Dynamically Assembling Biological Intelligence to Predict Novel Cellular Phenotypes 1. The article introduces Bio-AMLM, a groundbreaking framework designed to enhance out-of-distribution (OOD) generalization in predicting cellular responses. Unlike traditional models, Bio-AMLM dynamically constructs a bespoke analytical pipeline for each biological query, leveraging a library of pre-trained, functionally specialized biological modules. 2. The core innovation of Bio-AMLM lies in its Adaptive Inference Planner. This planner, guided by a biological context encoder, intelligently selects, configures, and links specialized modules to form an optimal analysis chain. This dynamic assembly allows the model to adapt its analytical logic to new types of biological problems, significantly improving its robustness and accuracy. 3. Bio-AMLM was rigorously tested on several challenging bio-simulation benchmarks, including Gene-Edit-Bench, Drug-Response-Bench, and Toxicity-Bench. The results were impressive: Bio-AMLM consistently outperformed state-of-the-art approaches, demonstrating superior performance in predicting cellular behavior under complex OOD conditions. 4. The framework's modular nature not only enhances its adaptability but also improves interpretability. Domain experts rated Bio-AMLM highly for its interpretability and robustness in novel tasks, finding the visualized analysis chain highly insightful for downstream wet-lab experiments. 5. Future work will focus on expanding the biological module library to include modules for immunology and single-cell transcriptomics, exploring reinforcement learning for the inference planner, and validating Bio-AMLM's predictions through prospective wet-lab experiments. 📜Paper: biorxiv.org/content/10.1101/… #BioAMLM #ComputationalBiology #OODGeneralization #DynamicAssembly #BiologicalPrediction