Pankaj Yadav

This study investigates the genetic and structural impact of NLRP1 FIIND-domain variants in an Indian glioma cohort, finding associations between specific SNPs and glioma risk.

The paper catalogs mobile element insertions in 82 Indian populations, revealing population-specific variants not present in European databases and highlighting their potential impact on disease associations.

The paper presents a comprehensive genomic atlas of Indian populations, identifying novel variants and highlighting genetic diversity and pharmacogenomic implications.

This study compares nano urea and bulk urea effects on Arabidopsis, finding nano urea increases biomass, chlorophyll, and nitrogen through differential gene regulation.

This study shows that nano urea enhances biomass and chlorophyll content in Arabidopsis thaliana hydroponics compared to bulk urea, with differential gene expression patterns.

This study identifies AP2/ERF transcription factors as key to drought tolerance in Prosopis cineraria, showing higher copy numbers and DNA-binding affinity compared to drought-sensitive species.

The paper identifies shared differentially expressed genes between type 2 diabetes and three cancers, and finds hub genes that may predict cancer risk in diabetic patients.

This paper uses machine learning on lung microbiome data to identify biomarkers that can distinguish between lung adenocarcinoma and squamous cell carcinoma with moderate accuracy.

This study develops a deep learning model to identify disease-resistance proteins in rice and related species, showing superior performance compared to traditional machine learning methods.

SpatialPrompt improves spot deconvolution and domain identification in spatial transcriptomics by integrating spatial coordinates and scRNA-seq data, showing scalability and accuracy across multiple datasets.

The paper uses scRNA-seq data to identify molecular markers and cell types associated with liver cancer subtypes HCC and ICC.

The paper identifies five novel disease-resistance proteins in rice that contribute to resistance against bacterial leaf blight and rice blast diseases through in silico analysis.

The paper benchmarks DR and clustering methods on scRNA-seq datasets, finding dataset-dependent performance and no single best method.