Work

Variable deep learning training horizons reveal the temporal complexity of biological systems

Year: 2025

Type: article

Abstract: case studies. The varying results reflect the intrinsic challenges stochastic, complex biological systems pose to data-driven modeling, and offer a new method through which we can identify biological transition points using temporal dynamics.

Source: PubMed

DOI: 10.17912/micropub.biology.001926

Authors: Po-Hao Chiu, Jacob I Evarts, Patrick Feng, Neda Bagheri

Language: English

Topic: Gene Regulatory Network Analysis

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Science Foundation, Washington Research Foundation

Awards: G653274256, Graduate Research Fellowship Program (GRFP)

OpenAlex ID: W7130332160