Method for statistical analysis and predictive modeling of state transition diagrams
A technology of state transition diagrams and statistical models, applied in laboratory analysis data, medical simulation, drugs or prescriptions, etc., can solve the problem of not reflecting the rich diversity of sequences, misalignment, clinical relevance and inaccuracy of highly variable regions, etc. question
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example 1
[0087] Create a transition graph
[0088] State transition maps are needed in cancer centers to evaluate their most and least effective treatment lines for existing and future HCC patients. The center expects to build a comprehensive graph of all patients and split the graph into subgraphs according to various stages of the disease.
[0089] When building a graph, the following events are designated as transitions by the clinician:
[0090] i) Transarterial chemoembolization (TACE);
[0091] ii) TACE with drug-eluting beads (DEB-TACE);
[0092] iii) Targeted systemic chemotherapy (sorafenib, sunitinib, linifanib, brivanib, c-Met inhibitor (tivantinib), Moss);
[0093] iv) Combination treatment of chemotherapy and TACE (sorafenib + TACE);
[0094] v) radioembolization;
[0095]vi) Combined chemotherapy and radioembolization (sorafenib + radioembolization);
[0096] vii) percutaneous ethanol injection (PEI);
[0097] viii) cryoablation;
[0098] ix) radiofrequency ablat...
example 2
[0109] Subgraph selection for downstream analysis
[0110] Using the state transition diagram formed in Example 1, a cancer center wants to evaluate the outcome and follow-up of patients awaiting liver transplantation. In recent years, wait times for liver transplantation (LT) have increased, leading to withdrawal of patients due to tumor progression, so downstaging, followed by a minimum observation period, is standard practice to keep patients on the waiting list (i.e. the Milan guidelines must be met). Instead of analyzing the entire map, criteria should be applied to limit the analysis to a selected subset of patients.
[0111] Figure 4 A disposition map of HCC patients meeting the Milan criteria for liver transplantation is shown. Three subsets of patients were first treated with PEI, TACE and RFA. Patients treated with TACE and RFA maintained the Milan criteria; however, patients treated with PEI experienced tumor progression and were no longer eligible. Among those...
example 3
[0113] Haplotype detection using genome maps
[0114] In this example, a clinician is trying to assess whether a patient is at risk for developing type 1 diabetes. Although the exact cause of the disease is unknown, certain variants in several human leukocyte antigen (HLA) genes are known to increase the risk of developing it later in life. Not any one variant in particular, but certain combinations or haplotypes that are risk indicators for the eventual onset of the disease. The HLA region is unique in that it is highly variable even in healthy populations, resulting in complex and largely unknown haplotypes.
[0115] From a pre-constructed and subsetted (for HLA regions) genomic variation map, clinicians first select a submap containing sample cohorts representing patients with diagnosed and undiagnosed type 1 diabetes, with the goal of identifying Closely matching haplotype(s). Subsequently, the clinician chose to restrict the analysis to the HLA region of chromosome 6, ...
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