Md. Zubbair Malik

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 study generates and characterizes single-cell-derived spheroids from head and neck cancer cells to reveal functional and genetic heterogeneity.

This study identifies ARIZ-047, an siRNA targeting PRDM2/RIZ2, as a therapeutic candidate that inhibits lung cancer progression by increasing RIZ1 expression and suppressing Wnt signaling.

This study compares salivary and plaque microbiomes in children with and without type 1 diabetes, finding saliva provides better classification accuracy for detecting T1D.

This study identifies CVD-related genes and conserved pathways in C. elegans using bioinformatics, supporting its use as a model for cardiovascular disease research.

This study uses Hi-C to compare genome organization between virulent and avirulent Mycobacterium tuberculosis strains, finding differences in Chromosome Interacting Domain (CID) size and gene distribution.

The paper identifies CDK1 and HSP90AA1 as key regulatory genes in NSCLC using network analysis of microarray data.

The paper investigates how time delays influence stochastic processes in biological systems, showing their role in inducing oscillations, switching states, and creating bistable states.

This study identifies 15 antigenic peptides from SARS-CoV-2 surface glycoprotein using vaccine-informatics, with 12 showing high similarity to SARS-CoV epitopes and potential for T-cell activation.

This study identifies bioactive compounds from three medicinal plants that inhibit ACE activity, with hibiscitrin showing the highest binding affinity for the enzyme.

The paper analyzes SARS-CoV-2 genome data using multifractal methods to study mutation patterns and host-virus interactions, finding high mutational rates and complex genomic organization.

The paper explains population-specific differences in COVID19 outcomes by analyzing ACE2 expression, homo-dimerization, and interactions with host proteins and genetic variants.

The paper identifies key regulators in prostate cancer by analyzing gene expression data and constructing a network of interactome genes.

The paper analyzes brain network energy distribution using a Hamiltonian function and identifies phase transitions linked to brain states through a resolution parameter.