Results 161 to 170 of about 37,604 (258)

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source

Advancing Efficient Error Reduction in DNA Data Storage Systems with Deep Learning‐Based Denoising Models

Advanced Intelligent Discovery, EarlyView.
Deep learning‐based denoising models are applied to DNA data storage systems to enhance error reduction and data fidelity. By integrating DnCNN with DNA sequence encoding methods, the study demonstrates significant improvements in image quality and correction of substitution errors, revealing a promising path toward robust and efficient DNA‐based ...
Seongjun Seo, Anshula Tandon, Nguyen Kim Uyen, Quoc Nam Le, Jee‐Hyong Lee, Sung Ha Park   +5 more
wiley   +1 more source

Enhancing WSI image classification with graph convolutional neural networks and model uncertainty modeling. [PDF]

BMC Med Imaging
Liu C, Cheng Y, Li T, Hao Y, Zhang Q.
europepmc   +1 more source

Automating AI Discovery for Biomedicine Through Knowledge Graphs and Large Language Models Agents

Advanced Intelligent Discovery, EarlyView.
This work proposes a novel framework that automates biomedical discovery by integrating knowledge graphs with multiagent large language models. A biologically aligned graph exploration strategy identifies hidden pathways between biomedical entities, and specialized agents use this pathway to iteratively design AI predictors and wet‐lab validation ...
Naafey Aamer, Muhammad Nabeel Asim, Shan Munir, Andreas Dengel   +3 more
wiley   +1 more source

Digital image quality evaluation based on multi-scale aesthetic features and graph convolutional neural networks. [PDF]

Sci Rep
Wu J, Li D.
europepmc   +1 more source

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

Advanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows, G. Zhang, S. Anand, Z. Chen, J. Lin, A. Desai, S. Martiniani, F. Caravelli   +7 more
wiley   +1 more source

Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes. [PDF]

Inf Process Med Imaging
Chen Y, Farazi M, Yang Z, Fan Y, Ashton N, Reiman EM, Su Y, Wang Y.   +7 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

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
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