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This is where combinatorialists, researchers, and biologists come in. Often, they use some of these methods to uncover new drugs, especially when data is involved. This chapter reports on some of the various algorithms that combine data, logical plans, and ideas into one great computational utility. While DNA is not as reliable as other genomic locations—among many other reasons—it is very dynamic. Some genes have duplicates. Or fragments of genes. Some genes, like the ATP and DNA polymerase or the complement, have many genes mixed together. There are several ways to identify a gene in one instance: some programs come with programs that give you a structure for it, or some programs come with programs that try to find a known gene, which makes sense for what information would lead scientists to such a structure. There are a couple of programs from Dr. Samuel Bielwein, lead researcher at Eli Lilly, but here are a few experiments that can help in deriving some of the computational models and concepts. First, see the examples in Figure 1.1. **Figure 1.1**. Compound of interest. Now that you have a map of all likely gene sequences, and a DNA sequence, or whatever types are written down, it’s easy to see that some of this data is almost useless and that the rest is in fact useless. A DNA sequence is usually more likely to be the same type of data if data can be used to represent such a structure. However, you might figure out which DNA or other materials will be the most useful for that particular structure, so that you could actually use sequences to express something about it. Unfortunately, these algorithms aren’t really good at making other data efficient, and you just can’t do the work that they do to do those useful things. One group of algorithms, called DNA engineering, found themselves in the arms race to find DNA for the human genome with the Human Genome Project database.
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