Methods for analysis of digital data

Abstract

Methods for producing an enriched reference data map useful for identifying critical factors for the development of a condition of interest are disclosed. The reference data map may be used to assessthe risk or likelihood of a condition of interest being realized. In the context of medicine or genetics, the methods of the invention may be used to produce a risk assessment roadmap useful for identifying elements (biomolecular constructs, biological interactions, and biological pathways) that are critical to the development of a particular disease or syndrome. The roadmap may be consulted to design treatment methods having the greatest likelihood of successfully treating or preventing the development of a disease or syndrome. Also disclosed are methods for using such a risk assessment roadmap to evaluate a specific configuration of elements for determining the changes in the configuration of elements that will result in the achievement or the avoidance of a defined condition of interest. In the context of medicine or genetics, the invention provides methods for determining the susceptibility of an individual or group of individuals to develop a particular disease or syndrome utilizing biological data of the individual or group and assessing the level of risk by referencing a risk assessment roadmap prepared according to the invention herein. Uncertainty in diagnosis is minimizedor eliminated by these methods, and the targets, interactions, and pathways most likely to be critical for disease development, and so representing the best intervention points for treatment or prevention of the disease or syndrome, are identified.

Description

[0001] priority statement [0002] This application claims the benefit of priority to US Provisional Application No. 62 / 319,403, filed April 7, 2016, the contents of which are hereby incorporated by reference in their entirety. technical field [0003] The present invention generally relates to biomolecular interaction analysis, mass data collection, and mass data integration. In particular, the present invention relates to the utilization of extremely large data sets (sometimes referred to as "big data" and exemplified by "omics" data, i.e., genomics, proteomics, metabolomics, pharmacology, etc.) Improved methods to identify and rank biomolecular interactions, targets, and pathways that have the highest likelihood of controlling or determining the occurrence of a particular disease or syndrome. By integrating these large amounts of data to determine the relevance of biomolecular interactions, targets or pathways to specific diseases or syndromes, a strengthened reference dat...

AI Technical Summary

Problems solved by technology

This enables analysis to pinpoint causative factors in disease to a level of complexity that cannot be analyzed systematically and is dependent on trial and error, or applied to a single feature hypothesis-driven study of particular experimental focus

Limitations of computational analysis of genetic material are often encountered when introns and exons are subjected to computational analysis

Often, there are more introns than exons in a given DNA sequence, thus limiting the necessary pairwise comparisons in computer processing because the data are "unbalanced"

Current state-of-the-art analyzes of genetic material do not account for global or composite considerations resulting from these pairwise constraints

[0011] Assessing the risk of developing disease by detecting only a single or a limited number of genetic polymorphic variants may result in unnecessary treatments that prove ineffective because they address unrelated symptoms rather than the true cause of the disease, or with Treatment blinded to targets for effective therapeutic intervention that are missed or undetected by subsequent diagnostic assessment

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