Results 121 to 130 of about 264,591 (190)

De Novo Structure Prediction from Tandem Mass Spectra: Algorithms, Benchmarks, and Limitations. [PDF]

Molecules
Schneider MY, Kholmanskikh DD, Romanov KY, Perekina EA, Nikolenko SA, Lukin RY, Golov IV.   +6 more
europepmc   +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

Prediction of Standard Combustion Enthalpy of Organic Compounds Combining Machine Learning and Chemical Graph Theory: A Strategy. [PDF]

ACS Omega
Saviñon-Flores F, Arzola-Flores JA, García-Castro MA, Díaz-Sánchez F, Vidal Robles E, Maruri Valderrabano FA.   +5 more
europepmc   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Anatomical Associations Between Focal Mitochondrial Metabolism and Patterns of Neurodegeneration in Amyotrophic Lateral Sclerosis. [PDF]

Ann Neurol
Tahedl M, Siah WF, Karavasilis E, Hengeveld JC, Doherty MA, McLaughlin RL, Hardiman O, Tan EL, Christidi F, Kleinerova J, Bede P.   +10 more
europepmc   +1 more source

Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy

Advanced Intelligent Discovery, EarlyView.
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang, Andrew Bai, Alexandra Bermudez, Yunqi Hong, Zoe Latham, Iris Sloan, Michael Liu, Vishrut Goyal, Cho‐Jui Hsieh, Neil Y. C. Lin   +9 more
wiley   +1 more source

Integrative multi-omics and network-based machine learning for early diagnosis of Parkinson's disease. [PDF]

PLoS One
Liu W, Xu L, Wang X, Wang J.
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, Jesus Gil, Pedro J. Ballester   +2 more
wiley   +1 more source

Dynamic causal weighting-based risk propagation modeling for airport movement areas. [PDF]

Sci Rep
Wu W, Lin J, Wei M, Wang X, Zhu L.
europepmc   +1 more source

Terrestrial Cyborg Insects for Real‐Life Applications

Advanced Intelligent Systems, EarlyView.
This article reviews the development of terrestrial cyborg insects from their emergence in 1997 to mid‐2025, examining three key aspects: locomotion control methods, associated challenges with proposed solutions, and practical applications. Framing these biohybrid systems as insect‐scale mobile robots, the review provides foundational insights for new ...
Hai Nhan Le, Lachlan Fitzgerald, Phuoc Thien Phan, Robbie Wilson, Christofer Clemente, Huu Duoc Nguyen, Hirotaka Sato, Hoang‐Phuong Phan, Peter Ross McAree, Thanh Nho Do, Tat Thang Vo‐Doan   +10 more
wiley   +1 more source