Sridhar S. Hannenhalli

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.

A fully connected neural network trained on 9491 TCGA bulk tumor transcriptomes estimates activity of 645 cytokines and secreted proteins, checked against perturbation and protein data.

STITCH pairs size-independent tangent-PCA contour shape modes with expression factors by canonical correlation, recovering keratinocyte, fibroblast and melanoma shape-expression links in Xenium and CosMx data.

The paper identifies genotype-methylation interactions that reconfigure transcriptional regulation in colorectal cancer, showing their link to disease progression.

This study identifies drug-specific resistance mechanisms in ER+ breast cancer by analyzing multi-omic data, revealing distinct transcriptional and chromatin states associated with SERM, SERD, and ESR1 mutant resistance.

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 paper develops a machine learning framework, EXPRESSO, to predict treatment response from tumor transcriptomics across multiple cancer types and therapies, showing improved performance over existing methods.

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.

The paper maps promoter methylation and gene expression changes in colorectal cancer, showing that Polycomb-associated promoters resist methylation-driven repression, particularly for transcription factor genes.

This study develops mouse models of brain metastatic melanoma to identify immune and molecular factors influencing response to immunotherapy.

The paper infers tumor immune status and predicts immunotherapy response from blood scRNA-Seq data in HNSCC patients.

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.

The paper predicts gene expression changes after HDACi treatment using machine learning with pre-treatment transcriptome and epigenomic data, achieving high accuracy in distinguishing up- and downregulated genes.

The paper identifies two groups of transcription factors that show intra-group attraction and inter-group repulsion in both genomic and spatial contexts across human cell lines.

The paper identifies ~4000 de novo human brain enhancers gained through single-nucleotide mutations, linked to neural development and CNS disorders.

miRSCAPE infers miRNA expression from RNA-seq data, showing high accuracy in bulk and single-cell datasets across multiple tissues and cell types.

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.

This study predicts tissue-specific gene expression from whole blood transcriptome using GTEx data, finding that blood expression can predict expression in many tissues and improve disease prediction.

The paper introduces SNiPage, a framework to identify SNP-age interactions in transcriptional control, finding 637 TF-SNP-Gene triplets across 25 tissues linked to aging and age-related diseases.

PathExt identifies differentially active paths in biological networks to prioritize context-relevant genes, even with single samples or no control.

The paper shows that unliganded ERα pre-marks enhancer sites, which form 3D clusters upon ligand exposure, driving transient gene expression.

This study compares proliferation and migration transcriptomic signatures in breast cancer, finding migration is more strongly linked to patient survival.

The paper identifies 71,946 genetic interactions in cancer using a novel method, revealing non-synthetic-lethal interactions that explain driver gene specificity and drug response.

ChIPulate evaluates how biological and experimental factors affect ChIP-seq outcomes, finding that replicate numbers and extraction efficiency significantly impact binding strength inference.

This paper presents the first draft genome assembly and annotation of the red raspberry Rubus idaeus, providing a valuable resource for research and breeding.

The paper addresses compositional bias in sparse sequencing count data by proposing an empirical Bayes normalization approach and clarifying assumptions of existing scaling methods.

The paper identifies PPP1R3A as a central cardioprotective factor in pressure overload heart failure by analyzing gene network rewiring in human hearts.