Abstract: A method to select a protein target for therapeutic application includes accessing genomic information and protein-protein interaction (PPI) data, computing a thermodynamic measure for each protein node within the network of protein nodes, generating an energy landscape data corresponding to the network of protein nodes and the thermodynamic measure, generating a PPI subnetwork by applying a topological filtration to the energy landscape data of the PPI data, computing a first Betti number for the PPI subnetwork, sequentially removing a protein node(s) from the PPI subnetwork while replacing the previously removed nodes(s), computing a new Betti number for the PPI subnetwork with the protein node(s) removed, computing a change between the Betti numbers, and determining, based on the change between the Beti numbers, a most significant protein target within the PPI network.

Abstract: A method for selecting a molecular target for therapeutic application involves accessing omic information and protein-protein interaction (PPI) data including a network of protein nodes. The method further involves computing a Gibbs free energy for each protein node within the network of protein nodes using the omic information and the PPI data, interpreting information for one or more products of gene fusion from the omic information as one or more gene fusion protein probabilities, and converting the one or more gene fusion protein probabilities into one or more gene fusion protein networks based on a Fermi distribution. The method also involves taking a union of the network of protein nodes with the one or more gene fusion protein networks and generating an energy landscape corresponding to the union of the network of protein nodes with the one or more gene fusion protein networks, and the Gibbs free energy.

Abstract: A method for therapeutic application involves accessing information associated with a patient and a reference biological network database, generating, using the information associated with the patient and the reference biological network database, a disease model, identifying, from the disease model, a molecular target, identifying, from the molecular target, a drug for the patient, generating, based on the drug for the patient, a treatment plan for the patient, and repetitively generating, based on repetitively inputting a patient outcome from the treatment plan into a feedback loop mechanism, a different treatment plan for the patient based on either the molecular target or a different molecular target.

Abstract: A method to select a protein target for therapeutic application includes accessing genomic information and protein-protein interaction (PPI) data, computing a thermodynamic measure for each protein node within the network of protein nodes, generating an energy landscape data corresponding to the network of protein nodes and the thermodynamic measure, generating a PPI subnetwork by applying a topological filtration to the energy landscape data of the PPI data, computing a first Betti number for the PPI subnetwork, sequentially removing a protein node(s) from the PPI subnetwork while replacing the previously removed node(s), computing a new Betti number for the PPI subnetwork with the protein node(s) removed, computing a change between the Betti numbers, and determining, based on the change between the Beti numbers, a most significant protein target within the PPI subnetwork.