Szalai, Bence

Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data

Dimitrov, D., Türei, D., Garrido-Rodriguez, M., Burmedi, P. L., Nagai, J. S., Boys, C., Ramirez Flores, R. O., Kim, H., Szalai, B., Costa, I. G., et al. (2022) · Nature Communications

Single-cell and spatial transcriptomicsBioinformatics and Genomic Networks

Cited by 664

Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data

Holland, C. H., Tanevski, J., Perales-Patón, J., Gleixner, J., Kumar, M. P., Mereu, E., Joughin, B. A., Stegle, O., Lauffenburger, D. A., Heyn, H., et al. (2020) · Genome Biology

Single-cell and spatial transcriptomicsBioinformatics and Genomic Networks

Cited by 323

A community challenge for a pancancer drug mechanism of action inference from perturbational profile data

Douglass, E. F., Allaway, R. J., Szalai, B., Wang, W., Tian, T., Fernández-Torras, A., Realubit, R., Karan, C., Zheng, S., Pessia, A., et al. (2022) · Cell Reports Medicine

Computational Drug Discovery MethodsGene expression and cancer classification

Cited by 68

Benchmarking foundation cell models for post-perturbation RNA-seq prediction

Csendes, G., Sanz, G., Szalay, K. Z., & Szalai, B. (2025) · BMC Genomics

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 47

Why do pathway methods work better than they should?

Szalai, B., & Saez‐Rodriguez, J. (2020) · FEBS Letters

Bioinformatics and Genomic NetworksGene expression and cancer classification

Cited by 34

The EFFECT benchmark suite: measuring cancer sensitivity prediction performance - without the bias

Szalai, B., Gáspár, I., Kaszás, V., Mérő, L., Sztilkovics, M., & Szalay, K. Z. (2023) · bioRxiv (Cold Spring Harbor Laboratory)

Computational Drug Discovery MethodsCancer Genomics and Diagnostics

Cited by 5