Researchers developed a new mathematical tool to validate and improve methods used by medical professionals to interpret results from clinical genetic tests. The work was published this month in Genetics in Medicine.
The research was led by Sean Tavtigian, PhD, a cancer researcher at Huntsman Cancer Institute (HCI) and professor of oncological sciences at the University of Utah, in collaboration with genetics experts from around the United States.
Tavtigian utilized Bayes' Theorem, a math equation first published in 1763, as the basis of a computational tool he and the team developed to assess the rigor of the current, widely-used approach to evaluate the results of a clinical genetic test.
Clinical genetic testing is used in a variety of medical fields, including cancer care, obstetrics, and neurosciences, among others. Results of a genetic test may help to provide a definitive medical diagnosis, or assess the likelihood of a person to develop a particular disease before symptoms appear. The range of approaches employed to provide health care based on the results of the test can vary significantly. Patients may be at negligible risk for disease with no medical management required, or they may pursue costly, invasive medical treatment in an effort to stave off disease or manage and minimize symptoms.
With millions and millions of changes possible in genes that control health in any given person, the challenge of discerning which gene changes are likely to cause disease is vast. In the past few years, human genetic researchers have identified thousands of Variants of Uncertain Significance (VUS), that is, genetic changes without a known understanding of how they may impact a person's health. "A large fraction of VUS are believed to be generally harmless," describes Tavtigian. "One only wants to change the medical management of patients when the genetic testing identifies a variant that is likely to be disease-causing. Against a huge population of harmless VUS, how do you identify the small subset that are likely to require medical management?"
Source: https://huntsmancancer.org/newsroom/2018/01/centuries-old-math-equation.php
Sean V Tavtigian, Marc S Greenblatt, Steven M Harrison, Robert L Nussbaum, Snehit A Prabhu, Kenneth M Boucher, Leslie G Biesecker. Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework. FENETICS in MEDICINE, 2018; DOI: 10.1038/gim.2017.210
(Score: -1, Spam) by Anonymous Coward on Sunday January 21 2018, @01:05PM
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(Score: 3, Insightful) by Anonymous Coward on Sunday January 21 2018, @01:36PM (15 children)
This article is written by a really clueless person. There is nothing special about the use of bayes' theorem here that would merit this description. It is an equation used all the time...
It would be like saying "millennia old math equation used to solve construction problem". (Pythagorean theorem)
(Score: 0) by Anonymous Coward on Sunday January 21 2018, @01:45PM
This person obviously likes to hear him/her self speak. In this case read one's own writing.
(Score: 2) by AthanasiusKircher on Sunday January 21 2018, @01:52PM (9 children)
Yeah, this is really basic stats/probability.
I hoped there might be more about Bayes' Theorem in TFA or something to justify the excitement about math... But alas no. It's basically what you see in the summary, plus more vague description of validating their approach. I have no idea why the author of the headline thought the history was noteworthy. A lot of basic math is centuries or millennia old... I guess this person just hadn't heard of Bayes before??
(If so, that's an education issue now. Given all the applicatons in modern data and probabilistic analysis, Bayes' Theorem should be better known than Pythagoras. It's not like it's significantly more difficult to understand, but it's essential to understand why intuitive estimates of probability are often so off...)
(Score: 3, Informative) by janrinok on Sunday January 21 2018, @03:54PM (7 children)
I suspect that you are concentrating on the wrong part of the summary - the use of Bayesian Theory is only the tool used to solve the following breakthrough:
The breakthrough is, that by using a mathematical theory that has been know about for years (and yet had not be used up to now to solve this problem) scientists are now able to better identify which genes are linked to which to specific problems and thus help genetic testing to treat those who require it with a higher degree of certainty that was the case before. Often, it is not the tool itself that is the ground breaking step, but that somebody thought to use that tool to solve a problem in a way that had not been done before.
(Score: 0) by Anonymous Coward on Sunday January 21 2018, @05:56PM
What theory?
(Score: 2) by AthanasiusKircher on Sunday January 21 2018, @06:30PM (4 children)
I don't mean this to sound aggressive or insulting, but do you know what Bayes' Theorem is? It's an incredibly basic concept in probability. And any sort of problem that is even vaguely like the one you quote would be an obvious application for Bayes' Theorem. Maybe there's something interesting about the details of how they employed Bayes in a complex way or something -- but the concept of applying the basic theory is so utterly simplistic that all geneticists would be idiots if they didn't think of using it in some form. Any problem involving multiple probabilities that may be dependent on each other is a potential application.
In fact, when you click on the link to the actual study (which only gives a limited summary), you see the actual authors explain what they did:
In other words, they had some guidelines that were only qualitative, and then they employed the most obvious probabilistic tool to those guidelines to quantify them in their first attempt at a mathematical analysis. Now -- there may be more to this, but I can't tell without reading the complete study. On the surface, this all sounds like a really basic idea, though...
Regardless of whether there's more to the actual research, I still maintain that whoever wrote the headline doesn't understand how utterly common and obvious applying Bayesian reasoning to such a problem would be.
