KAIST

<(From left) Dr. Hwa-young Jeong, Professor Kyung-seo Park, Dr. Yoon-tae Jeong, Dr. Ji-hoon Seo, Professor Min-kyu Je, Professor Jung Kim > A joint research team led by Professor Jung Kim of KAIST Department of Mechanical Engineering and Professor Min-kyu Je of the Department of Electrical and Electronic Engineering recently published a review paper on the latest trends and advancements in intuitive Human-Robot Interaction (HRI) using bio-potential and bio-impedance in the internatio

A groundbreaking new method developed by researchers at KAIST and Chungnam National University could drastically streamline drug interaction testing — replacing dozens of traditional experiments with just one. The research, led by Professor Jae Kyoung Kim of KAIST Department of Mathematical Sciences & IBS Biomedical Mathematics Group and Professor Sang Kyum Kim of Chungnam National University's College of Pharmacy, introduces a novel analysis technique called 50-BOA, published in Natu

- KAIST-KRIBB Develops ‘FiNi-seq’ Technology to Capture Characteristics of Fibrotic Microenvironments Accumulated in Liver Tissue and Dynamic Changes of Early Aging Cells - Elucidation of the Spatial Ecosystem of Aged Liver Tissue, where Reprogramming of Senescent Cells and Immune Exhaustion Progresses, at the Single-Cell Genome and Epigenome Levels < (From left) Professor Jong-Eun Park of KAIST Graduate School of Medical Science and Engineering (GSMSE), Dr. Chuna Kim of K

· A team led by Professor Won Do Heo from the Department of Biological Sciences, KAIST, has developed a pioneering technology that selectively acetylates specific RNA molecules in living cells and tissues. · The platform uses RNA-targeting CRISPR tools in combination with RNA-modifying enzymes to chemically modify only the intended RNA. · The method opens new possibilities for gene therapy by enabling precise control of disease-related RNA without affecting the rest of the

Moving beyond traditional methods of observing thinly sliced and stained cancer tissues, a collaborative international research team led by KAIST has successfully developed a groundbreaking technology. This innovation uses advanced optical techniques combined with an artificial intelligence-based deep learning algorithm to create realistic, virtually stained 3D images of cancer tissue without the need for serial sectioning nor staining. This breakthrough is anticipated to pave the way for next-g