Results 171 to 180 of about 105,151 (260)

Evaluation of Matrix Effects in SIMS Using Gaussian Process Regression: The Case of Olivine Mg Isotope Microanalysis. [PDF]

Rapid Commun Mass Spectrom
Itano K, Fukuda K, Kita NT, Ueki K, Kuwatani T, Akaho S.   +5 more
europepmc   +1 more source

A novel correlation Gaussian process regression-based extreme learning machine. [PDF]

Knowl Inf Syst, 2023
Ye X, He Y, Zhang M, Fournier-Viger P, Huang JZ.   +4 more
europepmc   +1 more source

TSTScope Unifies Single‐Cell Multi‐Omics to Identify Functional T Cell States Predictive of Immunotherapy Response

Advanced Science, EarlyView.
TSTScope is an interpretable AI framework that integrates single‐cell transcriptomes with TCR information through curated gene‐program constraints. By linking receptor context to functional T cell states, it reveals response‐associated tumor‐specific T cell programs in lung cancer immunotherapy cohorts and defines an MPR score associated with ...
Shiwei Cao, Jinyu Cheng, Feng‐ao Wang, Chenxin Yi, Jiajun Chen, Keyue Wang, Lulu Liu, Junwei Liu, Yixue Li   +8 more
wiley   +1 more source

Enhancing Cutting Oil Efficiency with Nanoparticle Additives: A Gaussian Process Regression Approach to Viscosity and Cost Optimization. [PDF]

Nanomaterials (Basel)
Erdoğan B, Kılıç İ, Güneş A, Yaman O, Çakır Şencan A.   +4 more
europepmc   +1 more source

Gaussian Process Regression Models for Predicting Atomic Energies and Multipole Moments. [PDF]

J Chem Theory Comput, 2023
Burn MJ, Popelier PLA.
europepmc   +1 more source

RNA Promotes Synapsin Condensates That Organize Synaptic Vesicles in Live Neurons and Enable Localized Translation in Reconstituted System

Advanced Science, EarlyView.
Condensates at synapses organize synaptic vesicles (SVs) and are crucial for efficient neurotransmitter release, yet how RNA contributes to this mesoscale architecture remains poorly understood. Here we uncover that RNA modulates synapsin–driven condensates.
Branislava Rankovic, Zachary M. Geisterfer, Akshita Chhabra, Vladimir M. Jovanovic, Ian Seim, Christian Hoffmann, Antonela Condric, Gerard Aguilar Pérez, Rinse de Boer, Lars T. Hofstede, Jingao Sun, Kiara Freitag, Katja Nowick, Marina Jendrach, Frank L. Heppner, Vladimir Despic, Michaela Müller‐McNicoll, Stephan Sigrist, Geert van den Bogaart, Sofiia Reshetniak, Amy S. Gladfelter, Dragomir Milovanovic   +21 more
wiley   +1 more source

Quantifying and exploring state-dependent ecological interactions from time series data using Gaussian process regression. [PDF]

J R Soc Interface
Yukihira T, Osada Y, Kondoh M.
europepmc   +1 more source

Fusing Direct and Indirect Measurements Through Multi‐Fidelity Learning For Accelerated Electrocaloric Materials Discovery

Advanced Science, EarlyView.
A multi‐fidelity framework integrates sparse direct and abundant indirect electrocaloric measurements. Multi‐objective active learning accelerates BaTiO3‐based electrocaloric materials discovery at –70∘C$^{\circ }{\rm C}$. A diffuse transition enables an electrocaloric strength of 0.06×$\times$10−6 Km/V at –70℃ with an operational temperature span of ...
Bo Wang, Pengfei Dang, Yuan Tian, Zhengwang He, Wei Gu, Lixue Zhang, Turab Lookman, Yumei Zhou, Dezhen Xue   +8 more
wiley   +1 more source

Predicting the academic achievement of students using black hole optimization and Gaussian process regression. [PDF]

Sci Rep
Chen Y, Yao X.
europepmc   +1 more source

Learning Work Function via Implicit Reasoning on Electrostatic Potential Landscapes

Advanced Science, EarlyView.
StructPot‐CLR establishes a cross‐modal contrastive learning framework that aligns the crystal structures of 2D materials with plane‐averaged electrostatic potential landscapes for physically informed work‐function prediction. The model achieves an MAE of 0.265 eV and an R2 of 0.902 on the held‐out test set while accurately preserving key morphological
Haoyu Wan, Yue Wu, Tianhao Su, Deng Pan
wiley   +1 more source