Progress Update #3: Context-aware Adapters

The second part of the framework is a set of context-aware adapters. They let one model make predictions across biological settings so a separate model is not needed for each. Each step is built with a specific method.

Every input is defined by a seven-factor context: species, cell type, donor, genotype, microenvironment, dose and timepoint. A context embedding generator encodes these categorical and numerical values into a normalized 16-dimensional vector.

A Feature-wise Linear Modulation (FiLM) layer then takes that vector and computes a scale and a shift . These are applied to a hidden representation. The weights of the underlying model stay fixed while its behavior adjusts to the context being queried.

In a multiscale graph, this modulation re-weights nodes and edges for the context. The edge weights from cellular states to tissues scale with pH, hypoxia, dose and exposure duration. The same cellular effect can therefore carry a beneficial weight in an efficacy tissue and a liability weight in a protected host structure.

The embedding and FiLM operations are linear and vectorized so they run in milliseconds. Each context passes its vector to the layers that follow.

What's next : Applying the context-aware adapters to evaluate how modulating the host ISC machinery behaves across different contexts.