; Glen, Robert; Ebbels, Timothy et al.

Ingår i Medical principles and practice, s. 301-310, 2021

• DOI för Metabolomics: The Stethoscope for the Twenty-First Century
• Ladda ner fulltext (pdf) av Metabolomics: The Stethoscope for the Twenty-First Century

Deep-learning models for lipid nanoparticle-based drug delivery

Harrison, Philip J.; Wieslander, Håkan; Sabirsh, Alan; Karlsson, Johan et al.

Ingår i Nanomedicine, s. 1097-1110, 2021

• DOI för Deep-learning models for lipid nanoparticle-based drug delivery

A phenomics approach for antiviral drug discovery

Rietdijk, Jonne; Tampere, Marianna; Pettke, Aleksandra; Georgiev, Polina et al.

Ingår i BMC Biology, 2021

• DOI för A phenomics approach for antiviral drug discovery
• Ladda ner fulltext (pdf) av A phenomics approach for antiviral drug discovery

Predicting target profiles with confidence as a service using docking scores

Ahmed, Laeeq; Alogheli, Hiba; Arvidsson Mc Shane, Staffan; Alvarsson, Jonathan et al.

Ingår i Journal of Cheminformatics, 2020

• DOI för Predicting target profiles with confidence as a service using docking scores
• Ladda ner fulltext (pdf) av Predicting target profiles with confidence as a service using docking scores

Using Predicted Bioactivity Profiles to Improve Predictive Modeling

Norinder, Ulf; Spjuth, Ola; Svensson, Fredrik

Ingår i Journal of Chemical Information and Modeling, s. 2830-2837, 2020

• DOI för Using Predicted Bioactivity Profiles to Improve Predictive Modeling

MaRe: Processing Big Data with application containers on Apache Spark

Capuccini, Marco; Dahlö, Martin; Toor, Salman; Spjuth, Ola

Ingår i GigaScience, 2020

• DOI för MaRe: Processing Big Data with application containers on Apache Spark
• Ladda ner fulltext (pdf) av MaRe: Processing Big Data with application containers on Apache Spark

Container-based bioinformatics with Pachyderm

Novella, Jon Ander; Emami Khoonsari, Payam; Herman, Stephanie; Whitenack, Daniel et al.

Ingår i Bioinformatics, s. 839-846, 2019

• DOI för Container-based bioinformatics with Pachyderm

SciPipe - Turning Scientific Workflows into Computer Programs

Lampa, Samuel; Dahlö, Martin; Alvarsson, Jonathan; Spjuth, Ola

Ingår i Computing in science & engineering (Print), s. 109-113, 2019

• DOI för SciPipe - Turning Scientific Workflows into Computer Programs
• Ladda ner fulltext (pdf) av SciPipe - Turning Scientific Workflows into Computer Programs

On-demand virtual research environments using microservices

Capuccini, Marco; Larsson, Anders; Carone, Matteo; Novella, Jon Ander et al.

Ingår i PeerJ Computer Science, 2019

• DOI för On-demand virtual research environments using microservices

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

Herman, Stephanie; Åkerfeldt, Torbjörn; Spjuth, Ola; Burman, Joachim et al.

Ingår i Cells, 2019

• DOI för Biochemical Differences in Cerebrospinal Fluid between Secondary Progressive and Relapsing-Remitting Multiple Sclerosis
• Ladda ner fulltext (pdf) av Biochemical Differences in Cerebrospinal Fluid between Secondary Progressive and Relapsing-Remitting Multiple Sclerosis

Interoperable and scalable data analysis with microservices: Applications in metabolomics

Emami Khoonsari, Payam; Moreno, Pablo; Bergmann, Sven; Burman, Joachim et al.

Ingår i Bioinformatics, s. 3752-3760, 2019

• DOI för Interoperable and scalable data analysis with microservices: Applications in metabolomics
• Ladda ner fulltext (pdf) av Interoperable and scalable data analysis with microservices: Applications in metabolomics

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

Herman, Stephanie; Niemelä, Valter; Emami Khoonsari, Payam; Sundblom, Jimmy et al.

Ingår i Scientific Reports, 2019

• DOI för Alterations in the tyrosine and phenylalanine pathways revealed by biochemical profiling in cerebrospinal fluid of Huntington's disease subjects
• Ladda ner fulltext (pdf) av Alterations in the tyrosine and phenylalanine pathways revealed by biochemical profiling in cerebrospinal fluid of Huntington's disease subjects

PhenoMeNal: Processing and analysis of metabolomics data in the cloud

Peters, Kristian; Bradbury, James; Bergmann, Sven; Capuccini, Marco et al.

Ingår i GigaScience, 2019

• DOI för PhenoMeNal: Processing and analysis of metabolomics data in the cloud

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

Lampa, Samuel; Dahlö, Martin; Alvarsson, Jonathan; Spjuth, Ola

Ingår i GigaScience, 2019

• DOI för SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines
• Ladda ner fulltext (pdf) av SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines

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

Kensert, Alexander; Harrison, Philip J; Spjuth, Ola

Ingår i SLAS discovery : advancing life sciences R & D, s. 466-475, 2019

• DOI för Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes
• Ladda ner fulltext (pdf) av Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes

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

Svensson, Fredrik; Aniceto, Natalia; Norinder, Ulf; Cortes-Ciriano, Isidro et al.

Ingår i Journal of Chemical Information and Modeling, s. 1132-1140, 2018

• DOI för Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty

Exploring the usefulness of morphological profiling of cells to study toxicity mechanisms

Georgieva, Polina; Schaal, Wesley; Spjuth, Ola

Ingår i Toxicology Letters, 2018

• DOI för Exploring the usefulness of morphological profiling of cells to study toxicity mechanisms