Newly developed AI shows speed and accuracy in identifying the location and expression of proteins uniofsurrey CommsBio
. Here, each point on the plot represents a single cell. Only cells with single labels are chosen, coloured by their respective label.
Fig. 9: Visualisation of the deep features for DSA and CLH DNNs from the UMAP dimensionality reduction method. High-dimensional data is projected onto a 2D plane such that local structures in the original space are captured, whilst simultaneously retaining the global structure of the data. Different subcellular locations are effectively clustered by deep features in each model.Firstly, we observe that the DNNs cluster the majority of cells from the same class together, which shows their underlying discriminative power.
HCPL narrows the performance gap between AI methods and human experts and provides a toolbox of methods to tackle the challenges of single-cell protein localisation successfully. This will help accelerate the characterisation of unknown proteins and our understanding of cellular function and biology to advance our knowledge of disease-related phenotypes and drug discovery.Our work uses the HPA dataset provided by the Human Protein Atlas - Single Cell Classification Kaggle challenge.
. The mean value was calculated over the 19 segmentable classes of the challenge with a mask-to-mask IoU > 0.6 as described below:
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