Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks
Niko Bernaola, Mario Michiels, Pedro Larrañaga, Concha Bielza, Sushmita Roy, Mingyao Li, Sushmita Roy, Mingyao Li, Sushmita Roy, Mingyao Li · 2023
Read Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks by Niko Bernaola, Mario Michiels, Pedro Larrañaga, Concha Bielza, Sushmita Roy, Mingyao Li, Sushmita Roy, Mingyao Li, Sushmita Roy, Mingyao Li (2023) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-10745139.txt.
Description
Niko Bernaola and co-authors present a 2023 method for learning huge gene control networks in the human brain. Such networks map which genes switch others on or off. The new method learns small local networks with a fast search, then merges them into one big graph.
On a standard score that weighs both hits and false calls, the method matches top rivals while it runs faster. It scales to tens of thousands of variables and can take in prior expert knowledge on network shape. As a show case, the team built the network for the full human genome from brain samples in a public atlas. An open viewer tool lets experts explore the vast graph and pick nodes for lab tests.
Catalog record
| CREATOR | Niko Bernaola, Mario Michiels, Pedro Larrañaga, Concha Bielza, Sushmita Roy, Mingyao Li, Sushmita Roy, Mingyao Li, Sushmita Roy, Mingyao Li |
|---|---|
| YEAR | 2023 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 79 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |