Results 191 to 200 of about 97,243 (265)

Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology. [PDF]

JHEP Rep
Beaufrère A, Lazard T, Nicolle R, Lubuela G, Augustin J, Albuquerque M, Pichon B, Pignolet C, Priori V, Théou-Anton N, Lesurtel M, Bouattour M, Mondet K, Cros J, Calderaro J, Walter T, Paradis V.   +16 more
europepmc   +1 more source

Machine Learning Accelerated Non‐Adiabatic Molecular Dynamics Elucidates Local Polarization Effects on Non‐radiative Recombination in Halide Perovskites

Advanced Science, EarlyView.
This work proposes and constructs the Hefei‐NAMD‐S framework based on machine learning stacked models to investigate the relationship between local polarization and non‐radiative recombination. The results indicate that, compared with A‐site local polarization, B‐site local polarization shows a more evident association with the non‐radiative ...
Bing Yang, Xiaoli Wei, Bo Cai, Yan Yang, Xinghai Zhu, Yi Liu, Junmin Xia, Lihui Liu, Kun Cao, Wei Shen, Pengfei Xia, Shufen Chen, Siyu Chen, Jin Zhao   +13 more
wiley   +1 more source

Weakly supervised colorectal gland segmentation through self-supervised learning and attention-based pseudo-labeling. [PDF]

Sci Rep
Wen H, Wu Y, Huang D, Liu C.
europepmc   +1 more source

stMixer for Scalable Mosaic Integration and Label Transfer in Spatial Histology and Multi‐Omics

Advanced Science, EarlyView.
stMixer is an unsupervised framework for scalable integration and label transfer across spatial histology and multi‐slide multi‐omics data with incomplete modality overlap. It combines self‐looped cross‐attention, multimodal metric learning, and graph‐guided cluster voting to align heterogeneous sections, correct batch effects, and propagate ...
Qixing Yang, Yan Wang, Luonan Chen, Chunman Zuo   +3 more
wiley   +1 more source

The detection of algebraic auditory structures emerges with self-supervised learning. [PDF]

PLoS Comput Biol
Orhan P, Boubenec Y, King JR.
europepmc   +1 more source

Machine Learning‐Driven Prediction of Microplastic Aging Processes and Environmental Risk Assessment Across Multi‐Media Systems

Advanced Science, EarlyView.
This perspective proposes a cohesive machine learning strategy to decode microplastic aging. It advocates for Federated Learning to dismantle global data silos and introduces the TRACE framework (TRansport, Aging, Corona, Ecotoxicity). By integrating physics‐informed modeling with causal discovery, this approach bridges the laboratory‐field gap to ...
Yaping Lyu, Xinran Qiu, Xing Li, Tianhuan Yang, Xuetao Guo, Hao Qiu, Peng Zhang   +6 more
wiley   +1 more source

A novel dual-dimensional contrastive self-supervised learning-based framework for rolling bearing remaining useful life prediction. [PDF]

Sci Rep
Shen Z, Yang C, Cheng L, Kong X, Liu Z, Su K.   +5 more
europepmc   +1 more source

Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design

Advanced Science, EarlyView.
A large language model (LLM) based pipeline is developed to automatically extract a comprehensive and accurate multicomponent alloy database from literature corpus. The extracted dataset is integrated with sustainability indicators to identify potential alloys that outperform existing industrial benchmark materials in terms of both performance and ...
Aravindan Kamatchi Sundaram, Mohit Chakraborty, Sai Mani Kumar Devathi, B. Pabitramohan Prusty, Rohit Batra   +4 more
wiley   +1 more source

Improving Coronary Artery Disease Diagnosis in Cardiac MRI with Self-Supervised Learning. [PDF]

Diagnostics (Basel)
Khalid U, Kaya M, Alhajj R.
europepmc   +1 more source

ProSiteHunter: A Unified Framework for Sequence‐Based Prediction of Protein‐Nucleic Acid and Protein‐Protein Binding Sites

Advanced Science, EarlyView.
This study proposed a unified sequence‐based framework for protein binding site prediction, which adopted a tri‐track semantic multi‐source feature fusion strategy to effectively capture diverse macromolecular interaction sites and further improved the accuracy of antibody‐antigen interaction prediction.
Dongliang Hou, Qihang Zhen, Zexin Lv, Xinyue Cui, Suhui Wang, Minghua Hou, Zhan Zhou, Xiaogen Zhou, Guijun Zhang   +8 more
wiley   +1 more source