Parimal Kumar Das

This study evaluates the diagnostic potential of urinary exosomal let-7c cluster miRNAs in bladder cancer using machine learning models, finding significant differentiation between cancer and healthy controls with high accuracy.

This study uses whole exome sequencing and multi-omics integration to identify genetic variants associated with congenital tooth agenesis, revealing potential biomarkers and therapeutic targets.

The paper establishes and validates an iPSC line from an Eastern Indian donor, providing a population-specific control resource for disease modelling.

The paper describes the generation and characterization of a stable human iPSC line from peripheral blood to serve as a control resource for disease modeling and regenerative medicine.

This study uses machine learning to analyze miRNA expression in blood samples to improve prostate cancer diagnosis, achieving 77.42% accuracy and reducing reliance on fixed cutoff values.

This study examines the structural and dynamic effects of PKD1 missense variants on RNA and protein levels using computational methods to understand their role in ADPKD pathogenesis.

gQuant improves normalizer gene identification in qRT-PCR data by using ensemble voting classifiers, showing better consistency and accuracy than existing tools.

The paper identifies G-quadruplex forming sequences in the PRCC-TFE3 fusion gene and demonstrates their stabilization by pyridostatin, suggesting potential as a therapeutic target for tRCC.

The paper identifies a novel PAX9 mutation causing non-syndromic tooth agenesis through in vitro and computational analysis.

This paper uses computational methods to analyze how the Omicron variant's mutations affect its binding to ACE2 and antibodies compared to Delta and wild-type SARS-CoV-2.

The paper analyzes SARS-CoV-2 RBD variants using molecular dynamics to assess their impact on vaccine efficacy and ACE2/antibody binding.