We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| howchihlee/BNGPU | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| W_reg_init | Ran | howchihlee/BNGPU/BNGPU/NOBEARS.py pointer only (licence: NONE) · get_code("aecdc257e560be74") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Constructing gene regulatory networks is a critical step in revealing disease mechanisms from transcriptomic data. In this work, we present NO-BEARS, a novel algorithm for estimating gene regulatory networks. The NO-BEARS algorithm is built on the basis of the NOTEARS algorithm with two improvements. First, we propose a new constraint and its fast approximation to reduce the computational cost of the NO-TEARS algorithm. Next, we introduce a polynomial regression loss to handle non-linearity in gene expressions. Our implementation utilizes modern GPU computation that can decrease the time of hours-long CPU computation to seconds. Using synthetic data, we demonstrate improved performance, both in processing time and accuracy, on inferring gene regulatory networks from gene expression data.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1911.00081")
get_code_for_paper("1911.00081")
have("1911.00081")
Connect an agent — have() is free.