A novel essential protein identification method based on PPI networks and gene expression data

Abstract Background Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify proteins. However, gene prone fluctuations, which may affect the accuracy of protein identification. Therefore, we propose an identification method based on and PPI network calculate similarity "active" "inactive" state in a cluster network. Our experiments show that can improve predicting Results In this paper, new measure named JDC, data. The JDC offers dynamic threshold binarize After that, it combines degree centrality Jaccard index score each We benchmark four organisms respectively, evaluate our ROC analysis, modular jackknife overlapping top analysis. performance than DC, IC, EC, SC, BC, CC, NC, PeC, WDC. compare with both NF-PIN TS-PIN methods, predict through active networks constructed from expression. Conclusions demonstrate measure, more efficient state-of-the-art prediction same input. main ideas behind are as follows: (1) Essential densely connected clusters (2) Binarizing screen out fluctuations profiles. (3) essentiality depends