Many other methods have also been used in functional genomics analysis. Only a short list is included here, with references to published biomedical works incorporating each technique, and using the hierarchy proposed at the start of this chapter:
a. Feature determination.
i. Principle component analysis and singular value decomposition [189, 95, 68, 149, 5].
b. Cluster determination.
i. Nearest neighbor clustering.
ii. Agglomerative clustering.
A. Fitch dendrogram algorithm .
B. Self-organizing tree algorithm .
iii. Divisive or partitional clustering.
A. Matrix incision tree .
B. Two-way clustering binary tree .
C. Coupled two-way clustering .
D. Cluster affinity search technique: .
c. Network determination.
i. differential equations .
ii. Bayesian networks .
iii. Hybrid petri networks .
iv. Dynamic bayesian networks .
v. Boolean regulatory networks [119, 197, 172, 1, 2].
a. Single feature or sample determination.
i. Naive bayes classifier [21, 46].
ii. Naive Bayes global relevance .
b. Multiple-feature determination.
i. Decision trees: Supervised and multiple-feature determination .
ii. Support vector machines [72, 33, 46].
iii. Tree harvesting .
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