Back to search

Article

Emergent populations derived with unsupervised learning of human whole genomes

2018-06-18

Abstract excerpt

Artificial intelligence (AI) holds great promise to precisely classify human ancestry and the genetic causes of complex diseases. I have constructed an unsupervised machine learning paradigm that examines the whole genome as a hyper-dense, nonlinear, multidimensional feature space. The AI system culminates in 26 neural network neurons each sensitive to a specific heritage that can identify an individual’s componen...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
5b992078-8206-5c00-92ae-bbe765bbbc2c
DOI
10.1101/329789
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Emergent populations derived with unsupervised learning of human whole genomesDOI 10.1101/329789
Select a neighboring publication to make it the new centre.