Currently it takes two days to find differences in genome data between cancer patients and healthy people. This results in poor decision making for hospitals that conduct cancer detection from base on DNA sequence matching. It also produces delays in new drug discovery and higher associated costs due to lack of insights in patient data Solution By using SAP HANA as the mission-critical and reliable genome data platform it becomes possible to deliver advanced medical treatment to many more patients. MKI offers cloud-based genome analytic services and anticipates that with SAP HANA what traditionally took two days to process can be performed in 20 minutes. Their Genome Analysis process has three steps:
- Case analysis: comprises of Fragment Extraction, High Speed Entry, and Genome DNA extraction. First, all cases are collected and the data is preprocessed using Hadoop (fragment extraction). After that, HANA is used to do fast data analysis to find the patterns in the genome fragments and find the relationship between genome and the case.
- Data Consumption: With the genome fragment library and the relationship to the cases in place a doctor can collect a patient’s genome and send it to the system to compare the genome fragment. Based on the knowledge library, the doctor can recommend most appropriate treatment for the patient.
- Case study: The new clinic case will be sent to the researcher to do further study which can improve the correctness of the knowledge library.
Benefits Successfully overcoming challenges of Big Data in the bioscience sphere has far-reaching ramifications on drug discovery as well as and on individual, detailed medical practices in the medical front such as:
- For hospitals: Real-time DNA sequence data analysis makes it faster and easier to identify the root cause.
- Patient care based on genome analysis results can actually happen in one doctor visit versus waiting for several days or multiple follow-up visits.
- For Pharmaceutical companies: provide required drugs in time and help identify “driver mutation” for new drug target.