Selected publications:
Dong, et al. Causal identification of single-cell experimental perturbation effects with CINEMA-OT. Nature Methods (2023, in press)
Fonseca, et al. Continuous Spatiotemporal Transformers. ICML (2023)
Ravindra, et al. Disease State Prediction From Single-Cell Data Using Graph Attention Networks CHIL (2020).
van Dijk, et al. Recovering Gene Interactions from Single-Cell Data Using Data Diffusion Cell (2018)
All publications:
2023
Cheemarla, et al. Nasal host response-based screening for undiagnosed respiratory viruses: a pathogen surveillance and detection study. Lancet Microbe, 2023
Fonseca, et al. Continuous Spatiotemporal Transformers. ICML 2023
Schultz, et al. A machine learning method for the identification and characterization of novel COVID-19 drug targets. Nat. Sci. Rep., 2023
Zappala, et al. Neural Integro-Differential Equations . AAAI 2023
Dong, et al. Causal identification of single-cell experimental perturbation effects with CINEMA-OT. In press at Nature Methods, 2023
2022
Rizvi, et al. AMPNet: Attention as Message Passing for Graph Neural Networks
Zappala, et al. Neural Integral Equations. arXiv, 2022
Rice, et al. Interspecies commensal interactions have nonlinear impacts on host immunity. Cell Host & Microbe, 2022
Collora, et al. Single-cell multiomics reveals persistence of HIV-1 in expanded cytotoxic T cell clones. Cell Immun. 2022
Hong, et al. Prdm6 controls heart development by regulating neural crest cell differentiation and migration. ASCI, 2022
Unterman, et al. Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19. Nat Commun, 2022
Klein, et al. Distinguishing features of Long COVID identified through immune profiling. medRxiv, 2022
2021
Tejwani, et al. Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1. bioRxiv, 2021
Amodio, et al. Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference Cell, 2021
Oikonomou, et al. A phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST). Eur Heart J. 2021
Golan, et al. Single-Cell Transcriptional Profiling of the Adult Corticospinal Tract Reveals Forelimb and Hindlimb Molecular Specialization. bioRxiv, 2021
Schultz, et al. A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization. Nat Sci Rep, 2021
Ramaswamy, et al. Immune dysregulation and autoreactivity correlate with disease severity in SARS-CoV-2-associated multisystem inflammatory syndrome in children. Cell, 2021
Collora, et al. Single-cell immune profiling reveals the impact of antiretroviral therapy on HIV-1-induced immune dysfunction, T cell clonal expansion, and HIV-1 persistence in vivo. bioRxiv, 2021
Ravindra, et al. Single-cell longitudinal analysis of SARS-CoV-2 infection in human airway epithelium identifies target cells, alterations in gene expression, and cell state changes. PLoS Biol. 2021
Sehanobish, et al. Permutation invariant networks to learn Wasserstein metrics. arXiv, 2021
Zhang, et al. Learning Potentials of Quantum Systems using Deep Neural Networks. arXiv, 2021
Burkhardt, et al. Quantifying the effect of experimental perturbations at single-cell resolution. Nat Biotechnol, 2021
Wei, et al. Genome-wide CRISPR Screens Reveal Host Factors Critical for SARS-CoV-2 Infection. Cell, 2021
2020
Amodio, et al. Learning General Transformations of Data for Out-of-Sample Extensions. MSLP 2020
Haimovich, et al. Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation. Ann Emerg Med, 2020
Haimovich, et al. Patient factors associated with SARS-CoV-2 in an admitted emergency department population. J Am Coll Emerg Physicians Open, 2020
Sehanobish, et al. Self-supervised edge features for improved Graph Neural Network training. arXiv, 2020
Ravindra, et al. Disease State Prediction From Single-Cell Data Using Graph Attention Networks. arXiv, 2020
Meizlish, et al. Circulating Markers of Angiogenesis and Endotheliopathy in COVID-19. medRxiv, 2020
Ravindra, et al. Single-cell longitudinal analysis of SARS-CoV-2 infection in human bronchial epithelial cells. bioRxiv, 2020
Sehanobish, et al. Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks. arXiv, 2020
