Arashdeep Singh

The paper identifies transient isoform remodeling as a key mechanism in early MAPKi resistance in melanoma, showing it is distinct from gene expression changes and linked to splicing regulators and developmental pathways.

The paper develops CSI-TME to identify prognostic cell state interactions in IDH-mutant gliomas, revealing a pro-tumor network associated with immune checkpoint therapy response.

The paper identifies non-coding SNPs associated with prostate cancer risk in men of African ancestry using a deep learning model and experimental validation.

This study identifies early transcriptional signatures of drug resistance in cancer and across species, showing conservation and predictive value for treatment response.

This paper introduces TIME_ACT, a transcriptomic signature predicting immune checkpoint blockade response across cancers, validated using histopathology and multiple cohorts.

This study identifies exosomal miRNAs that influence the pre-metastatic niche across multiple cancers, showing their association with survival and chemotherapy response.

This study applies a network-based method to identify key genes in BRCA subtypes and chemotherapy response in TNBC, revealing subtype-specific mediators and potential therapeutic targets.

This paper introduces FUGUE, a machine learning model that predicts tissue-relevant genes across 30 human tissues using transcriptional and network features, outperforming conventional metrics and providing prioritized gene lists.

The paper identifies a transcriptionally distinct subpopulation of healthy acinar cells with features of pancreatic progenitors and PDAC, linked to poor patient survival and age-related changes.