How it works

We make it easy for you

Our tools

Derive useful insights with our cutting-edge techniques

Single cell data analysis

We use our own developed deep learning techniques for high performance single cell data visualization, clustering and cell annotation. We have included classical methods such PCa, t-SNE, PHATE and UMAP for performance comparison.

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Neural network feature analysis

We have developed techniques for analyzing the high dimensional feature space data from neural networks in classification, regression, segmentation, superresolution and prediction tasks. These techniques improve the interpretability and performance of the black box neural networks.

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Tabular data analysis

We have developed deep learning techniques for accurate analysis of tabular data in both classification and regression tasks. Our methods convert the tabular data into images and apply 2D CNN for high performance and interpretable analysis.

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