Deep learning in computational biology : from predictive modeling to knowledge extraction
Keywords: Chemistry.

The rapid development of deep learning methods has transformed concepts and pipelines in the analysis of large-scale data cohorts. In parallel, datasets of unprecedented size and diversity stemming from novel biological experimental techniques have largely exceeded the capacity of conventional human-engineered tools. Driven by the versatility and expressive power of deep neural networks, the past few years have witnessed a burst in efforts to incorporate deep learning-based techniques to model the rich information from experimental data. In addition to the need for accurate predictive modeling, biological research problems place great emphasis on model interpretability, aiming to unravel the underlying mechanism by extracting model-learned knowledge. With these challenges posed by the new techniques and datasets in mind, I present three works in this thesis that developed deep learning-based tools to model, analyze and understand various types of molecular and cellular data. In the first project, we summarized methods and datasets for molecular machine learning and proposed a large-scale benchmark MoleculeNet to facilitate the comparison of model efficacy. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high-quality open-source implementations of multiple molecular featurization and learning algorithms. MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance, though learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. We further recognized that for quantum mechanical and biophysical datasets, the use of physics-aware featurization can be more important than the choice of modeling algorithm. In the second project, we proposed an automated analysis tool: DynaMorph for quantitative live-cell imaging. DynaMorph is composed of multiple modules sequentially applied to perform cell segmentation, tracking, and self-supervised morphology encoding. We employed DynaMorph to learn the cellular morphodynamics of live microglia through label-free measurements of optical density and anisotropy. These cells show complex behavior and have varied responses to disease-relevant perturbations. DynaMorph generates quantitative morphodynamic representations that can be used to compare the effects of the perturbations. Furthermore, by analyzing DynaMorph representations we identify distinct morphodynamic states of microglia polarization and detect rare transition events between states. In the third project, I studied spatial cellular community structures based on multiplex immunofluorescence imaging. By parsing high-resolution immunofluorescence images as graphical representations of cellular communities, we developed SPAtial CEllular Graphical Modeling (SPACE-GM), a geometric deep learning framework that models tumor microenvironments as cellular graphs. We applied SPACE-GM to human head-and-neck and colorectal cancer samples assayed with 40-plex immunofluorescence imaging to identify spatial motifs associated with patient survival and recurrence outcomes after immunotherapy. SPACE-GM achieves substantially higher accuracy in predicting patient outcomes than previous approaches based on neighborhood cell-type compositions. Computational interpretation of the disease-relevant microenvironments identified by SPACE-GM generates insights into the effect of spatial dispersion of tumor cells and granulocytes on patient prognosis