(Score: 3, Insightful) by MichaelDavidCrawford on Sunday January 21 2018, @08:35PM
Very few computer scientists know about Noether's theorem despite its fundamental importance to physics
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(Score: 2) by sbgen on Monday January 22 2018, @03:36AM
You sound like you have training in mathematics. However, do you know the data analysis paradigm in use in the clinical and molecular genetics field from which the paper is? The most prevailing way to analyze data is Null Hypothesis Significance Testing (NHST). This is a major problem because the basic assumptions of NHST are patently unmet in most cases in clinical genetics yet the paradigm persists. Without some sort of significance test your data will not be published in most journals. People have only started to talk about it recently and use of Bayesian analysis is is beginning. You might rightfully be disappointed with the headline or the fact that Baye's theorem is not widely used in estimating the probabilities in medical genetics; but next time you get a prescription remember the decision may be based on NHST.
I havent read the paper and I am not commenting on the headline you are unhappy with. Thought I should elaborate on what janinrok said above.
Warning: Not a computer expert, but got to use it. Yes, my kind does exist.
(Score: 2) by janrinok on Monday January 22 2018, @07:59AM (1 child)
And I repeat - until now, nobody had thought of using this very obvious analysis approach. That is why this is news. It might be obvious to you, me, and to many others, but none of us suggested doing it before.
(Score: 0) by Anonymous Coward on Monday January 22 2018, @03:42PM
Yes, moving past ignorance is progress. But not all progress is worthy of attention by non-specialists.
(Score: 0) by Anonymous Coward on Monday January 22 2018, @01:33AM
A computer working tirelessly would try all available approaches to solving a problem - why can't we automate this kind of discovery?
(Score: 2) by Dr Spin on Sunday January 21 2018, @07:03PM
Bayes' Theorem should be better known than Pythagoras.
Are you suggesting that the average doctor understands Pythagoras?
Next, you will be suggesting they are capable of understanding the odds on horses!
You, here, probably can understand most first year degree level maths. The average person has a problem
with percentages. People often go into medicine because they cannot understand maths at all!
Warning: Opening your mouth may invalidate your brain!
(Score: 0) by Anonymous Coward on Sunday January 21 2018, @03:06PM
Bayes' theorem is also interesting because it connects the logical perfection of mathematics to a tricky philosophical question that doesn't have a clear-cut answer. What should our prior assumptions be?
But I agree with your point that the headline and article are clueless.
(Score: 1, Interesting) by Anonymous Coward on Sunday January 21 2018, @03:42PM (2 children)
I guess you haven't heard about this one yet:
https://fliptomato.wordpress.com/2007/03/19/medical-researcher-discovers-integration-gets-75-citations/ [wordpress.com]
For most medical professionals, mathematics is this weird foreign thing completely unrelated to their work (my guess is this is strongly related to the lack of reproducibility of various pharmaceutical/biological research).
(Score: 2) by AthanasiusKircher on Sunday January 21 2018, @06:45PM (1 child)
Yes, I hate to jump on the bandwagon of stereotyping here -- there ARE plenty of medical researchers who DO know at least something about basic math and particularly stats -- but it's sometimes quite painful to see the ignorance in some studies. Years ago when I had an infant, I spent several days trying to find and read lots of studies about SIDS in medical journals, because that's often a big concern that parents are warned about with infants and sleep.
Anyhow, it became pretty obvious to me that some studies were seeing statistical significance or meaning where there was none, often based on confusion of basic probability terms (like odds ratio vs. relative risk, for example). At first I tried to give authors the benefit of the doubt, but then when I saw a study that contained a long footnote where the authors basically "showed the work" in painstaking detail for calculating an odds ratio, I realized I probably wasn't imagining things. That is, something that was essentially a basic arithmetic problem justified having a long footnote showing how it was calculated (not just stating the formula either... the author obviously thought what they were doing was complicated enough to show all the "work" involved in calculating basic arithmetic).
Again, I have seen plenty of very good studies done by much more competent people, and kudos to them. But over the years I've also seen plenty of these things where it's clear that even incredibly basic mathematical tools are unfamiliar to such researchers. And it's really concerning when the conclusions depend on the interpretation of said tools (as they did in the studies I was reading about SIDS).
(Score: 2) by AthanasiusKircher on Sunday January 21 2018, @06:55PM
Oh, and lest ye think I'm making all this up, there have been numerous studies over the past 40 years or so showing consistent ignorance of Bayesian reasoning among practicing physicians and how that likely leads to all sorts of misinterpretations of test results, screenings, unnecessary follow-up tests, etc. Here's one analysis [slatestarcodex.com] of one of the more recent studies on this phenomenon, but you can find plenty of similar articles over the decades.
Even worse, as you can read in that link, the doctors who were most likely to get basic probability questions wrong in interpreting frequency for understanding test results were more likely to claim they had good training in stats. In other words, the most ignorant thought they had the best grasp of the material.
(Note that most of the studies on this have interviewed doctors, not medical researchers. And as I said in my previous post, there are plenty of competent researchers out there with a knowledge of stats. But I wanted to point out that ignorance of Bayesian reasoning and its basic implications is pretty much normal in medical circles... even though applications like the one in TFA are crying out for this sort of analysis.)
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(Score: 3, Funny) by deadstick on Sunday January 21 2018, @10:25PM
I used Newton's equations of motion just the other day, and he died in 1726.
(Score: 3, Interesting) by linkdude64 on Monday January 22 2018, @12:15AM
ANCIENT NUMBERS REVIVED FOR USE BY MIDDLE SCHOOLER
HERE IS THE PROBLEM, AS OBSERVED AND DEPICTED BY OUR "REAL NEWS" """"PANEL OF EXPERTS""""
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