Dynamically Assembling Biological Intelligence to Predict Novel Cellular Phenotypes

Abstract

In this work, we introduce Bio-AMLM (Biological Adaptive Modular Learning Model), a new framework designed to address out-of-distribution (OOD) generalization challenges in predicting cellular responses. Unlike monolithic deep learning models or simple data retrieval methods, which struggle to predict the effects of novel genetic or chemical perturbations, Bio-AMLM dynamically constructs a bespoke analytical pipeline for each biological query. It leverages a library of pre-trained, functionally specialized biological modules (e.g., for genomic, proteomic, and metabolic analysis). Guided by a biological context encoder, an adaptive inference planner selects, configures, and links these modules to form an optimal analysis chain. In experiments on several challenging bio-simulation benchmarks, including Gene-Edit-Bench, Drug-Response-Bench, and Toxicity-Bench, Bio-AMLM consistently outperformed state-of-the-art approaches, producing more reliable, robust, and interpretable predictions of cellular behavior in complex OOD scenarios.