Machine learning for synergistic network pharmacology: a comprehensive overview
Network pharmacology is an emerging area of systematic drug research that attempts to
understand drug actions and interactions with multiple targets. Network pharmacology has …
understand drug actions and interactions with multiple targets. Network pharmacology has …
Explainable, domain-adaptive, and federated artificial intelligence in medicine
Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in
each domain is driven by a growing body of annotated data, increased computational …
each domain is driven by a growing body of annotated data, increased computational …
[HTML][HTML] The FeatureCloud platform for federated learning in biomedicine: unified approach
Background Machine learning and artificial intelligence have shown promising results in
many areas and are driven by the increasing amount of available data. However, these data …
many areas and are driven by the increasing amount of available data. However, these data …
Human-in-the-loop integration with domain-knowledge graphs for explainable federated deep learning
A Holzinger, A Saranti, AC Hauschild… - … -Domain Conference for …, 2023 - Springer
We explore the integration of domain knowledge graphs into Deep Learning for improved
interpretability and explainability using Graph Neural Networks (GNNs). Specifically, a …
interpretability and explainability using Graph Neural Networks (GNNs). Specifically, a …
[HTML][HTML] Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions
Abstract The Internet of Things (IoT) has revolutionized various sectors by enabling
seamless device interaction. However, the proliferation of IoT devices has also raised …
seamless device interaction. However, the proliferation of IoT devices has also raised …
A federated learning-enabled predictive analysis to forecast stock market trends
S Pourroostaei Ardakani, N Du, C Lin, JC Yang… - Journal of Ambient …, 2023 - Springer
This article proposes a federated learning framework to build Random Forest, Support
Vector Machine, and Linear Regression models for stock market prediction. The …
Vector Machine, and Linear Regression models for stock market prediction. The …
[HTML][HTML] Learning vs. understanding: When does artificial intelligence outperform process-based modeling in soil organic carbon prediction?
In recent years, machine learning (ML) algorithms have gained substantial recognition for
ecological modeling across various temporal and spatial scales. However, little evaluation …
ecological modeling across various temporal and spatial scales. However, little evaluation …
Collaborative weighting in federated graph neural networks for disease classification with the human-in-the-loop
The authors introduce a novel framework that integrates federated learning with Graph
Neural Networks (GNNs) to classify diseases, incorporating Human-in-the-Loop …
Neural Networks (GNNs) to classify diseases, incorporating Human-in-the-Loop …
Federated learning in health care using structured medical data
W Oh, GN Nadkarni - Advances in kidney disease and health, 2023 - Elsevier
The success of machine learning–based studies is largely subjected to accessing a large
amount of data. However, accessing such data is typically not feasible within a single health …
amount of data. However, accessing such data is typically not feasible within a single health …
A survey on class imbalance in federated learning
J Zhang, C Li, J Qi, J He - arXiv preprint arXiv:2303.11673, 2023 - arxiv.org
Federated learning, which allows multiple client devices in a network to jointly train a
machine learning model without direct exposure of clients' data, is an emerging distributed …
machine learning model without direct exposure of clients' data, is an emerging distributed …