We build a cellular regulatory network with the relations identifiable among the genes from such as signaling and metabolic pathways, TF-targets or epigenetic regulation targets, E3 ligase-targets, kinase/phosphatase-targets or other protein modification targets, protein interactions, and scaffold-targets. We also build a functional module set with the gene set correlated together with any level of function in a cell from such as the gene sets in each GO term, co-expressed or co-identified in any specific cell function or subcellular location regardless of the presence of identifiable relations between the genes. 

  The disease (or any functional) significances of each functional module and the genes in each module are introduced by the enrichment of pre-defined disease (or functional) genes. The enriched significance values are propagated into the genes connected in the cellular regulatory network.

  These processes are integrated in a platform of “Disease Function Enriched & Networked Scoring System for Genes: DFENS-Gene”. This system provides the expected significance of each gene to the given function or disease reflecting their regulatory relation and functional association, which enables us to select the candidates of disease or function associated markers.

  DFENS system is expanding to DFENS-X depending on the contents of the regulatory relations or associated function sets, and especially on the target function or disease types. Currently, this system is evolving to handle the significance of modules rather than individual genes (DFENS-Module).

  We pursue to identify the best minimum sets of markers or regulators that can represent or regulate the activities of mechanisms and functional modules in specific diseases or patient groups with both gene-wise and module-wise DFENS systems by obtaining the feedbacks from various disease specific omics data.

  Various artificial intelligence methods can be applied on the DFENS system to infer the most possible target genes, functional modules, disease mechanisms, and disease types of individual or associated compounds and regulator genes, or reversely to infer those drugs or genes promoting the responses

  To do this, we collect all known information related to the candidate compounds and their cellular or clinical responses regarding the effects on the target genes, mechanisms, and disease types represented in the various types of experimental or clinical outputs. The input features can be structural signatures of intact compounds or their fragments like 2D fingerprints, 3D pharmacophores and reverse pharmacophores against the target structures. The features can be obtained from the signatures in omics, cellular functional of phenotypic responses, and clinical effects including the side effects.

  The mechanisms of a drug can be estimated directly with DFENS by evaluating the effect on mechanisms or diseases from the DFENS scores of involved genes or modules. Inversely, the target of drug can be predicted in this systems by tracing back the genes that can generate the most similar patterns of drug response gene or modular signatures.

  We analyze the mutation and expression patterns of thousands of cell lines to identify the disease mechanism specificities in each cells, which provide us to develop the cellular surrogate systems for the in vitro validation of the disease marker and therapeutic drug candidates for the patients with the same specificities. The omics data of the available patient populations and the cell lines are analyzed and clustered by the predicted marker sets and their representing functional modules and mechanisms.

 The cell lines to be tested are selected heterogeneously from the clusters having different mechanisms signatures. The cell lines are tested if the specified mechanisms and functional modules of the predicted marker sets and cell lines are correlated well with the activities of corresponding cellular functions and molecular mechanisms assayed in the selected cell lines.

  Our comprehensive and synergistic methods delineating the cellular mechanisms from the molecular structural details to the regulatory networks of cellular functions will support to construct more reasonable solutions for the current problems in biomedical fields that cannot be solved by individual method or skill sets.

  The validated compounds or regulator genes can be used to manipulate cellular functions. Those agents can be engineered further for the better and more specific performance by changing their molecular structures or gene structures based on their possible implications obtained from our methods in the structure, modification, interactions, or functional associations

  Our primary engineering targets are the multi-functional and multi-targeting compounds or proteins that improve the detection or manipulation of the cellular functions, pathways or regulation circuitry with high fidelity and flexibility. The results will provide more refined solutions for the diagnosis or therapy and also in the other fundamental uses in biotechnology.