Keynote Speaker

Yoshihiro Yamanishi mainly focuses on Bioinformatics, Drug discovery, Inference, Drug side effects and Drug. His Bioinformatics study typically links adjacent topics like Computational biology. His Computational biology study combines topics in areas such as Biological data, Genome, Drug target, KEGG and In silico. He interconnects Diagnostic marker, Disease gene, Kernel canonical correlation analysis and Kegg pathway in the investigation of issues within Genome. His work in Inference covers topics such as Phylogenetic tree which are related to areas like Data mining, Chromosome and Inference engine. In his study, Drug withdrawal, Intensive care medicine and Drug reaction is inextricably linked to Drug development, which falls within the broad field of Drug side effects. Yoshihiro Yamanishi mostly deals with Computational biology, Artificial intelligence, Data mining, Bioinformatics and Drug discovery. His study in Computational biology is interdisciplinary in nature, drawing from both KEGG, Gene, Interactome and Drug. His Artificial intelligence research incorporates elements of Machine learning, Protein–protein interaction and Pattern recognition. His Data mining research is multidisciplinary, incorporating elements of Quantitative structure – activity relationship, Interpretability, Support vector machine and Metabolic pathway. Yoshihiro Yamanishi combines subjects such as Interaction network, Diagnostic marker, G protein-coupled receptor and Genomics with his study of Bioinformatics. The Drug discovery study combines topics in areas such as Plasma protein binding, Data-driven, Transcriptome, In silico and Binding site.

Title: Data-driven drug discovery and healthcare by machine learning

Abstract: 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.