Philip Harrison

Combining molecular and cell painting image data for mechanism of action prediction

Tian, Guangyan; Harrison, Philip J.; Sreenivasan, Akshai P.; Carreras-Puigvert, Jordi et al.

Ingår i Artificial Intelligence in the Life Sciences, s. 100060-100060, 2023

• DOI för Combining molecular and cell painting image data for mechanism of action prediction

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

Combining molecular and cell painting image data for mechanism of action prediction

Tian, Guangyan; Harrison, Philip J.; Sreenivasan, Akshai P.; Carreras-Puigvert, Jordi et al.

Ingår i ARTIFICIAL INTELLIGENCE IN THE LIFE SCIENCES, 2023

• DOI för Combining molecular and cell painting image data for mechanism of action prediction
• Ladda ner fulltext (pdf) av Combining molecular and cell painting image data for mechanism of action prediction

Deep learning approaches for image cytometry: assessing cellular morphological responses to drug perturbations

Harrison, Philip John

2023

• Ladda ner fulltext (pdf) av Deep learning approaches for image cytometry: assessing cellular morphological responses to drug perturbations

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

Combining molecular and cell painting image data for mechanism of action prediction

Tian, Guangyan; Harrison, Philip J.; Sreenivasan, Akshai P.; Carreras-Puigvert, Jordi et al.

Ingår i Artificial Intelligence in the Life Sciences, s. 100060-100060, 2023

• DOI för Combining molecular and cell painting image data for mechanism of action prediction

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

Combining molecular and cell painting image data for mechanism of action prediction

Tian, Guangyan; Harrison, Philip J.; Sreenivasan, Akshai P.; Carreras-Puigvert, Jordi et al.

Ingår i ARTIFICIAL INTELLIGENCE IN THE LIFE SCIENCES, 2023

• DOI för Combining molecular and cell painting image data for mechanism of action prediction
• Ladda ner fulltext (pdf) av Combining molecular and cell painting image data for mechanism of action 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

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

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

Learning to see colours: Biologically relevant virtual staining for adipocyte cell images

Wieslander, Håkan; Gupta, Ankit; Bergman, Ebba; Hallström, Erik et al.

Ingår i PLOS ONE, 2021

• DOI för Learning to see colours: Biologically relevant virtual staining for adipocyte cell images
• Ladda ner fulltext (pdf) av Learning to see colours: Biologically relevant virtual staining for adipocyte cell images

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

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

Occupancy versus colonization-extinction models for projecting population trends at different spatial scales

Norden, Jenni; Harrison, Philip J; Mair, Louise; Siitonen, Juha et al.

Ingår i Ecology and Evolution, s. 3079-3089, 2020

• DOI för Occupancy versus colonization-extinction models for projecting population trends at different spatial scales
• Ladda ner fulltext (pdf) av Occupancy versus colonization-extinction models for projecting population trends at different spatial scales

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

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

Ingår i SLAS Discovery, 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

Deep Learning in Image Cytometry: A Review

Gupta, Anindya; Harrison, Philip J.; Wieslander, Håkan; Pielawski, Nicolas et al.

Ingår i Cytometry Part A, s. 366-380, 2019

• DOI för Deep Learning in Image Cytometry: A Review
• Ladda ner fulltext (pdf) av Deep Learning in Image Cytometry: A Review

Deep learning approaches for image cytometry: assessing cellular morphological responses to drug perturbations

Harrison, Philip John

2023

• Ladda ner fulltext (pdf) av Deep learning approaches for image cytometry: assessing cellular morphological responses to drug perturbations

Is brightfield all you need for MoA prediction?

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

2022

Exploring the evolution of cellular morphological changes after drug administration based on brightfield image data

Harrison, Philip J.; Rietdijk, Jonne; Holmberg, David; Carreras-Puigvert, Jordi et al.