Natural Sciences
Life Sciences
Scientific Computing
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Category:
Scientific Computing

Lecturer:
Prof. Dr. Hidetoshi Kotera, Dr. Kazuhiro Sakurada, RIKEN Research Institute, Saitama, Japan

Place:
BioQuant, Seminar Room 043, Im Neuenheimer Feld 267

Host:
Motomu Tanaka, CellNetworks Member

Description:
Personalized medicine is a new paradigm that represents a shift from a statistical abstraction of the patients toward the view that each patient is unique. This is a new scientific challenge as well as a new social challenge. Although linear causations and correlations have been used in the explanation of biological phenomena, biological systems form complex network whose collective behavior cannot be reduced to simple correlations. In addition, explanations usually eliminate information on differences between each individual patient. To overcome this problem, we are developing a new biomedical science based on pure description of diseases by using multi-omics data. Human beings are not genetically programmed systems—they are historical systems that organize themselves through cooperation. For this reason, we humans must define ourselves not by spatial properties like structure and function, but by temporal properties. In order to define what exactly these “time properties” are, I am working to collect and organize, in machine-readable form, “life course data” related to the physical condition of a person and then leverage the power of artificial intelligence to analyze that data. Using a hidden hierarchical Markov model to calculate state transition probability, I have discovered a method to represent individual characteristics by using the concept of degree of freedom, as well as the limits of that freedom. The state allocation is done by the reduction of dimensionality and data granularity using machine learning and energy land scale analysis. Now these new concepts are applied to the data gathered from patients with immune disorders, cancer and developmental disorders.

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