Results 51 to 60 of about 6,391,262 (321)

Symbolic regression in materials science [PDF]

MRS Communications, 2019
We showcase the potential of symbolic regression as an analytic method for use in materials research. First, we briefly describe the current state-of-the-art method, genetic programming-based symbolic regression (GPSR), and recent advances in symbolic regression techniques.
Wang, Yiqun, Wagner, Nicholas, Rondinelli, James M.   +2 more
openaire   +2 more sources

Multiview Symbolic Regression

Proceedings of the Genetic and Evolutionary Computation Conference
Published in GECCO-2024.
Etienne Russeil, Fabrício Olivetti de França, Konstantin L. Malanchev, Bogdan Burlacu, Emille E. O. Ishida, Marion Leroux, Clément Michelin, Guillaume Moinard, Emmanuel Gangler   +8 more
openaire   +4 more sources

Improving eye-tracking calibration accuracy using symbolic regression.

PLoS ONE, 2019
Eye tracking systems have recently experienced a diversity of novel calibration procedures, including smooth pursuit and vestibulo-ocular reflex based calibrations.
Almoctar Hassoumi, Vsevolod Peysakhovich, Christophe Hurter   +2 more
doaj   +1 more source

Feature and Language Selection in Temporal Symbolic Regression for Interpretable Air Quality Modelling

Algorithms, 2021
Air quality modelling that relates meteorological, car traffic, and pollution data is a fundamental problem, approached in several different ways in the recent literature.
Estrella Lucena-Sánchez, Guido Sciavicco, Ionel Eduard Stan   +2 more
doaj   +1 more source

In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery [PDF]

Annual Meeting of the Association for Computational Linguistics
State of the art Symbolic Regression (SR) methods currently build specialized models, while the application of Large Language Models (LLMs) remains largely unexplored.
Matteo Merler, Katsiaryna Haitsiukevich, Nicola Dainese, Pekka Marttinen   +3 more
semanticscholar   +1 more source

Pharmacological chromatin remodeling enhances response to estrogen therapy in ER+ breast cancer

Molecular Oncology, EarlyView.
Estrogen therapy elicits clinical benefit in ~ 30% of patients with endocrine‐resistant estrogen receptor (ER)‐positive breast cancer. Based on findings that ER transcriptional activation underlies response to estrogen therapy, we tested the effects of epigenetic dysregulation via pharmacological inhibition of histone deacetylases (HDACi).
Anneka L. Johnson Thomas, Barbara Karakyriakou, Patricia C. Muskus, Alyssa M. Roberts, Justin Rendleman, Alexa Warren, Hanxu Lu, Kimberly A. Birmingham, Malone K. Friedman, Abigail E. Goen, Huijuan Yang, Owen M. Wilkins, Fred W. Kolling, Eugene Demidenko, Julie M. Jorns, Brock C. Christensen, Todd W. Miller   +16 more
wiley   +1 more source

$\mathcal{CP}$-analyses with symbolic regression

SciPost Physics
Searching for $\mathcal{CP}$ violation in Higgs interactions at the LHC is as challenging as it is important. Although modern machine learning outperforms traditional methods, its results are difficult to control and interpret, which is especially ...
Henning Bahl, Elina Fuchs, Marco Menen, Tilman Plehn
doaj   +1 more source

Hyperosmotic stress‐induced redistribution of pre‐mRNA cleavage factor I subunits is associated with shifts in alternative polyadenylation

FEBS Open Bio, EarlyView.
Hyperosmotic stress triggers the relocation of the CFIm complex from the nucleus to the cytoplasm. This shift creates a nuclear ‘stoichiometric bottleneck’, limiting CFIm availability for mRNA processing. Consequently, specific mRNAs like NUDT21 and DICER1 undergo targeted 3′UTR shortening, demonstrating how spatial protein dynamics drive rapid ...
Hitomi Soumiya, Masatake Osawa, Hidefumi Fukumitsu   +2 more
wiley   +1 more source

Control Synthesis as Machine Learning Control by Symbolic Regression Methods

Applied Sciences, 2021
The problem of control synthesis is considered as machine learning control. The paper proposes a mathematical formulation of machine learning control, discusses approaches of supervised and unsupervised learning by symbolic regression methods.
Elizaveta Shmalko, Askhat Diveev
doaj   +1 more source

Type I interferons modulate autophagy to shape gemcitabine response in pancreatic cancer cells

FEBS Open Bio, EarlyView.
Type I interferons differentially modulate autophagy and the response of pancreatic cancer cells to gemcitabine. IFNα2b stimulates autophagic flux and protects cells from gemcitabine‐induced cell death, contributing to chemoresistance. In contrast, IFNβ1a inhibits autophagosome formation and enhances gemcitabine‐induced cell death, resulting in ...
Lucy E. Bonilla, Martín M. Ledesma, Santiago A. Behr, Matías A. Pibuel, María L. Palanek, Daniel H. Grasso, Maria N. Garcia, Juan Garona, Silvina L. Lompardía, Elida Álvarez, Daniela L. Papademetrio   +10 more
wiley   +1 more source