Prof. Yoshihiro Yamanishi
Nagoya University, Japan

Yoshihiro Yamanishi is a full professor at Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Japan. He received his Ph.D. from Kyoto University in 2005. He was post-doctoral fellow at Ecole des Mines de Paris from 2005 to 2006. He was assistant professor at Kyoto University from 2006 to 2007. He was permanent researcher at Mines ParisTech and Curie Institute from 2008 to 2012. He was associate professor (principal investigator) at the Medical Institute of Bioregulation, Kyushu University from 2012 to 2018. He was professor at Department of Bioscience and Bioinformatics, Kyushu Institute of Technology from 2018 to 2023. He is working on machine learning in bioinformatics, chemoinformatics, and drug discovery.

Speech Title: "Data-driven Drug Discovery and Healthcare by Machine Learning"

Abstract: In recent years, drug discovery has become increasingly difficult. Computational approaches are expected to promote the efficiency of drug development processes. Recent developments in biotechnology have contributed to the increase in the amounts of high-throughput data in the genome, transcriptome, proteome, interactome, phenome and diseasome. These biomedical big data can be useful resources for drug development processes. Machine learning methods are expected to play key roles in the big data analysis. In this study, we developed novel machine learning methods to predict therapeutic targets of diseases, to search for drug candidate molecules, and to design new chemical structures of drug candidate molecules, by integrating various biomedical data on compounds (e.g., chemical structures, clinical phenotypes, gene expression patterns, target molecules) and diseases (e.g., disease-causing genes, environmental factors, and clinical information). A unique feature of our data-driven approach is that it clarifies all target proteins of each drug including off-targets, estimates the mechanisms of action at the pathway level, and generates molecular structures of drug candidates by deep learning. In my talk at the conference, we will show some of the applications to therapeutic target identification, large-scale compound screening, combination therapy, and drug molecular structure design for a variety of diseases.

Prof. Kuo-Sheng Cheng
National Cheng Kung University, Taiwan

Prof. Kuo-Sheng Cheng received his BSc. and M.Sc. degrees both in electrical engineering in 1980 and 1982 from the National Cheng Kung University, Tainan, Taiwan, and the M.S. degree in biomedical engineering in 1988 from Rensselaer Polytechnic Institute, Troy, NY, USA. In 1990, he received a Ph.D. degree in electrical engineering from the National Cheng Kung University. Currently, he is a Professor at the Department of Biomedical Engineering and an Adjunct Professor at the Institute of Oral Medicine at National Cheng Kung University. He also serves as the Director of the Department of Medical Engineering at National Cheng Kung University Hospital and the Director of the Engineering and Technology Promotion Center financially supported by the National Science and Technology Council, TAIWAN. He is also the Chair of the IEEE EMBS Tainan Chapter. His research interests include medical image processing and analysis, electrical impedance tomography system development and applications, and biomedical instrumentation and measurement.

Speech Title: "The Applications of Deep Learning in Separation of Heart and Lung Impedance Images in Electrical Impedance Tomography"

Abstract: Electrical impedance tomography (EIT) is a noninvasive medical imaging technique that measures the impedance of tissues by applying low-amplitude electrical currents and measuring the resulting voltages. The impedance distribution inside the body can then be reconstructed by solving an inverse problem, which provides useful information for diagnosing and monitoring a variety of conditions, such as respiratory and circulatory functions. One of the challenges of EIT in chest applications is separating the impedance signals from the heart and lungs. This is difficult because the two organs are close together and their impedance signals are mixed. However, deep learning has emerged as a promising approach to solving this problem. Deep learning models can be trained on EIT data to learn the features that distinguish between heart and lung impedance. Once trained, these models can be used to separate the two signals and produce more accurate EIT images. In our recent study, we proposed a novel semi-Siamese U-Net architecture for heart and lung impedance image separation. This architecture is based on the state-of-the-art U-Net, with modifications and extensions to improve performance on EIT image separation.