Naidu Subbarao

This study identifies key genes and pathways in triple-negative breast cancer using bioinformatics and experimental validation, finding six hub genes associated with tumor progression.

This paper generates TLR5-activating molecules using a systems-level approach combining transcriptome data and computational methods, validated experimentally.

This study investigates how mutations in the SARS-CoV-2 omicron variant's spike protein affect the binding affinity of RBD-targeting antibodies through computational analysis.

The paper identifies novel inhibitors of Plasmodium falciparum M17 Leucyl Aminopeptidase (PfM17LAP) through molecular docking and simulation using the ChEMBL antimalarial library.

This study improves the genome assembly of Pigeonpea by merging draft assemblies and increasing genome coverage, leading to better gene prediction and resistance trait analysis.

This paper uses a Random Forest classifier to predict JNK1 inhibitors that regulate autophagy in cancer by selecting and ranking molecular descriptors from a ChEMBL dataset.