The objective of this ESR project is to develop Big Sensing intelligent warehouse for translation and clinical diagnosis study. This new data warehouse are designed with to handle the variety across subjects and provides better support for the processing, enable each subject to have measurement of arbitrary types of sensors with variable datatypes and support more flexible sensing analysis pipeline. Technically, the new database will be based on the No SQL database technology to handle variable data with an interface for high performance computing enable to process all the data in real-time. Machine Learning (ML) and Deep Learning (DL) based system to autonomous charting the genomic-sensing pathway, i.e. advance machine learning approaches will be diploid to extract the relationship between gene, omics and sensing, and therefore automatically build the pathway from gene and discovery the hiding medicine knowledge or hypothesis for future neurology research. By applying innovative ML & DL algorithms, an optimal Bayesian mixture prediction model and intelligent information management system will be developed.