Fonseca, et al. Learning aligned embeddings for semi-supervised word translation using Maximum Mean Discrepancy. arXiv, 2020
Tong, et al. TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics. arXiv, 2020
Tong, et al. Interpretable Neuron Structuring with Graph Spectral Regularization. arXiv, 2020
Ramaswamy. et al. Post-infectious inflammatory disease in MIS-C features elevated cytotoxicity signatures and autoreactivity that correlates with severity. medRxiv, 2020
Pappalardo, et al. Transcriptomic and clonal characterization of T cells in the human central nervous system. Sci Immunol, 2020
Meizlish, et al. A neutrophil activation signature predicts critical illness and mortality in COVID-19. medRxiv, 2020
Zhao, et al. Single cell immune profiling of dengue virus patients reveals intact immune responses to Zika virus with enrichment of innate immune signatures. PLoS Negl Trop Dis, 2020
Chen, et al. Uncovering axes of variation among single-cell cancer specimens. Nat Methods, 2020
McCullough, et al. Neurohormonal Blockade and Clinical Outcomes in Patients With Heart Failure Supported by Left Ventricular Assist Devices. JAMA Cardiol, 2020
Moon, et al. Author Correction: Visualizing structure and transitions in high-dimensional biological data. Nat Biotechnol, 2020
Brugnone, et al. Coarse Graining of Data via Inhomogeneous Diffusion Condensation. arXiv, 2020
Song, et al. Neuroinvasion of SARS-CoV-2 in human and mouse brain
2019 and before
van Dijk, et al. Finding Archetypal Spaces Using Neural Networks. arXiv, 2019
Gigante, et al. Compressed Diffusion. arXiv, 2019
Moon, et al. Visualizing transitions and structure for high dimensional data exploration. Nature Biotech, 2019
Amodio, et al. Exploring single-cell data with deep multitasking neural networks. Nature Methods, 2019
Burkhardt, et al. Enhancing experimental signals in single-cell rna-sequencing data using graph signal processing. bioRxiv:532846, 2019
van Dijk, et al. Finding archetypal spaces for data using neural networks. arXiv:1901.09078, 2019
Gigante, et al. Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks
van Dijk, et al. Recovering gene interactions from single-cell data using data diffusion. Cell, 2018
Amodio, et al. Out-of-sample extrapolation with neuron editing. arXiv preprint arXiv:1805.12198, 2018
Chen, et al. Embedding the single-cell sample manifold to reveal insights into cancer pathogenesis and disease heterogeneity. bioRxiv, 2018 [In press at Nature Methods]
van Dijk, et al. Large-scale mapping of gene regulatory logic reveals context-dependent repression by transcriptional activators. Genome research, 27(1):87–94, 2017
Moon, et al. Manifold learning- based methods for analyzing single-cell rna-sequencing data. Current Opinion in Systems Biology, 2017
Lowther, et al. Pd-1 marks dysfunctional regulatory t cells in malignant gliomas. JCI insight, 1(5), 2016
van Dijk, et al. Slow- growing cells within isogenic populations have increased rna polymerase error rates and dna damage. Nature communications, 6:7972, 2015
Keren, et al. Noise in gene expression is coupled to growth rate. Genome research, pages gr–191635, 2015
Sharon, et al. Probing the effect of promoters on noise in gene expression using thousands of designed sequences. Genome research, pages gr–168773, 2014
van Dijk, et al. Publication metrics and success on the academic job market. Current Biology, 24(11):R516–R517, 2014
Carey, et al. Promoter sequence determines the relationship between expression level and noise. PLoS biology, 11(4):e1001528, 2013
Dadiani, et al. Two dna-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise. Genome Research, 2013
van Dijk, et al. Identifying potential survival strategies of hiv-1 through virus-host protein interaction networks. BMC systems biology, 4(1):96, 2010
van Dijk, et al. Individual-based simulation of sexual selection: A quantitative genetic approach. In ICCS, number 1, pages 2003–2011, 2010
Jaeger, et al. Inference of surface membrane factors of hiv-1 infection through functional interaction networks. PLoS One, 5(10):e13139, 2010