A Nested Model for AI Design and Validation DOI Creative Commons
Akshat Dubey, Zewen Yang, Georges Hattab

et al.

iScience, Journal Year: 2024, Volume and Issue: 27(9), P. 110603 - 110603

Published: July 30, 2024

The growing AI field faces trust, transparency, fairness, and discrimination challenges. Despite the need for new regulations, there is a mismatch between regulatory science AI, preventing consistent framework. A five-layer nested model design validation aims to address these issues streamline application validation, improving adoption. This aligns with addresses practitioners' daily challenges, offers prescriptive guidance determining appropriate evaluation approaches by identifying unique validity threats. We have three recommendations motivated this model: (1) Authors should distinguish layers when claiming contributions clarify specific areas in which contribution made avoid confusion; (2) authors explicitly state upstream assumptions ensure that context limitations of their system are clearly understood, (3) venues promote thorough testing systems compliance requirements.

Language: Английский

A beginner’s approach to deep learning applied to VS and MD techniques DOI Creative Commons
Stijn D'Hondt, José Oramas, Hans De Winter

et al.

Journal of Cheminformatics, Journal Year: 2025, Volume and Issue: 17(1)

Published: April 8, 2025

Language: Английский

Citations

0
A Novel Approach to Automated Cybersecurity Response for Critical Infrastructures Using Graph Neural Networks and Reinforcement Learning DOI
Aws Naser Jaber, Maria Christopoulou, Giordano Colò

et al.

Lecture notes on data engineering and communications technologies, Journal Year: 2025, Volume and Issue: unknown, P. 35 - 47

Published: Jan. 1, 2025

Language: Английский

Citations

0
3DReact: Geometric Deep Learning for Chemical Reactions DOI Creative Commons
Puck van Gerwen, Ksenia R. Briling, Charlotte Bunne

et al.

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(15), P. 5771 - 5785

Published: July 15, 2024

Geometric deep learning models, which incorporate the relevant molecular symmetries within neural network architecture, have considerably improved accuracy and data efficiency of predictions properties. Building on this success, we introduce 3DReact, a geometric model to predict reaction properties from three-dimensional structures reactants products. We demonstrate that invariant version is sufficient for existing sets. illustrate its competitive performance prediction activation barriers GDB7-22-TS, Cyclo-23-TS, Proparg-21-TS sets in different atom-mapping regimes. show that, compared models property prediction, 3DReact offers flexible framework exploits information, if available, as well geometries products (in an or equivariant fashion). Accordingly, it performs systematically across sets, regimes, both interpolation extrapolation tasks.

Language: Английский

Citations

3
Knowledge graph driven medicine recommendation system using graph neural networks on longitudinal medical records DOI Creative Commons

Rajat Mishra,

S. Shridevi

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 26, 2024

Abstract Medicine recommendation systems are designed to aid healthcare professionals by analysing a patient’s admission data recommend safe and effective medications. These categorised into two types: instance-based longitudinal-based. Instance-based models only consider the current admission, while longitudinal medical history. Electronic Health Records used incorporate history models. This project proposes novel K nowledge G raph- D riven Recommendation System using Graph Neural Net works, KGDNet , that utilises EHR along with ontologies Drug-Drug Interaction knowledge construct admission-wise clinical medicine Knowledge Graphs for every patient. Recurrent Networks employed model historical data, learn embeddings from Graphs. A Transformer-based Attention mechanism is then generate medication recommendations patient, considering their state, history, joint records. The evaluated on MIMIC-IV outperforms existing methods in terms of precision, recall, F1 score, Jaccard control. An ablation study our various inputs components provide evidence importance each component providing best performance. Case also performed demonstrate real-world effectiveness KGDNet.

Language: Английский

Citations

3
A Nested Model for AI Design and Validation DOI Creative Commons
Akshat Dubey, Zewen Yang, Georges Hattab

et al.

iScience, Journal Year: 2024, Volume and Issue: 27(9), P. 110603 - 110603

Published: July 30, 2024

The growing AI field faces trust, transparency, fairness, and discrimination challenges. Despite the need for new regulations, there is a mismatch between regulatory science AI, preventing consistent framework. A five-layer nested model design validation aims to address these issues streamline application validation, improving adoption. This aligns with addresses practitioners' daily challenges, offers prescriptive guidance determining appropriate evaluation approaches by identifying unique validity threats. We have three recommendations motivated this model: (1) Authors should distinguish layers when claiming contributions clarify specific areas in which contribution made avoid confusion; (2) authors explicitly state upstream assumptions ensure that context limitations of their system are clearly understood, (3) venues promote thorough testing systems compliance requirements.

Language: Английский

Citations

2