Abstract: The present disclosure relates to systems and methods for bioinformatics and data processing. In particular, in a first aspect, the present disclosure relates to methods and systems for generating a personalized treatment guideline for a patient and for selecting a treatment for a patient. In another aspect, the present disclosure relates to methods and systems for selecting patients for a clinical trial of a treatment. The invention resolves cases in which patients have more than one “actionable” aberration by combining the patient-specific molecular information and the treatment-specific molecular information further with a clinico-molecular disease model, specifically a scoring of genes and/or proteins that represents several aspects of their involvement into the disease. In this way, treatments and patients can be prioritized that are most likely to impact or impacted by the disease mechanism, respectively.

Abstract: The present disclosure describes systems and methods for using patient-specific genomic information to optimize or de-risk therapy for the patient. A user may identify a medication for consideration for prescription to a patient, and a genetic variant of the patient affecting a first protein. An analyzer may identify a second medication targeting the first protein, and may retrieve adverse event data from an adverse event database for patients co-medicated with both the first medication and second medication. The analyzer may determine, based on rates of adverse events, the likelihood of an adverse event occurring through co-medication of the first medication and second medication. Based on the likelihood, and based on a correspondence or non-correspondence between a protein activation characteristic of the first medication and the effect of the genetic variant of the patient, the analyzer may indicate or contra-indicate the first medication for the patient.

Abstract: The present disclosure is directed to systems and methods for identifying unknown drug targets via adverse event data. An analyzer receives an identification of a first drug having one or more unknown target proteins and identifies a second drug related to the first drug. The analyzer retrieves, from an adverse event database, a first side effect profile associated with the first drug, and a second side effect profile associated with the second drug. The analyzer generates a third side effect profile comprising a subset of the first side effect profile not shared by the second side effect profile, and identifies a third drug having a fourth side effect profile comprising the third side effect profile. The analyzer retrieves a list of one or more target proteins of the third drug not targeted by the second drug, and presents the retrieved list as potential target proteins of the first drug.

Abstract: The present disclosure describes systems and methods for predicting a likely side effect profile for even new, untested medications. A predicted side effect profile may be generated based on intersections of side effect profiles of other medications that affect the same or related molecular entities, such as the nearby target proteins, involve the same pathways, or are otherwise similarly related. To generate a predicted side effect profile for a new drug targeting a novel or previously un-targeted protein target, an analyzer may query an adverse event database for records pertaining to patients who have taken drugs or combinations of drugs that target or affect molecular entities in the vicinity of the novel target within a global molecular entity graph, and, in some embodiments, may retrieve a plurality of adverse event records and generate an intersection of side effects associated with related targets to predict likely side effects for the novel target.

Abstract: The present disclosure is directed to systems and methods for identifying unknown drug targets via adverse event data. An analyzer receives an identification of a first drug having one or more unknown target proteins and identifies a second drug related to the first drug. The analyzer retrieves, from an adverse event database, a first side effect profile associated with the first drug, and a second side effect profile associated with the second drug. The analyzer generates a third side effect profile comprising a subset of the first side effect profile not shared by the second side effect profile, and identifies a third drug having a fourth side effect profile comprising the third side effect profile. The analyzer retrieves a list of one or more target proteins of the third drug not targeted by the second drug, and presents the retrieved list as potential target proteins of the first drug.

Abstract: The present disclosure relates to systems and methods for bioinformatics and data processing. In particular, in a first aspect, the present disclosure relates to methods and systems for generating a personalized treatment guideline for a patient and for selecting a treatment for a patient. In another aspect, the present disclosure relates to methods and systems for selecting patients for a clinical trial of a treatment. The invention resolves cases in which patients have more than one “actionable” aberration by combining the patient-specific molecular information and the treatment-specific molecular information further with a clinico-molecular disease model, specifically a scoring of genes and/or proteins that represents several aspects of their involvement into the disease. In this way, treatments and patients can be prioritized that are most likely to impact or impacted by the disease mechanism, respectively.

Abstract: The present disclosure is directed to systems and methods for identifying unknown drug targets via adverse event data. An analyzer receives an identification of a first drug having one or more unknown target proteins and identifies a second drug related to the first drug. The analyzer retrieves, from an adverse event database, a first side effect profile associated with the first drug, and a second side effect profile associated with the second drug. The analyzer generates a third side effect profile including a subset of the first side effect profile not shared by the second side effect profile, and identifies a third drug having a fourth side effect profile including the third side effect profile. The analyzer retrieves a list of one or more target proteins of the third drug not targeted by the second drug, and presents the retrieved list as potential target proteins of the first drug.

