BioSymetrics Launches Augusta Pre-Processing and Analytics Platform
NEW YORK, Dec. 19, 2017 — BioSymetrics, Inc., a technology company that aims to transform data analytics for the biomedical industry, today announced the launch of its pre-processing and analytics platform, Augusta. Augusta is a proprietary technology that allows standardized processing and integration of multiple, diverse raw data types, facilitating rapid deployment of AI projects in precision medicine, drug discovery, and health data applications. Currently, Augusta provides over 150 modules for the processing of raw genomic, metabolomic, MRI/fMRI, chemical, ECG/EEG, and EMR data, and subsequent analysis, all controlled through an automated optimization framework.
BioSymetrics’ Chief Scientific Officer, Gabriel Musso, said about Augusta: “When you work in Data Science, specifically in the health space, the major hurdles in analyzing data are not technological, they’re practical. We’ve seen this in our own work when developing diagnostic models for Autism and Alzheimer’s Disease, and were astonished at how much of our time was spent processing MRIs and other medical data before analytic projects could begin. We’ve sought to address this need by designing an easily deployable, automated pre-processing framework that can take multiple data types from source, process them, integrate them, and apply machine learning, all in a data-driven way.”
“AI may change the medical world in the next ten years, however, there are challenges around truly harnessing the data needed to make this promise a reality. Augusta uniquely brings together massive data, data mining, and real-time processing capabilities, enabling data of any type, size, and dimensionality to be explored and modeled with unprecedented speed and accuracy. These features lend themselves very well to challenges in the biomedical industry looking to predict outcomes and gain actionable insights,” said Wendy Tsai, VP, Business Development at BioSymetrics. “We are thrilled to bring this product to market and pleased with the successes we have achieved to date.”
The Market Opportunity
IDC reports that the Big Data and Analytics market will grow from $130B last year to more than $203B in 2020. Frost & Sullivan projects that the Machine Learning in Medicine market will reach $6B by 2021. Yet streaming data, real-time analytics, and machine learning will remain a significant challenge for the rapidly changing and data-rich biomedical space due to data variety/heterogeneity, lack of standards, and difficulty in scaling.
The BioSymetrics Offering
BioSymetrics addresses challenges in biomedicine by developing massive data analytics and optimized end-to-end machine learning technology with a focus on preprocessing and standardization capabilities across multiple and combined data types in medicine. Specific benefits of the BioSymetrics offering include:
- Integrated analytics and machine learning solutions that can integrate large repositories of images, genomics data, streaming data, and compounds
- Modular and customizable pipelines for processing raw phenotypic, imaging, drug, and genomic data types using any combination of datasets
- Automated model optimization based on a proprietary parameter iteration method
- Scalable solution architecture for enterprise and cloud computing applications that can be deployed anywhere (cloud services such as Microsoft Azure, AWS; and private or local servers)
- Fully dockerized distributed infrastructure that eliminates the need for transfer of sensitive data
BioSymetrics takes a specialized approach to pre-processing of data, feature extraction, and feature selection. Methods are outlined in a recent white paper on benchmarking of technologies used for analysis of combined biomedical datasets.
About BioSymetrics, Inc.
Founded in 2015, Biosymetrics Inc. is based in NY, Boston, and Toronto. BioSymetrics serves biopharmaceutical, genomics, diagnostic, healthcare, and technology companies. BioSymetrics’ capabilities are underpinned by expertise in AI, Data Science, predictive analytics, big data, medicine, bioinformatics, computational biology, advanced image processing, healthcare, and health IT.