Method of identifying cancer drive pathway
A cancer and pathway technology, applied in instrumentation, genomics, proteomics, etc., can solve problems such as low efficiency
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Embodiment 1
[0048]A method for identifying cancer-driven pathways includes the following steps:
[0049]1) Construct a weighted non-binary mutation matrix:
[0050]Existing glioblastoma GBM somatic mutation matrixCopy number variation matrixAnd gene expression matrixMedium mutation matrixCopy number variation matrixAnd gene expression matrixThe middle row represents the same sample set p of a cancer, and the columns represent the gene set GS, GCAnd GE, In the matrixMiddle, sij ∈{0,1}(i=1,2,...,|p|,j=1,2,...,|GS|), j gene mutation in i sample, sij The value is 1, otherwise the value is 0; matrixEach element in cij ∈{-2,-1,0,1,2}(i=1,2,...,|p|,j=1,2,...,|GC|), represents the copy number variation value of j gene in i sample; in the matrixMiddle eij ∈R(i=1, 2,..., |p|, j=1, 2,..., |GE|), represents the expression of j gene in i sample; let the matrixGene set GA=GS∪GC, The sample set is p, letaij ∈{0,1}(i=1,2,...,|p|,j=1,2,...,|GA|), whereIs the mutation matrix, when sij The value is 1 or when the j gene...
Embodiment 2
[0079]Set λ in step 1) of this example1= 3 and λ2=7, construct a weighted non-binary mutation matrix A|p|×|G| , Where |p|=90, |G|=920;
[0080]Step 7 of this example) Enter the weighted non-binary mutation matrix A|p|×|G| , Where |p|=90, |G|=920, the model in formula (2), the drive path size k=10, CGA-MWS related parameters, population size P=460, mutation probability Pm=0.3, the maximum evolutionary algebra maxstep=1000, the optimal value is kept constant threshold maxt=10;
[0081]In this example, step 9) get the drive path with size k=10, the operation diagram is as followsfigure 2 Shown.
[0082]The remaining steps are the same as in Example 1.
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