Abstract: Systems and methods are described herein for the prioritization of possible treatment options based on biomarkers, such as (but not limited to) tumor and germline-based genomic variants. The system and methods may thereby identify patient and the status of a biomarker in the treatment options tailored to a patient, in particular to his/her clinical, molecular, and/or genetic condition. Furthermore, the system and method provides a means for prioritizing the possible treatment options based on the extraction and contextualization of clinical and molecular knowledge. The system gathers and/or accesses biomarker information and transforms the information into prioritized, clinically actionable options identified for a specific patient case.

Abstract: The present disclosure describes systems and methods for multivarlate analysis of adverse event data. According to a first aspect, patient-specific genomic Information is used to optimize or de-risk therapy for the patient. According to other aspects of the invention, unknown drug targets are identified via adverse event data. According to still other espects, a medication is identified to exclude from use for an indication or from a clinical trial of another medication. According to another aspect, a predicted side effect profile is generated for a medication targeting a novel target. According to still another aspect, combination therapies are identified via adverse event data. According to another aspect, molecular interactions between a plurality of molecular entities are displayed in an intuitive format. According to still another aspect, molecular entities responsible for adverse event differences between similar indications are identified.

Abstract: Systems and methods are described herein for disease knowledge modeling and clinical treatment decision support. Disease or indication information, including identification of biomolecular entities associated with the indication may be culled through data mining to create a knowledge model of the indication. In some embodiments, the knowledge model may comprise a network of associations between molecular entities, including drug targets and biomarkers, genes, pathways. The model is used for prioritizing treatment decisions, for treatments comprising one or more medications associated with one or more molecular entities in the model. The priority of a suggested treatment depends on at least one property of one or more medications of the suggested treatment.

Abstract: Systems and methods are described herein for disease knowledge modeling and clinical treatment decision support, and the prioritization of possible treatment options based on tumor or other disease biomarkers. Disease or indication information, including identification of biomolecular entities associated with the indication may be culled through text data mining to create a knowledge model of the indication. In some embodiments, the knowledge model may comprise a network of associations between molecular entities, including such drug targets and biomakers, genes, pathways. The model may be combined with patient-specific variant information and historical treatment records to identify and prioritize treatment decisions and allow for the prediction of disease drivers and provide treatment options tailored to a patient's genetic data.

Abstract: The present disclosure is directed to systems and methods for identifying unknown drug targets via adverse event data. An analyzer receives an identification of a first drug having one or more unknown target proteins and identifies a second drug related to the first drug. The analyzer retrieves, from an adverse event database, a first side effect profile associated with the first drug, and a second side effect profile associated with the second drug. The analyzer generates a third side effect profile comprising a subset of the first side effect profile not shared by the second side effect profile, and identifies a third drug having a fourth side effect profile comprising the third side effect profile. The analyzer retrieves a list of one or more target proteins of the third drug not targeted by the second drug, and presents the retrieved list as potential target proteins of the first drug.

Abstract: The present disclosure describes systems and methods for using patient-specific genomic information to optimize or de-risk therapy for the patient. A user may identify a medication for consideration for prescription to a patient, and a genetic variant of the patient affecting a first protein. An analyzer may identify a second medication targeting the first protein, and may retrieve adverse event data from an adverse event database for patients co-medicated with both the first medication and second medication. The analyzer may determine, based on rates of adverse events, the likelihood of an adverse event occurring through co-medication of the first medication and second medication. Based on the likelihood, and based on a correspondence or non-correspondence between a protein activation characteristic of the first medication and the effect of the genetic variant of the patient, the analyzer may indicate or contra-indicate the first medication for the patient.

Abstract: The present disclosure describes systems and methods for de-risking patient treatment by identifying medications or combinations of medications to be contraindicated for a specific indication. An analyzer executed by a processor of a computing device from a user may receive an identification of an indication (e.g. the subject of a clinical trial, or the diagnosis of a patient visiting a physician's office). The analyzer may retrieve, from an adverse event database, medication and co-medication information of patients that experienced a side effect corresponding to the indication. The analyzer may sort the retrieved medication and co-medication information to generate an ordered list of medications consumed by patients that experienced the side effect, and identify a first medication of the ordered list. A display module executed by the computing device may display, to the user, the first medication of the ordered list for contraindication from the clinical trial.

Abstract: The present disclosure describes systems and methods for predicting a likely side effect profile for even new, untested medications. A predicted side effect profile may be generated based on intersections of side effect profiles of other medications that affect the same or related molecular entities, such as the nearby target proteins, involve the same pathways, or are otherwise similarly related. To generate a predicted side effect profile for a new drug targeting a novel or previously un-targeted protein target, an analyzer may query an adverse event database for records pertaining to patients who have taken drugs or combinations of drugs that target or affect molecular entities in the vicinity of the novel target within a global molecular entity graph, and, in some embodiments, may retrieve a plurality of adverse event records and generate an intersection of side effects associated with related targets to predict likely side effects for the novel target.

Abstract: A method for the joint analysis of molecular expression data and biological networks by clustering comprising the steps of