Results 171 to 180 of about 4,069,375 (260)

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

Representing Born effective charges with equivariant graph convolutional neural networks. [PDF]

Sci Rep
Kutana A, Shimizu K, Watanabe S, Asahi R.   +3 more
europepmc   +1 more source

Classification of Cancer Types Using Graph Convolutional Neural Networks. [PDF]

Front Phys, 2020
Ramirez R, Chiu YC, Hererra A, Mostavi M, Ramirez J, Chen Y, Huang Y, Jin YF.   +7 more
europepmc   +1 more source

AS‐pHopt: An Optimal pH Prediction Model Enhanced by Active Site of Enzymes

Advanced Intelligent Discovery, EarlyView.
To address the low accuracy of enzyme optimal pH (pHopt) prediction, this study develops active site‐based pHopt (AS‐pHopt), a prediction model enhanced by active site information and pseudo‐label prediction. Integrating key structural and physicochemical features affecting enzyme pHopt, AS‐pHopt uses Evolutionary Scale Modeling (ESM)‐2 with active ...
Wenxiang Song, Xinmin Li, Hongbo Yu, Le Xiong, Guixia Liu, Weihua Li, Yun Tang   +6 more
wiley   +1 more source

MVHGCN: Predicting circRNA-disease associations with multi-view heterogeneous graph convolutional neural networks. [PDF]

PLoS Comput Biol
Miao Y, Tang X, Wang C, Sun Z, Wang G, Huang S.   +5 more
europepmc   +1 more source

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Advanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi, Yu Kumagai, Arata Takamatsu, Fumiyasu Oba   +3 more
wiley   +1 more source

Enhanced Simulation of Complicated MXene Materials with Graph Convolutional Neural Networks. [PDF]

Chemphyschem
Chen X, Wan Z, Lao S, Tian Z.
europepmc   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

Graph convolutional neural networks improved target-specific scoring functions for cGAS and kRAS in virtual screening. [PDF]

Comput Struct Biotechnol J
Wang B, Junaid M, Li W.
europepmc   +1 more source

Estimating Tissue Microstructure with Undersampled Diffusion Data via Graph Convolutional Neural Networks. [PDF]

Med Image Comput Comput Assist Interv, 2020
Chen G, Hong Y, Zhang Y, Kim J, Huynh KM, Ma J, Lin W, Shen D, Yap PT, UNC/UMN Baby Connectome Project Consortium.   +9 more
europepmc   +1 more source