Healthcare
❯
Biomarker Discovery
❯Research
❯Explainable artificial intelligence for genomic medicine
Explainable artificial intelligence for genomic medicine
For:
Doctors of genomic medicine, researchers of genomic medicine, patientsGoal:
Improved Product Development / R&DProblem addressed
To improve the efficiency of investigatory work for experts in genomic medicine
Scope of use case
To explain the reason and basis behind AI-generated findings in genomic medicine
Description
Deep learning is one of the most representative technologies
in recent AI and shows high performance in pattern
recognition and analysis. However, as it cannot explain the
reasons for its judgment, it is called "black box AI."
There is a graph-structured data-based machine learning
technology called "Deep Tensor" that can directly analyse the
relations among numerous pieces of real-world data ranging
from intercompany transactions to material structures.
Additionally, there is also a technology for building a largescale knowledge base, which is called a "knowledge graph"
and consists of vast knowledge existing around the world
such as academic papers, by using our unique technology.
This technology identifies the factors (partial graphs) that
had a significant influence on an inference and coordinates
these with partial graphs from a knowledge graph, building
a series of pieces of information in the form of connections in
the knowledge graph as the basis for the findings.
People can combine these two technologies and develop a
system that enables AI to explain the reasons and basis
(evidence) for its judgment.
A use case of applying this explainable AI is genomic
medicine (for cancer treatment). The latest genomic
medicine helps detect patients' genetic defects that have
caused disease (cancer) and uses therapeutic drugs that
affect cancer cells produced by such genetic defects.
In genomic medicine today, a patient's normal and cancerous
cells are analysed with a next-generation sequencer; then, a
medical team uses the obtained genetic data to identify a
causal gene and determines the recommended treatment. It
takes at least two weeks for the medical team to conduct an
2
examination after completing genetic analysis. Unless the
cost and time problems are solved, spreading this
advantageous genomic medicine far and wide would be
difficult.
In this use case, the explainable AI trained Deep Tensor using
180 000 pieces of disease mutation data, successfully
embedding more than ten billion pieces of knowledge from
seventeen million medical articles and other materials into
a knowledge graph. Inputting genetic mutation data into this
system enables Deep Tensor to infer disease-causing factors
and enables the knowledge graph to find medical evidence to
justify the obtained results. Medical specialists then simply
are expected to review the flow of obtained inference logic,
thereby reducing the period between analysis and report
submission significantly from two weeks to a single day.
Knowledge graph
AI: Understand
Retrieve Information
AI: Act