Thomson, Matt

Low Dimensionality in Gene Expression Data Enables the Accurate Extraction of Transcriptional Programs from Shallow Sequencing

Heimberg, G., Bhatnagar, R., El-Samad, H., & Thomson, M. (2016) · Cell Systems

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 156

D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response

Jiang, J., Chen, S., Tsou, T., McGinnis, C. S., Khazaei, T., Zhu, Q., Park, J. H., Strazhnik, I.-M., Vielmetter, J., Gong, Y., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory)

Gene Regulatory Network AnalysisSingle-cell and spatial transcriptomics

Cited by 21

Benchmarking protein language models for protein crystallization

Mall, R., Kaushik, R., Martinez, Z. A., Thomson, M. W., & Castiglione, F. (2025) · Scientific Reports

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 20

Benchmarking Protein Language Models for Protein Crystallization

Mall, R., Kaushik, R., Martinez, Z. A., Thomson, M., & Castiglione, F. (2024) · Scientific Reports

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 20

What's the Magic Word? A Control Theory of LLM Prompting

Bhargava, A., Witkowski, C., Looi, S.-Z., & Thomson, M. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 7