Biomedical scientific advances have created a tsunami of information that could contain the key to developing new drug solutions. This requires sophisticated bioinformatics to aggregate and structure the data used by predictive analytics like active machine learning.
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While often equated with only genomic research, bioinformatics has become an integral part of virtually every aspect of bio-tech research, including drug discovery and development. Bioinformatics offers an unprecedented opportunity to utilize computational tools and approaches, and apply them to biological, medical, behavioral or health data.
CoRE™ employs a number of bioinformatics methods at a number of steps in its process in order to utilize large volumes of existing data from a wide variety of sources. For example, genomics data can be utilized to identify candidate compounds prior to execution of any experimentation. Once a gene mutation has been identified, information about the resulting abnormal protein target can be utilized to predict compounds that affect it.
Quantitative Medicine’s CoRE™ employs a number of bioinformatics methods which optimize both:
- Public data – aggregating and curating public data and scientific literature to expand the CoRE big data “knowledge base.” This knowledge base currently contains over 200,000,000 data points that greatly enhance the power of predictive models developed from clients’ data.
- Client Data – so it can be utilized in CoRE calculations, the system manipulates clients’ data from:
- research information in chemical libraries,
- results from experiments and assays,
- compound structures and descriptions.
By combining these data sets with The active machine learning methodology of CoRE™ takes bioinformatics to a new level.