Results 111 to 120 of about 2,307,608 (312)

Machine learning phases of active matter

, 2022
5 pages, 4 ...
Xue, Tingting, Li, Xu, Chen, Xiaosong, Chen, Li, Han, Zhangang   +4 more
openaire   +2 more sources

Longitudinal Assessment of Biomarkers in ALS: Discriminative Biomarkers for Disease Progression and Survival

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To assess the association and discriminative performance of serum biomarkers with clinical disease progression and survival in patients with amyotrophic lateral sclerosis (ALS). Methods This retrospective study, conducted at Houston Methodist Hospital, Houston, TX, used longitudinal serum samples collected between January 2018 and ...
David R. Beers, Yueh‐Yun Lin, Jason R. Thonhoff, Aaron D. Thome, Alireza Faridar, Weihua Zhao, Shixiang Wen, Stanley H. Appel   +7 more
wiley   +1 more source

Active Learning for Machine Learning Driven Molecular Dynamics

9 pages, 4 figures, for Neurips Workshop: Machine Learning and the Physical Sciences ...
Bachelor, Kevin, Murdeshwar, Sanya, Sabo, Daniel, Marinescu, Razvan   +3 more
openaire   +2 more sources

Comparing the Effect of Semi‐Immersive Virtual Reality, Computerized Cognitive Training, and Traditional Rehabilitation on Cognitive Function in Multiple Sclerosis: A Randomized Clinical Trial

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Cognitive impairment is a common non‐motor symptom in Multiple Sclerosis (MS), negatively affecting autonomy and Quality of Life (QoL). Innovative rehabilitation strategies, such as semi‐immersive virtual reality (VR) and computerized cognitive training (CCT), may offer advantages over traditional cognitive rehabilitation (TCR ...
Maria Grazia Maggio, Desiree Latella, Lilla Bonanno, Paulo Cezar Rocha dos Santos, Marcos Maldonado‐Díaz, Meir Plotnik, Rosaria De Luca, Angelo Quartarone, Rocco Salvatore Calabrò   +8 more
wiley   +1 more source

Combining Three Peripheral Blood Biomarkers to Stratify Rheumatoid Arthritis–Associated Interstitial Lung Disease Risk

Arthritis Care &Research, EarlyView.
Objective The purpose was to evaluate a biomarker score consisting of MUC5B rs35705950 promoter variant, plasma matrix metalloproteinase‐7 (MMP‐7), and serum anti–malondialdehyde‐acetaldehyde (anti‐MAA) antibody for rheumatoid arthritis (RA)–associated interstitial lung disease (ILD) risk stratification.
Kelsey Coziahr, Austin M. Wheeler, Brent A. Luedders, Michael Duryee, Halie Frideres, Katherine D. Wysham, Grant W. Cannon, Gary Kunkel, Dana P. Ascherman, Paul A. Monach, Gail S. Kerr, Andreas M. Reimold, Scott M. Matson, Joshua F. Baker, Geoffrey M. Thiele, Ted R. Mikuls, Bryant R. England   +16 more
wiley   +1 more source

Modeling PROTAC degradation activity with machine learning

Artificial Intelligence in the Life Sciences
13 pages, 10 ...
Stefano Ribes, Eva Nittinger, Christian Tyrchan, Rocío Mercado   +3 more
openaire   +3 more sources

Artificial Intelligence in Systemic Sclerosis: Clinical applications, challenges, and future directions

Arthritis Care &Research, Accepted Article.
Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos, José Miguel Sequí‐Sabater, Diego Benavent   +2 more
wiley   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Active Learning for Stacking and AdaBoost-Related Models

Stats
Ensemble learning (EL) has become an essential technique in machine learning that can significantly enhance the predictive performance of basic models, but it also comes with an increased cost of computation.
Qun Sui, Sujit K. Ghosh
doaj   +1 more source