Padma Sheila Rajagopal

BC-SELECT predicts treatment response in non-metastatic breast cancer using genetic interactions and gene expression data, showing significant accuracy for PARP inhibitors and immunotherapy.

Path2Space predicts spatial gene expression and tumor microenvironment from histopathology slides, identifying new breast cancer subgroups and improving biomarker prediction.

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 DECODEM to predict chemotherapy response by integrating deconvolved expression patterns of immune, stromal, and tumor cells in breast cancer, finding that cell-type-specific expression is predictive of response.

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 paper introduces LogiCAR designer, a computational method using single-cell transcriptomics to identify logic-gated antigen combinations for CAR therapy, showing improved efficacy and safety compared to existing approaches.

The paper compares gene expression in primary and metastatic tumors to their origin and target tissues, finding metastases more resemble target tissues and show reduced cancer hallmark activity.

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.

ENLIGHT predicts patient response to targeted and immunotherapies using tumor transcriptomes without prior treatment response data, showing better accuracy than existing transcriptomics-based signatures.

The paper analyzes whole-genome data from Nigerian breast cancer patients, identifying ethnic-driven somatic evolution and distinct genomic subtypes associated with African ancestry.

This study improves transcriptome-wide association studies by integrating pleiotropy-guided methods with GWAS data to identify new cancer susceptibility genes.

PhenomeXcan integrates GWAS and transcriptomic data to identify gene-trait associations, providing a resource for translational research with biological context.