Results 221 to 230 of about 145,055 (276)

Leveraging commonality across multiple tissue slices for enhanced whole slide image classification using graph convolutional networks. [PDF]

BMC Med Imaging
Noree S, Quinones Robles WR, Ko YS, Yi MY.   +3 more
europepmc   +1 more source

A Dynamic Model for Early Prediction of Alzheimer's Disease by Leveraging Graph Convolutional Networks and Tensor Algebra. [PDF]

Pac Symp Biocomput
Ozdemir C, Olaimat MA, Bozdag S, Alzheimer’s Disease Neuroimaging Initiative.   +3 more
europepmc   +1 more source

Analyzing world city network by graph convolutional networks. [PDF]

Sci Rep
Tian L, Rao W, Zhao K, Vo HT.
europepmc   +1 more source

TGNet: tensor-based graph convolutional networks for multimodal brain network analysis. [PDF]

BioData Min
Kong Z, Zhou R, Luo X, Zhao S, Ragin AB, Leow AD, He L.   +6 more
europepmc   +1 more source

autoscoRA: Deep Learning to Automate Sharp/van der Heijde Scoring of Radiographic Damage in Rheumatoid Arthritis

Arthritis &Rheumatology, Accepted Article.
Objective Regular imaging by conventional radiography to assess for joint damage is a cornerstone in the management of rheumatoid arthritis (RA). Scoring systems to quantify such damage, such as the widely used Sharp/van der Heijde (SvdH) score, are limited by the requirement of time and experienced staff as well as intra‐ and inter‐rater variability ...
Thomas Deimel, Paul J. Weiser, Martin Urschler, Christian Payer, Peter Mandl, Georg Langs, Daniel Aletaha   +6 more
wiley   +1 more source

Graph Convolutional Networks for multi-modal robotic martial arts leg pose recognition. [PDF]

Front Neurorobot
Yao S, Ping Y, Yue X, Chen H.
europepmc   +1 more source

Transfer Learning Approaches in Bioprocess Engineering: Opportunities and Challenges

Biotechnology and Bioengineering, EarlyView.
ABSTRACT Transfer learning (TL) has recently emerged as a promising approach to overcoming one of the key limitations of bioprocess engineering: data scarcity. By leveraging knowledge from one bioprocess to another, TL allows existing models and data sets to be reused efficiently, accelerating process development, improving prediction accuracy, and ...
Daniel Barón Díaz, Anna‐Lena Drommershausen, Alexander Grünberger, Dirk Holtmann   +3 more
wiley   +1 more source

CellMsg: graph convolutional networks for ligand-receptor-mediated cell-cell communication analysis. [PDF]

Brief Bioinform
Xia H, Ji B, Qiao D, Peng S.
europepmc   +1 more source

Addressing Small Data Challenges in Biopharmaceutical Development and Manufacturing: A Mini Review of Multi‐Fidelity Techniques

Biotechnology and Bioengineering, EarlyView.
ABSTRACT The growing demand for biopharmaceutical products reflects their effectiveness in medical treatments. However, developing new biopharmaceuticals remains a major bottleneck, often taking up to a decade before market approval. Machine learning (ML) models have the potential to accelerate this process, but their success depends on access to large
Mohammad Golzarijalal, Uwe Aickelin, Ellen Otte   +2 more
wiley   +1 more source

ProG-SOL: Predicting Protein Solubility Using Protein Embeddings and Dual-Graph Convolutional Networks. [PDF]

ACS Omega
Li G, Zhang N, Fan L.
europepmc   +1 more source