Shahul Alam

SpiderNet identifies cell-cell communication programs from spatial transcriptomics data, revealing key interactions and their associations with phenotypes and therapeutic responses.

The paper introduces SACCELERATOR, a framework for spatially aware clustering that improves benchmarking by incorporating expert feedback and consensus analysis across diverse datasets.

This paper introduces HO_SCPLOWEIMDALLC_SCPLOW, a framework to analyze tokenizer design in scFMs, showing that specific tokenization choices improve generalization under distribution shift.

This paper introduces a probabilistic model for analyzing multisample spatial transcriptomics data, identifying spatial metagenes and interactions across tissues.

The paper introduces a new method for analyzing spatial transcriptome data to improve cell type inference and spatial pattern analysis.

The paper introduces CISI, a method that uses compressed sensing to efficiently recover spatial gene expression data from composite measurements, achieving high efficiency and scalability.