Dima L. Shepelyansky

The paper identifies fibroblast-specific protein-protein interactions driving myocardial fibrosis using the MetaCore network and proposes a method to reverse fibrotic cascades.

The paper analyzes the statistical properties of the MetaCore protein-protein interaction network using Google matrix methods to identify activation and inhibition patterns in specific protein groups.

The paper uses Google matrix analysis of MetaCore network to predict protein-protein interactions involved in cardiac fibrosis in mouse and human tissues.

The paper applies Google matrix analysis to the SIGNOR protein-protein interaction network to identify effective interactions and dominant activation/inhibition functions between proteins.

The paper analyzes Wikipedia's protein functional network using the REGOMAX algorithm to identify evolving hidden communities of biological functions.

The paper analyzes interactions between pharmaceutical companies, countries, cancers, and rare diseases using Wikipedia network data and the REGOMAX algorithm.

The paper uses Wikipedia's network to analyze cancer-drug-country interactions, finding strong agreement between drug-cancer links and medical prescriptions.

The paper introduces Googlomics, a method using Google matrices to infer hidden causal relations in biological networks and analyze changes during cancerogenesis.