Lisa Fish

The paper introduces ecCLIP for high-resolution RBP binding maps and Pythia for structure-aware RNA-protein interaction modeling, improving RNA half-life prediction and variant effect assessment.

The paper identifies a metastasis-suppressive regulatory axis involving RBMS3 and TXNIP by analyzing mRNA stability in breast cancer using a deep-learning framework.

The paper systematically identifies and characterizes cancer-emergent oncRNAs across 32 tumor types, showing they act as blood-accessible molecular barcodes for cancer identity and prognosis.

Exai-1 is a transformer-based model that integrates RNA sequence and expression data to improve cfRNA analysis for disease detection.

The paper identifies 245 putative RNA structural switches in the human transcriptome using a computational and experimental approach, validates one with DMS-MaPseq and cryo-EM, and shows its regulation by trans factors and nonsense-mediated decay.

This study introduces ExoGRU, a machine learning model that predicts small RNA secretion from sequence, identifies key RNA-binding proteins, and develops exoCLIP to map RNA interactomes in extracellular vesicles.

The paper shows oxidative stress causes tyrosine tRNA fragmentation, leading to translational repression and growth suppression through depletion of the precursor tRNA and inhibition of transcript destabilization.

The paper identifies RBMS1 as a suppressor of colon cancer metastasis by stabilizing its mRNA targets, showing its clinical relevance in patient survival.

The paper identifies TARBP2 as a nuclear RNA-binding protein that regulates transcript stability through m6A methylation, splicing inhibition, and exosome-mediated degradation, with implications for lung cancer progression.