Biologically-Informed Networks Predict Phosphoproteomic Drug Responses
Researchers Antonopoulos, Nordenstorm, and Nilsson (published March 18, 2026 in PLoS Computational Biology) extend the LEMBAS biologically informed recurrent neural network to predict time‑resolved phosphoproteomic trajectories from mass‑spectrometry timeseries. They introduce phosphosite mapping and a monotonic time mapping, and demonstrate accurate interpolation and zero‑shot drug‑response prediction on an EGF stimulation dataset, outperforming naïve and fully connected baselines. The model also reveals canonical and non‑canonical signaling effects.
Scoring Rationale
Strong novelty and peer‑reviewed validation with practical zero‑shot capability; applicability mainly focused on phosphoproteomic signaling studies.
Practice interview problems based on real data
1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with.
Try 250 free problemsStep-by-step roadmaps from zero to job-ready — curated courses, salary data, and the exact learning order that gets you hired.
Sources
- Read OriginalZero-shot prediction of drug responses using biologically informed neural networks trained on phosphoproteomic timeseriesjournals.plos.org
