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Researchers at Stanford have developed a system that links inducible gene expression to the presence of extrachromosomal DNA (ecDNA) in cancer cells.

Researchers in Prof. David Myung's laboratory have developed a bio-compatible, crosslinking gel that can be used for in situ repair of damaged cornea or as a three-dimensional scaffold for keratocyte-keratinocyte tissue culture.

Stanford University researchers have developed a system that achieves atmospheric water harvesting with high specific productivity, defined as the rate of water collected per mass of absorbent material.

Using their newly developed acetyl-click screening platform, researchers at Stanford have identified riboflavin analogs as small molecule inhibitors of Histone Acetyltransferase 1 (HAT1) with anti-cancer activity.

Stanford researchers have developed a contrastive learning approach that can significantly reduce the amount of labeled electrocardiogram (ECG) data required for downstream healthcare tasks, such as arrhythmia identification.

Stanford inventors have developed a nanophotonic platform for detection and separation of chiral compounds that absorb in the ultraviolet regime.

Stanford researchers in the Mahajan Lab have created a customizable proteomics platform that can identify protein biomarkers to differentiate among ischemic eye diseases and identify novel therapeutic targets to treat them.

Stanford researchers have designed and prototyped an inexpensive, compact and easy-to-use smartphone lens mount for the capture of high quality photographs and videos of the eye's front and back structures.

Researchers at Stanford University and Johns Hopkins University have identified an antibody that is negatively associated with cancer in patients diagnosed with dermatomyositis.

Differential Phase Contrast (DPC) X-ray imaging measures both absorption and index of refraction of materials being imaged. This technique has several advantages compared to traditional absorption-only X-ray imaging.

Stanford researchers provide a testing strategy for autonomous vehicles that make use of rule-based domain knowledge.

Stanford inventors have developed a mechanical differential that is cable-actuated for controlling a 2 degree-of-freedom (DoF) of mobility in a robotic joint.

One of the crucial situations autonomous cars and driver-assist features need to navigate is pedestrian crossing.

Pedestrian movement prediction is a critical aspect of driver-assistance and autonomous cars. This requires predicting both human poses and human trajectories based on keypoints in an egocentric setting.