Machine Learning for Biology

Machine learning takes large quantities of data, decodes even complex patterns, and draws conclusions that would be extremely difficult or impossible for a human to catch. Large datasets are run through training algorithms to detect initial patterns, which are then used as templates for valuing new data observations. The more data points there are, the better machine learning is at accurately predicting future outcomes.  

Immediate Insight
Multi-Analyte Processing Capability
Removal of Confounding Noise for Improved Accuracy
  ​Cardea Insight Biosensors enable the opportunity for machine learning in real time with a direct link between sensor circuitry and cloud data processing. Measurements are performed in real-time, and unlike most laboratory systems, Cardea Insight biosensors use an electrical sensing mechanism rather than an optical one. This means no translation step is needed before machine learning can begin, opening the door to instant answers.
Unlike most research and detection tools, Cardea Insight biosensors can sense different types of analytes (e.g. DNA, RNA, and proteins) using a single biological sample, enabling simultaneous measurement of tens to hundreds of different analytes. This ground-breaking multi-analyte ability results in much more comprehensive biology insights, efficient data collection, and better-trained machine learning algorithms that provide more precise predictions and applications.
Machine learning requires accurate inputs for accurate conclusions. Cardea Insight biosensors can measure proteins and DNA in their natural forms, without exhaustive and rigorous sample preparation like added labels, excessive purification or amplification that can add noise that throws off algorithms. The Cardea Insight biosensor provides data sets that are more reflective of real, current biology and removes confounding factors that distract machine learning.