“We know the genes, but not their functions.” To resolve this long-standing bottleneck in microbial research, a joint research team has proposed a cutting-edge research strategy that leverages Artificial Intelligence (AI) to drastically accelerate the discovery of microbial gene functions.

KAIST announced on Jan. 12 that a research team led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, in collaboration with Professor Bernhard Palsson from the Department of Bioengineering at UCSD, has published a comprehensive review paper. The study systematically analyzes and organizes the latest AI-based research approaches aimed at revolutionizing the speed of gene function discovery.

A team of researchers from the Shanghai Institute of Applied Physics, Chinese Academy of Sciences, has developed an innovative bipolar cusp-like pulse-shaping algorithm to address the "pile-up" challenge prevalent in high-radiation environments. Through the implementation of real-time reconstruction on FPGA hardware, this technology enables precise discrimination between neutrons and gamma rays, even under extreme count rates. Consequently, it offers a more robust tool for applicaitons in nuclear security and fundamental physics research. 

Tokyo, Japan – Researchers from Tokyo Metropolitan University have developed a suite of algorithms to automate the counting of sister chromatid exchanges (SCE) in chromosomes under the microscope. Conventional analysis requires trained personnel and time, with variability between different people. The team’s machine-learning-based algorithm boasts an accuracy of 84% and gives a more objective measurement. This could be a game changer for diagnosing disorders tied to abnormal numbers of SCEs, like Bloom syndrome.