Results 41 to 50 of about 12,301 (257)

Basroparib inhibits YAP‐driven cancers by stabilizing angiomotin

Molecular Oncology, EarlyView.
Basroparib, a selective tankyrase inhibitor, suppresses Wnt signaling and attenuates YAP‐driven oncogenic programs by stabilizing angiomotin. It promotes AMOT–YAP complex formation, enforces cytoplasmic YAP sequestration, inhibits YAP/TEAD transcription, and sensitizes YAP‐active cancers, including KRAS‐mutant colorectal cancer, to MEK inhibition.
Young‐Ju Kwon, Dong Young Kim, Yuna Kim, Uk‐Il Kim, Jae‐Sung Kim   +4 more
wiley   +1 more source

Symbolic Regression Approaches for the Direct Calculation of Pipe Diameter

Axioms, 2023
This study provides novel and accurate symbolic regression-based solutions for the calculation of pipe diameter when flow rate and pressure drop (head loss) are known, together with the length of the pipe, absolute inner roughness of the pipe, and ...
Dejan Brkić, Pavel Praks, Renáta Praksová, Tomáš Kozubek   +3 more
doaj   +1 more source

Identification of serum protein biomarkers for pre‐cancerous lesions associated with pancreatic ductal adenocarcinoma

Molecular Oncology, EarlyView.
This work identified serum proteins associated with pancreatic epithelial neoplasms (PanINs) and early‐stage PDAC. Proteomics screens assessed genetically engineered mice with abundant PanINs, KPC mice (Lox‐STOP‐Lox‐KrasG12D/+ Lox‐STOP‐Lox‐Trp53R172H/+ Pdx1‐Cre) before PDAC development and also early‐stage PDAC patients (n = 31), compared to benign ...
Hannah Mearns, Jaclyn S. Long, Sergio Lilla, Kelly Hodge, Marcus J. G. W. Ladds, Colin Nixon, Paula Fernández‐Palanca, Sara Zanivan, Pilar Acedo, Stephen P. Pereira, Kevin M. Ryan   +10 more
wiley   +1 more source

Selecting Informative Data Samples for Model Learning Through Symbolic Regression

IEEE Access, 2021
Continual model learning for nonlinear dynamic systems, such as autonomous robots, presents several challenges. First, it tends to be computationally expensive as the amount of data collected by the robot quickly grows in time. Second, the model accuracy
Erik Derner, Jiri Kubalik, Robert Babuska   +2 more
doaj   +1 more source

Taylor genetic programming for symbolic regression

Proceedings of the Genetic and Evolutionary Computation Conference, 2022
Genetic programming (GP) is a commonly used approach to solve symbolic regression (SR) problems. Compared with the machine learning or deep learning methods that depend on the pre-defined model and the training dataset for solving SR problems, GP is more focused on finding the solution in a search space.
He, Baihe, Lu, Qiang, Yang, Qingyun, Luo, Jake, Wang, Zhiguang   +4 more
openaire   +2 more sources

Use of Symptomatic Drug Treatment for Fatigue in Multiple Sclerosis and Patterns of Work Loss

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To describe the use of central stimulants and amantadine for fatigue in MS and evaluate a potential association with reduced work loss in people with MS. Methods We conducted a nationwide, matched, register‐based cohort study in Sweden (2006 to 2023) using national registers with prospective data collection.
Simon Englund, Johan Reutfors, Thomas Frisell, Fredrik Piehl   +3 more
wiley   +1 more source

Discovery and Targeted Proteomic Studies Reveal Striatal Markers Validated for Huntington's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Clinical trials for Huntington's disease (HD) enrolling persons before clinical motor diagnosis (CMD) lack validated biomarkers. This study aimed to conduct an unbiased discovery analysis and a targeted examination of proteomic biomarkers scrutinized by clinical validation. Methods Cerebrospinal fluid was obtained from PREDICT‐HD and
Daniel Chelsky, Cara Joyce, H. Jeremy Bockholt, Paul A. Rudnick, William H. Adams, Fiona McAllister, Justin W. Smock, Michael A. Newton, Jane S. Paulsen   +8 more
wiley   +1 more source

SymFormer: End-to-End Symbolic Regression Using Transformer-Based Architecture

IEEE Access
Many real-world systems can be naturally described by mathematical formulas. The task of automatically constructing formulas to fit observed data is called symbolic regression.
Martin Vastl, Jonas Kulhanek, Jiri Kubalik, Erik Derner, Robert Babuska   +4 more
doaj   +1 more source

Clustering Algorithm Reveals Dopamine‐Motor Mismatch in Cognitively Preserved Parkinson's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore the relationship between dopaminergic denervation and motor impairment in two de novo Parkinson's disease (PD) cohorts. Methods n = 249 PD patients from Parkinson's Progression Markers Initiative (PPMI) and n = 84 from an external clinical cohort.
Rachele Malito, Chiara Meneghini, Alice Galli, Luca Gallo, Pierfrancesco Mitrotti, Paola Dimartino, Marianna Inglese, Cinzia Zatti, Alessandro Lupini, Andrea Pilotto, Alessandro Padovani, Micol Avenali, Enza Maria Valente, Cristina Tassorelli, Silvia P. Caminiti   +14 more
wiley   +1 more source

Functional and Structural Evidence of Neurofluid Circuit Aberrations in Huntington Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Disrupted neurofluid regulation may contribute to neurodegeneration in Huntington disease (HD). Because neurofluid pathways influence waste clearance, inflammation, and the distribution of central nervous system (CNS)–delivered therapeutics, understanding their dysfunction is increasingly important as targeted treatments emerge.
Kilian Hett, Abigail Dubois, Melanie Leguizamon, Alexander Song, Paula Trujillo, Colin D. McKnight, Ciaran M. Considine, Manus J. Donahue, Daniel O. Claassen   +8 more
wiley   +1 more source