Inter Disease Relations Based on Human Biomarkers by Network Analysis A biomarker (short for biological marker) is a medical sign of a disease or condition which indicates a normal or abnormal state of a body. The biomarker is a key factor in the analysis of diseases and also for analyzing inter disease relations. In the previous study, we designed and developed a human biomarker (metabolites and proteins) database and the database is currently available online. This work was supported by the Ministry of Education, Japan and NAIST Big Data Project. We have used our previously developed database and collected 486 human biomarkers and their respective diseases. We determined the similarity among NCBI disease classes based on associated biomarker fingerprints. For this purpose, we collected biomarker PubChem IDs and using them downloaded the SDF files in a batch, then with those molecular description files determined their atom pair fingerprints using ChemmineR package. We constructed a network of biomarkers based on Tanimoto similarity between their fingerprints and applied DPclusO algorithm to find clusters consisting of biomarkers with similar chemical structures. We also conducted hierarchical clustering of the biomarkers. We categorized all the diseases in our data into 18 NCBI disease classes. Combining all information, we finally determined inter disease relations based on structural similarity between biomarkers.

DualRank: multiplex networkbased dual ranking for heterogeneous complex disease analysis Analysis of heterogeneous complex diseases based on the expression of biomarkers has always been the focus of medical research. In the past studies, the powerful network propagation has been applied in finding marker genes related to specific diseases. However, the network propagation model largely depends on the reliability and integrity of the network data, current networks may cause some problems due to the incompleteness of the networks. In this study, we developed a multiplex networkbased dual ranking framework (DualRank) for heterogeneous complex disease analysis. We applied the proposed method to heterogeneous complex diseases for disease diagnosis, cancer prognosis, and similar disease classification. The results showed that DualRank outperformed current methods and could identify biomarkers with small quantity, strong prediction accuracy and biological interpretability.