Bioinformatics or B101nformatics

One of my last post was " I don't do machine learning ". Now, I realized that I am actually doing it (more on this below). However what I was doing previously? I would say I was doing data learning . What is different in machine learning to methods? After reading a lot, I think that the differences are on the focus, a regression (substitute for other procedures that model something) can be used for both machine learning and data learning. When I do a linear regression in data learning I focus on which variables have more weight and improve the adjusted R 2 . I learn from the data what is happening . When I do machine learning I focus on which linear regression is better with the data I have (or with new data). I learn from the data what models are better. That model can be a simple regression or a complicated deep neural network. Both have the same data, but the goal is different. Of course, nowadays where it is not too computationally expensive we want both: ...

I have recently learned about the RV coefficient and I wanted to share it: The RV coefficient is like a correlation between several variables. In R I found so far several methods that seems to calculate it: FactoMineR::coeffRV subselect::rv.coef MatrixCorrelation::RV However the rv.coeff function from the subselect package works with only one matrix, and the coeffRV and the RV functions differ in they results. This lead me to search for its definition. All the papers mention Escoufier as the originator of these idea. The longest citation (and more relevant) can be found on a preprint , where it is mentioned the year, the author and that several of these papers are in French. In this document we can find the definition which is only followed by the RV function, in the coeffRV function the values are scaled. You can check that: RV(scale(X, scale = FALSE), scale(Y, scale = FALSE)) == coeffRV(X, Y)$rv

Yes, the title is true even if I do data science in bioinformatics, I don't do machine learning. As seen recently if used correctly, regressions tend to work as well as machine learning. Classic tools (?) still work, I can't say I have tried all of them, but they are quite useful. Also in bioinformatics it is hard to get a big number of samples to make both a good and reliable generalization and to train reliable a model with enough confidence. Last, most machine learning methods are to me black boxes, I don't understand them (yet). I like to understand what I use. (Although I can't say I have deeply understood the differences between some regression methods I use). Then, why I am writing this? Because it seems like an hype to say things like "powerful network medicine tools", "machine learning model", without explaining them in detail. So it becomes a black box, and science is not about black boxes. In science we want to increase the know...

For some time I have been working on one topic. I am not sure if this is how it started but I fail to see other reasons. So I'll describe why I'm now working with gene sets collections. The trigger I usually try to help others in Biostars , Bioconductor , and in the StackExchange network (specially in Bioinformatics ). On one of these sites I was trying to help some person, and in one of the comments ( Jun 21 '17 ) it says: You don't build pathway maps from bioinformatics data, you build them from wet-lab experiments. And I was : " Why not? We already know (kind of) the number of genes, and we have an idea or the number of metabolites in a cell. We have many data, why can't we build pathways?" But I did a brief literature search and I couldn't find anything (if there is something let me know in the comments).   The background Let me explain why this comment got me puzzled: in my work I am usually asked what is the relevan...

Since some time I am working with Crohn's Disease. One of the problems with the disease is that it is not known what happens. People has found associations with microorganisms, but the relationship between those microorganisms and the patient is still unknown. Also the risk factors for complications is largely unknown. This post follows up the use of a patient cohort data enrolled for identifying the risk factors of complications and health-care costs in pediatric and adult onset Crohn’s disease. Where we can see some usage of the data and the problems of unclear descriptions when using the same data. Articles describing the RISK cohort The first mention to the RISK cohort I could found is in this article [1] where they describe a cohort as: an observational research program that enrolled patients younger than age 17 diagnosed with in flammatory (nonpenetrating, nonstricturing) CD from 2008 through 2012 at 28 pediatric gastroenterology centers in North America. In that ar...