3billion has recently announced 3Cnet, a pathogenicity prediction tool. Its performance surpasses existing tools.

What is the significance of that, and what could it mean for the diagnosis of rare genetic diseases, moving forward?

Let’s take a quick look and break it down into more simple terms.


Quick Overview

  • 3Cnet is a pathogenicity prediction tool, based on recurrent neural networks (RNN), which can detect disease-causing variants in humans
  • It has been in development for roughly a year and a half
  • Achieved a top-1 recall of 14.5%, surpassing REVEL’s leading top-1 recall of 6.5%
  • This means that 3Cnet can predict disease-causing variants of genetic disease patients with a sensitivity ~2.2 times greater than the current-leading tool

The Name

  • ‘3C’ is derived from the "three C’s" approach utilized; each C relates to how the model was trained
  • Conservation - derived from utilization of UniRef30 for simulated mutation data
  • Clinical - derived from utilization of ClinVar database
  • Common - derived from utilization of gnomAD database

What is ‘pathogenicity prediction’?

Much like its name suggests, pathogenicity prediction is the prediction of pathogens, or organisms which can cause a disease. This is where we get the term disease-causing variants from.

By applying various techniques, we are able to make accurate predictions, and streamline the diagnostic process. 3Cnet is able to look at a large number of variants, compare them against other variants, and determine which ones are malicious (disease-causing).

How does that benefit patients?

  • 3Cnet has potential to increase diagnostic rates, by streamlining the process of diagnosis using AI to a high degree
  • The faster and more efficient the diagnostic process is, along with high accuracy, the faster a patient can be diagnosed
  • The more data it looks at, the more accurate its predictions become, topping out at near-100% accuracy
  • 3Cnet looks at a long sequence of data, and by applying AI techniques, picks out specific variants which it predicts are disease-causing

What is the future of 3Cnet?

  • Currently, 3Cnet can look at a window, or sequence, of 201 amino acids around the variant site; this will be increased so that a larger sequence is able to be handled
  • By increasing sequence size, 3Cnet widens its scope, providing more coverage for the patient
  • Improvements to speed and performance are underway, to produce results faster
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  • With WES, we target 1% of the human genome, the protein-coding region, which contains 85% of disease-causing mutations
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