> I think part of the reason for an apparent increase rise in cancer rates is that we have eliminated many other causes of death already

Where did you read "an apparrent increase rise in cancer rates" ? in this article or in one of the articles it references? which one?

I am not a biochemist, but I would assume academics are referring to incidence rates, not causes of deaths... If you did actually witness such a confusion in the papers, it's important to point it out, but if you didn't it would be equivalent to a physicist suspecting a colleague of confusing mu (the reduced mass of a binary system) with mu (a muon)... rather incredulous if you ask me...

Every academic discipline expects its disciples to be at least proficient in disambiguating words from context, so when one refers to a "cancer rate" in the context of causation, that it would refer to "incidence rates" i.e. the transition probability per surviving individual per unit time. This is independent of deaths by other causes.

I was directly referencing the parent comment:

> At the same time, everybody seems to accept that the chance of getting cancer in your lifetime has risen to about one in three for men and one in five for women.

Which suggests that there is an apparent increase in cancer rates. So yes, I was referring to an incident rate. Regardless, the semantics here don't change the meaning of my statement. We have solved (for lack of a better word) many of the lower hanging fruits of human disorders. As such, we can't effectively control for incidence rates over time

I'm not disagreeing with you, but I have worked with world-class scientists where the author only had a gmail address (untill we gave them an appointment at Berkeley).

Of course, a gmail address in and of itself is not an indicator of quality, however on a single author paper it certainly makes me wary.

Just out of curiosity, what field was that in? I could certainly see some scientific disciplines having unaffiliated world-class scientists, but others it would be virtually impossible to do high quality research outside of a lab

bioinformatics/computational biology. Specifically, multiple sequence alignment and HMMs for protein recognition. The author was previously a physicist (PhD) who left physics due to the low number of jobs in the 70s-80s, founded a database company, sold it to Intel, and then visited Berkeley and saw a cool talk and volunteered.

There were already a few codes in the area and plenty of papers, and he was mathematically inclined, so it didn't take long for him to become an expert. Once he was an expert, he pointed out major problems in existing codes (both functional and performance).

This is an area where you're working with fairly straightforward data and math (linear strings from a chosen alphabet, probabilistic model is well-established). You don't need to understand the underlying biology in detail to contribute.

Thanks for the answer - I figured it would be some bioinformatics/mathematics/statistics leaning field - you can definitely do more as a lone wolf there than say, biology.