Once in a rare while, unexpected events leave a major impact on society such as the current COVID-19 pandemic, necessitating additional urgency to create, innovate and be more efficient. That is true for established enterprises and emerging ones alike.
ReSurfX is an ‘outcomes intelligence’ company focused on improving innovation, outcomes, and ROI of enterprises from data-intensive activities, with primary focus on healthcare (Pharma, Biotech and patient care). ReSurfX provides value through significant improvements in accuracy, robustness, novel insights, and advance prediction of outcome directions by leveraging a data-source agnostic novel machine learning (ML) approach we invented – Adaptive Hypersurface Technology (AHT). ReSurfX::vysen is an enterprise software product that delivers these functionalities.
Here we demonstrate the predictive power of AHT based functionalities through an extremely powerful SyRTOP configuration of ReSurfX::vysen product and outline several applications. The rare form of validations provided here are in the form of FDA actions based on Real World Evidence and Post Market Surveillance that are difficult to achieve even with expensive targeted studies.
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Recently we added a new ‘System Response Based Triggers and Outcomes Predictor’ (SyRTOP) to our enterprise grade Decision Analytics software product ReSurfX::vysen. Here we share an exemplary result that proves the enhanced accuracy, lack of contaminating incorrect results, and many other advantages of this new solution. SyRTOP was tested on a system-wide gene expression response in liver tissue after treatment with a panel of drugs compared against a larger drug response database (Knowledge Repository) and proved to be a best in class solution as with other solutions incorporated in ReSurfX::vysen. The result is shown below.
click on the image to see at high resolution
Update: February 13, 2021: A newer article on ReSurfX::vysen in SyRTOP configuration with further improved results, powerful validations and description of components
is published on February 11, 2021. This article has some information complementary to that.
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Large volumes of data (Big data) usually display a problem termed ‘curse of dimensionality (CoD)’. Often, many statistical practitioners will scoff at and be very circumspect of solutions that claim to do well in data analytics and decisions/outcomes that seem to overcome the curse of dimensionality (CoD). It is particularly refreshing to see that in a recent blog for MassBio that the author (Loralyn Mears) considers that CoD can be overcome – even though in the context of wondering how the recent efforts to consolidate data in Pharma as ‘data lake(s)’ and ‘stream computing’ are going to overcome CoD.
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