Padma Sheila Rajagopal

Optimizing transcriptome-based synthetic lethality predictions to improve targeted therapy / immunotherapy treatment prioritization in non-metastatic breast cancer: BC-SELECT

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.

AI-Driven Spatial Transcriptomics Unlocks Large-Scale Breast Cancer Biomarker Discovery from Histopathology

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

Non-coding genetic variants underlying higher prostate cancer risk in men of African ancestry

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

Enhanced prediction of breast cancer patient response to chemotherapy by integrating deconvolved expression patterns of immune, stromal and tumor cells

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.

A machine learning framework for supervised treatment response prediction from tumor transcriptomics: A large-scale pan-cancer study

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.

Single-cell-guided identification of logic-gated antigen combinations for designing effective and safe CAR therapy

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.

Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in Triple Negative Breast Cancer

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.

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

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

Whole-genome analysis of Nigerian patients with breast cancer reveals ethnic-driven somatic evolution and distinct genomic subtypes

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

Pleiotropy-guided transcriptome imputation from normal and tumor tissues identifies new candidate susceptibility genes for breast and ovarian cancer

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

PhenomeXcan: Mapping the genome to the phenome through the transcriptome

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