Spatially resolved transcriptomics (SRTs) provide gene expression while retaining the locations of sequencing and providing matched pathology images; however, there are high levels of noise. We made Sprod to impute accurate SRT gene expression by leveraging their matched location and imaging data.

How can we quantify the nanoscale distribution of proteins on the cell surface? How can we tell if they are randomly distributed or not? The answer lies in a new data analysis method for single-molecule super-resolution microscopy.

Photon-upconversion nanoparticles that convert two or more low-energy photons to one photon of UV, visible or NIR light enable the detection and imaging of cancer markers without optical background interference.

We have developed a deep learning method for large-scale virtual screening in a fraction of the time required by conventional docking. Deep Docking can be used in conjunction with any docking program and enables the screening of billions of compounds in a fast and efficient way.