May 2023
Our work on the development of the advanced AI system, HCPL (Hybrid subCellular Protein Localiser) for single-cell image analysis for protein localisation, is published in Communications Biology of the Nature Portfolio.
This technique can rapidly identify the subcellular localisation of proteins of interest in images, streamlining high throughput screening for various applications including the study of tumour heterogeneity, identification of disease and toxicity biomarkers, and drug discovery.
Husain, S.S., Ong, EJ., Minskiy, D. et al. Single-cell subcellular protein localisation using novel ensembles of diverse deep architectures. Commun Biol 6, 489 (2023). https://www.nature.com/articles/s42003-023-04840-z
May 2021
ForecomAI scientists won silver medal in a global competition to design single-cell AI analysis method that can automatically determine subcellular distribution of proteins from microscopy images. The competition was based on the Human Protein Atlas (https://www.proteinatlas.org/) and over 700 teams competed worldwide on the ML platform Kaggle.
Automated cell analysis methods are essential to support the ongoing revolution in biology, which promises to advance understanding of how human cells function, how diseases develop and how to cure them.
October 2020
ForecomAI has embarked in a collaboration with a pharmaceutical alliance to undertake machine learning methodology to develop and evaluate a novel risk prediction algorithm for the cardiovascular risk of pharmacological treatments using CPRD real world data.