Results 201 to 210 of about 3,644,794 (274)

Satellite data show trees delay budburst across landscapes to escape herbivores. [PDF]

Nat Ecol Evol
Mallick S, Lichter J, Bae S, Kneib T, Molleman F, Leroy BML, Hilmers T, Huszarik M, Liebhold AM, Weisser WW, Jehle JA, Müller J, Prinzing A.   +12 more
europepmc   +1 more source

Spatially Informed Feature Selection and Machine Learning in Matrix‐Assisted Laser Desorption/Ionization Imaging for Cohort‐Scale Molecular Tissue Phenomics in Glioblastoma

Advanced Intelligent Discovery, EarlyView.
Matrix‐assisted laser desorption/ionization imaging‐based identification of reliable small molecule markers across heterogeneous glioblastoma cohorts is challenging with intensity‐only methods. We present spatially informed feature selection (SIFS), a spatially informed framework that prioritizes molecules consistently colocalizing with histopathology.
Shad A. Mohammed, Denis Abu Sammour, Dirk C. Hoffmann, Philipp Sievers, Tobias Bausbacher, Melissa Hahn, Yasemin Ucal, Iris Mildenberger, Christiane A. Opitz, Stefan Schmidt, Michael Platten, Oliver Wasenmüller, Wolfgang Wick, Andreas von Deimling, Tobias Kessler, Carsten Hopf   +15 more
wiley   +1 more source

Mitochondrial genomic variation, evolutionary dynamics, and phylogeny of forest-associated <i>Phytophthora</i> species in the Qinling Mountains of China. [PDF]

IMA Fungus
Sun Y, Chen X, Wang W, Li Y, Qian Z, Ma X, Hieno A, Kageyama K, Li M.   +8 more
europepmc   +1 more source

From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials

Advanced Intelligent Discovery, EarlyView.
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley   +1 more source

A systematic comparison of machine learning models for missing value imputation in household electricity consumption data. [PDF]

Sci Rep
Hanna B, Xu G, Wang X, Hossain J.
europepmc   +1 more source

Collaborative Visual Localization for Modular Self‐Reconfigurable Robots

Advanced Intelligent Systems, EarlyView.
Relative localization in modular self‐reconfigurable robots is challenged by hardware limitations, constrained fields of view, and sensor faults. This paper, based on the SnailBot platform, presents a vision‐based collaborative localization method that combines ArUco markers with learning‐based algorithms to enable robust pose estimation from ...
Guanqi Liang, Rong Ou, Peiqi Wang, Xianghong Wang, Tin Lun Lam   +4 more
wiley   +1 more source

Phylogenetic and Population Genetic Analyses Reveal Patterns of Divergence Among Isolates of <i>Ceratocystis manginecans</i>. [PDF]

Ecol Evol
Lynn KMT, Wingfield MJ, Oliveira LSS, Alfenas AC, Alfenas RF, Marincowitz S, Barnes I.   +6 more
europepmc   +1 more source

Integrating Reinforcement Learning With Explainable Artificial Intelligence for Real‐Time Clinical Decision Support in Dynamic Healthcare Environments

Advanced Intelligent Systems, EarlyView.
A hybrid Reinforcement Learning–Explainable AI framework integrates SHAP and LIME explanations directly into a Deep Q‐Network inference loop for real‐time ICU decision support. Trained on 18 142 mechanically ventilated stays from the eICU database, the system attains 93.0% decision accuracy, 20% fewer errors than RL alone, and a 91% clinician trust ...
Jannatul Ferdaus Disha, MD Faiaz Zaman Dehan, Kazi Md. Tanvir Anzum   +2 more
wiley   +1 more source

Machine learning approaches for cancer prognosis and diagnosis via non-coding RNA: a comprehensive review. [PDF]

Brief Bioinform
Hossain MS, Chen YP.
europepmc   +1 more source

Characterization and Selection of Contractile Actuators for Soft Robotics Operating in the Quasistatic Regime

Advanced Intelligent Systems, EarlyView.
We developed methods to characterize soft contractile actuators using force–displacement measurements in the passive state and the total force in the active state and define the operational range of thermal, electrothermal, and pneumatic muscles. A graphical method enables the selection of an actuator based on the required mechanical force–displacement
Qiong Wang, Liuyang Cheng, Jeongmin Kim, Samuel Tsai, Devin Roach, Sameh Tawfick   +5 more
wiley   +1 more source