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2020 – today
- 2024
[j6]Chloe Engler Hart, António J. Preto, Shaurya Chanana, David Healey, Tobias Kind, Daniel Domingo-Fernández:
Evaluating the generalizability of graph neural networks for predicting collision cross section. J. Cheminformatics 16(1): 105 (2024)- 2023
- [j5]Chloe Engler Hart, Daniel Ence, David Healey, Daniel Domingo-Fernández:
On the correspondence between the transcriptomic response of a compound and its effects on its targets. BMC Bioinform. 24(1): 207 (2023) - [j4]Daniel Domingo-Fernández, Yojana Gadiya
, Sarah Mubeen, David Healey, Bryan H. Norman, Viswa Teja S. S. Colluru:
Exploring the known chemical space of the plant kingdom: insights into taxonomic patterns, knowledge gaps, and bioactive regions. J. Cheminformatics 15(1): 107 (2023) - [c1]Michael Murphy, Stefanie Jegelka, Ernest Fraenkel, Tobias Kind, David Healey, Thomas Butler:
Efficiently predicting high resolution mass spectra with graph neural networks. ICML 2023: 25549-25562 - [i3]Michael Murphy, Stefanie Jegelka, Ernest Fraenkel, Tobias Kind, David Healey, Thomas Butler:
Efficiently predicting high resolution mass spectra with graph neural networks. CoRR abs/2301.11419 (2023) - 2022
- [j3]Daniel Rivas-Barragan, Daniel Domingo-Fernández, Yojana Gadiya, David Healey:
Ensembles of knowledge graph embedding models improve predictions for drug discovery. Briefings Bioinform. 23(6) (2022) - [j2]Daniel Domingo-Fernández, Yojana Gadiya, Abhishek Patel, Sarah Mubeen, Daniel Rivas-Barragan, Chris W. Diana, Biswapriya B. Misra, David Healey, John C. Rokicki, Viswa Teja S. S. Colluru:
Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery. PLoS Comput. Biol. 18(2) (2022) - [i2]Zhong Fan, Jun Cao, Taskin Jamal, Chris Fogwill, Cephas Samende, Zoe Robinson, Fiona Polack, Mark Ormerod, Sharon George, Adam Peacock, David Healey:
The role of living laboratories in unlocking the potential of low-carbon energy technologies on the journey to net-zero. CoRR abs/2204.00293 (2022) - [i1]Gennady Voronov, Rose Lightheart, Joe Davison, Christoph A. Krettler, David Healey, Thomas Butler:
Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry Data. CoRR abs/2207.02980 (2022) - 2020
- [j1]Jun Cao, Dan Harrold, Zhong Fan, Thomas Morstyn, David Healey, Kang Li:
Deep Reinforcement Learning-Based Energy Storage Arbitrage With Accurate Lithium-Ion Battery Degradation Model. IEEE Trans. Smart Grid 11(5): 4513-4521 (2020)
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