A team of scientists has used GPU-accelerated deep learning to show how color can be brought to night-vision systems. In a paper published this week in the journal PLOS One, a team of researchers at the University of California, Irvine led by Professor Pierre Baldi and Dr. Andrew Browne, describes how they reconstructed color images of photos of faces using an infrared camera. The study is a step toward predicting and reconstructing what humans would see using cameras that collect light using imperceptible near-infrared illumination. The study's authors explain that humans see light in the so-called "visible spectrum," or light with wavelengths of between 400 and 700 nanometers. Typical night vision systems rely on cameras that collect infrared light outside this spectrum that we can't see.

Researchers at the Gwangju Institute of Science and Technology in Korea have developed a new deep learning model that can predict the binding between a drug and target molecule. The team, which was led by associate professor Hojung Nam and Phd student Ingoo Lee, called the new model "Highlights on Target Sequences" (HoTS).

Compared with commonly used clinical risk factors, a sophisticated type of artificial intelligence (AI) called deep learning does a better job distinguishing between the mammograms of women who will later develop breast cancer and those who will not, according to a new study in the journal Radiology. Researchers said the findings underscore AI's potential as a second reader for radiologists that can reduce unnecessary imaging and associated costs. Annual mammography is recommended for women starting at age 40 to screen for breast cancer. Research has shown that screening mammography lowers breast cancer mortality by reducing the incidence of advanced cancer. Mammograms not only help detect cancer but also provide a measure of breast cancer risk through measurements of breast density.

Scientists at the University of California, Irvine have developed a new deep-learning framework that predicts gene regulation at the single-cell level. In a study published recently in Science Advances, UCI researchers describe how their deep-learning technique can also be successfully used to observe gene regulation at the cellular level. Until now, that process had been limited to tissue-level analysis. According to co-author Xiaohui Xie, UCI professor of computer science, the framework enables the study of transcription factor binding at the cellular level, which was previously impossible due to the intrinsic noise and sparsity of single-cell data. A transcription factor (TF) is a protein that controls the translation of genetic information from DNA to RNA; TFs regulate genes to ensure they're expressed in proper sequence and at the right time in cells.