Big Data can Now Help Identify Metastatic Breast Cancer!
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Big Data can Now Help Identify Metastatic Breast Cancer!

By Pharma Tech Outlook | Monday, June 17, 2019

Big Data Researchers can now distinguish between a benign tumor and a malignant tumor cell line with the assistance of differentiated data sets provided by big data platforms.

FREMONT, CA: Over 2 million new cases of breast cancer reported in 2018 alone have raised a massive disturbance in women healthcare. After experiencing the highest rates of breast cancer in major 25 countries, the researchers and oncologists collectively have concluded to undertake advanced technology for providing immediate solutions. Studies conducted by Nature Communications states that doctors, medical professionals, and management members are investing in big data to improve the research work in metastatic breast cancer. With limited availability of therapeutic options in the healthcare sector, metastasis cancer leads to the death of 90 percent of affected patients.  

Check out: Top Oncology Companies

Advancements in the field of human genomics have disclosed a large-scale of genetic data for evaluation, but the broad range of data sets has created an amalgamation of complex information. Hence, the integration of big data have made segregation and evaluation stages much more accessible. Researchers can now distinguish between a benign tumor and a malignant tumor cell line with the assistance of differentiated data sets provided by big data platforms.

Through proper analysis, the clinical investigators have identified a substantial genomic difference in MDA-MB-231 cell line of metastatic breast cancer cell and metastatic tumor cells. The poorly recaptured somatic mutational patterns of metastatic breast cancer cells make the critical determining factor for researchers. Additionally, the defective cell lines also carry many specific genomic alterations.

Bin Chen, Ph.D., assistant professor in The College of Human Medicine was able to identify a close resemblance of tumor cells with other cell lines that elevates the predictability of breast cancer. Due to the close resemblance of cell lines, the detection of micro anomalies becomes a stress-less task for scientists.  

Previously, different computational methods were being undertaken to accelerate the pace of the research. However, with the inclusion of big data, the process of analyzing gene expression to orchestrate drug response and drug discovery is becoming uncomplicated. With the addition of big data, the process of facilitating and modeling the metastatic breast cancer translational research becomes more convenient. 

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