Douglas A. Lauffenburger

The paper uses a deep learning framework to analyze drug-induced transcriptomics and identifies a four-node module involved in TGFβ-IFNα competition in MASH.

This paper introduces a method to evaluate the robustness of biology-informed neural networks by measuring their ability to prune spurious interactions during training.

scLEMBAS models signaling pathway activity at single-cell resolution using a context-aware neural network to predict perturbation responses and capture cell-type-specific interactions.

The paper profiles antibody responses in children to identify correlates of resilience to Staphylococcus aureus disease and recurrence, finding that functional antibodies and Fc receptor engagement are key protective factors.

The paper develops an optimized ELLA assay for quantifying IgG glycosylation, improving reproducibility and cross-study comparability through standardized protocols and a quantitative surrogate standard.

FlowTransOP translates omics signatures between domains without paired samples or feature mappings, outperforming existing methods when data are sparse or features are weakly correlated.

This paper develops a vascularized liver microphysiological system to model hepatic insulin resistance and monocyte infiltration, showing altered metabolism, vascular changes, and immune cell recruitment.

This study identifies cross-species blood transcriptional correlates of BCG-mediated protection against tuberculosis using a systems modeling framework, linking innate and adaptive immune processes to vaccine efficacy in humans and non-human primates.

The paper uses cytokine perturbations in B cells to analyze glycogene expression and IgG glycosylation, identifying cytokine-specific signatures and glycosylation profiles.

The paper identifies novel IL13Rα2-targeted CARs with improved anti-glioblastoma efficacy using a pooled screening platform.

The paper develops a machine learning framework to identify optimal experimental conditions for improving the translatability of microphysiological systems to in vivo contexts, using MAFLD as a case study.

The paper uses Markov Fields to integrate multi-modal immune data from vaccinated macaques, identifying critical immune interactions and predicting perturbation effects.

This study uses multi-omics approaches to analyze myeloid cell diversity in TB granulomas and identifies IFN-γ, TGF-β, and time as factors driving macrophage diversity.

The paper uses single-cell sequencing to analyze CAR signaling and identifies ICD architectures that promote durable in vivo persistence in liquid tumors.

The paper uses cross-species modeling to identify the unfolded protein response as a key factor in the transcriptional response to Mycobacterium tuberculosis infection.

The paper develops a deep learning model to infer drug-target interactions and their downstream effects on cellular signaling, outperforming basic machine learning approaches and validating predictions with an independent dataset.

The paper develops AutoTransOP, an autoencoder framework to translate omics signatures across species and contexts, outperforming existing methods in identifying predictive molecular features.

Spateo provides a framework for spatiotemporal modeling of single-cell spatial transcriptomics data to study cell-cell interactions and organ development in 3D space.

The paper shows JNK and Erk signaling pathways have distinct roles in initiating and maintaining cell senescence after DNA damage through AP-1 and the senescence-associated secretory phenotype.

The paper develops a recurrent neural network to simulate intracellular signaling by predicting transcription factor activity from ligand concentrations, validated with synthetic and experimental data.

This study uses quantitative phosphoproteomics to identify dysregulated kinase networks associated with Alzheimer's disease histopathologies.

The paper develops a translation model to identify integrin signaling in anti-TNF-resistant Crohn’s disease and validates it using single-cell RNA sequencing.

The paper benchmarks bulk functional genomics tools on scRNA-seq data, showing comparable performance to bulk data and suggesting their applicability over dedicated single-cell tools.

This study shows that certain antibiotics increase CDAD risk through host-driven changes, not just microbiome disruption, by impairing mucosal barriers and immune function in human gut models.

This paper develops a semi-supervised learning framework to translate genomic responses from mouse models to human inflammatory disease contexts, finding it more accurate than direct generalization.