Results 151 to 160 of about 598,821 (278)
Advanced Intelligent Discovery, EarlyView.A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Autonomous AI‐Driven Design for Skin Product Formulations
Advanced Intelligent Discovery, EarlyView.This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang +5 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta +3 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary +1 more
wiley +1 more source
Conflict in ant phylogeny results from complex interaction between multiple evolutionary signals and tree reconstruction artifacts. [PDF]
Mol Biol EvolTao W +6 more
europepmc +1 more source
Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics
Advanced Intelligent Discovery, EarlyView.A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha +2 more
wiley +1 more source
Genomic Diversity of Avocado in the Morogoro Region and Southern Highlands of Tanzania. [PDF]
Int J Mol SciCortés AJ, Hussein JM, Juma I.
europepmc +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This review aims to provide a broad understanding for interdisciplinary researchers in engineering and clinical applications. It addresses the development and control of magnetic actuation systems (MASs) in clinical surgeries and their revolutionary effects in multiple clinical applications.
Yingxin Huo +3 more
wiley +1 more source
Semiochemical interruption of Dendroctonus rufipennis (Coleoptera: Curculionidae) in Alaska and Colorado, U.S. [PDF]
J Econ EntomolAudley JP +6 more
europepmc +1 more source