Even for a new allele, say a SNP, its combination is only A, C, T, or G. Unless they can show that it is highly unlikely that the patented modification would occur naturally, any new alleles should be patent free. And heck, they can't compare the chance with pure random chance since we know that mutations / gene modifications do not occur randomly either. Claiming so would be very hard.

New treatment may or may not be patentable as well. If the treatment involves a naturally occurring sequence from other people (I'm thinking of siRNA and the likes), they can't patent the sequence either. They can only patent the method to synthesize it. Even then, if the method is in fact a naturally occurring method (i.e., that's how the body of human or other creatures does it), then they can't patent it either.

Actually, it's a tough situation. There is no real life experimental data can 100% fit the assumptions of commonly used statistical models. Real life data is messy. There is some degree of simplification. In addition, resorting to whiz-bang fancy methods that "fit" the real data may not be easily interpretable. Ease of result interpretability is what medical scientists want. There are other issues as well, such as computing time, equations derivability, etc.

In addition, many many medical scientists use statistics as a tool to filter things (e.g. candidate genes, target enzymes, treatments, etc). In this case, 100% accuracy is not really important. Once the scientists narrow down the genes, they can test the validity directly in either test animals or real people.

Many of these biology experiments require very expensive machines, such as microarray machines, as mentioned by the article. I don't know if purchasing refurbished machines is a wise choice since we don't want data quality to be compromised. Also, don't forget about service plans when the machines break or producing inconsistent output. Not to mention various reagents, other chemicals, and supplies such as microarray chips that make the experiment yields high quality data. These easily reach hundreds of dollars a piece. Also, purchasing such chemicals will get you labeled as a terrorist.

Another issue is gathering the samples. If you're collecting yeast, that would be simple. Arabidopsis, other small plants, mice, or other small animals, you probably need quite some space. Humans? That won't be simple at all. You have to clear privacy issues, getting the research review board to sign papers, etc. Sample collection alone can cost you lots of money and time. You can always resort to publicly available data. But chances are that you won't be able to impress scientists much for going that route. Also, most of the important discoveries are already done on this data. Most likely, all you can do is to confirm existing results or to provide some tangential additional info.

The program for my dissertation (which were done in 3 years or so) has more than 160K lines. I've done several projects of that magnitude. 1M of codes is not as hard as you have thought. Some codes are just accessors, bridges, and other standard patterns that you would do over and over again for many different use cases. Each of these can easily reach 200-300 lines of code. If you're used to doing this, you wouldn't even think twice about the code.