Philip Harrison
Visiting researcher at Department of Pharmaceutical Biosciences; Research; Pharmaceutical Bioinformatics
- Visiting address:
- Biomedicinskt centrum BMC, Husargatan 3
- Postal address:
- Box 591
751 24 UPPSALA
- ORCID:
- 0000-0003-4046-9017
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Short presentation
I obtained my first PhD in marine biology in 2006 studying the population dynamics of grey seals. Between 2006-2016 I undertook several research projects modelling wildlife populations and analysing trends in biodiversity. With a desire to broaden my scientific horizons in 2017 I began studies for a second PhD in pharmaceutical science. For this PhD I shall develop machine learning methods for online, large-scale analysis of microscopy image data as part of the HASTE project.
Publications
Recent publications
- Deep learning approaches for image cytometry: assessing cellular morphological responses to drug perturbations (2023)
- Evaluating the utility of brightfield image data for mechanism of action prediction (2023)
- Combining molecular and cell painting image data for mechanism of action prediction (2023)
- Is brightfield all you need for MoA prediction? (2022)
- A method for Boolean analysis of protein interactions at a molecular level (2022)
All publications
Articles
- Evaluating the utility of brightfield image data for mechanism of action prediction (2023)
- Combining molecular and cell painting image data for mechanism of action prediction (2023)
- A method for Boolean analysis of protein interactions at a molecular level (2022)
- Predicting protein network topology clusters from chemical structure using deep learning (2022)
- Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit (2021)
- Deep-learning models for lipid nanoparticle-based drug delivery (2021)
- Learning to see colours: Biologically relevant virtual staining for adipocyte cell images (2021)
- Deep learning and conformal prediction for hierarchical analysis of large-scale whole-slide tissue images (2021)
- Occupancy versus colonization-extinction models for projecting population trends at different spatial scales (2020)
- Deep Learning in Image Cytometry (2019)
- Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes (2019)
- Exploring the evolution of cellular morphological changes after drug administration based on brightfield image data
Books
Conferences
