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A minimum path pmin is a shortest path meaning there are no shorter p′ paths between x and y.
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x3: Minimum path length (minimum number of edges/links from one of the nine external inputs).
Optimum performance is reached with a minimum path setting of four genes.
At small minimum path lengths, the performance is relatively poor and the predictive variance is large.
Increasing the minimum path length not only increases accuracy, but also stability of the HME3M model.
Clearly, lengthening the minimum path length yields a pronounced improvement in 3M clustering results.
We now set the minimum path parameter to nine genes and analyze the clustering results.
The minimum path analysis for HME3M classifying insulin resistance is presented in Figure 4. From Figure 4, we observe a direct impact between path length and HME3M performance.
The information fusion procedure is based on the seven attributes described previously: in-degree, out-degree, minimum path length, average path length, bias, average sensitivity, and canalizing degree.
To find the optimum 3M model, we vary the minimum path length from 1 to 10 genes.
We now set the minimum path setting to four and extract the key pathway components for insulin resistance.
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