MACHINE LEARNING

Molecular signatures have the power to assess one’s current biological state, unlike genomics which can only provide a static snapshot of what could be - butterfly or caterpillar. We apply machine learning solutions to large biological datasets to discover relevant markers for the current biological state.

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Product Summary

PerXeptive was built to accommodate demands of biomarker discovery in research and clinical applications. Able to utilize complex machine learning techniques while remaining flexible enough to deploy with data of almost any shape and size.


Motivation

Data pipelines used for machine learning and biomarker discovery are complex and often required being built for a specific goal. This can be particularly challenging for researchers that don't have in-house expertise and need insights for their next phase. To this end, SoCal Bioinformatics has developed a robust pipeline, built from a decade of experience in the field, that has the flexibility to be tailored to specific needs while still accessing innovative, proven technologies.


Process

Machine learning requires a balance between applied algorithms and human oversight.


Assessment

All projects have the data evaluated for size, complexity and the specific research goals. At this stage estimates for scope and cost are discussed.


Exploration

The data first gets explored with both statistical methods and predictive models to look for patterns and gain insights. This stage helps to focus in on the final path to quality biomarkers. Read more about how we avoid the common pitfalls of predictive modeling.


Modeling

Given the insights gained in the Exploration phase, specific modeling techniques are applied to the data. It is at this phase we hope to produce a candidate set of top performing biomarker panels, predictive models and performance estimates.


Consulting

Management of the entire process by an expert in the field.

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