ACTN3 Gene and Sports Performance – A Look into 1,750 Genomes [OpenSNP]

While phenotypes are most often defined by a combination of genetic mutations (SNPs and other), there are single gene modifications that seem to have powerful phenotypic effects – think of diseases driven by single nucleotide polymorphisms. In such circumstances, you can’t do much on the ‘nurture’ side of things – when the ‘nature’ or the genetics side of it is so determining.

We’re going to take a brief look at a phenotype that seems to be strongly affected by mutations in the alpha-actin-3 or ACTN3 gene. Specifically, we take advantage of the ‘opennes’ of the OpenSNP platform where users share genetic and phenotype data.

Even more specifically, we’re looking at rs1815739 (SNP) which refers to the coding of a premature stop codon in ACTN3, which is a muscle protein located on chromosome 11. This genetic mutation seems to affect muscle performance.

Beginning Machine Learning – A few Resources [Subjective]

I’ve been meaning to write this post for a while now, because many people following the scikit-learn video tutorials and the ML group are asking for direction, as in resources for those who are just starting out.

So, I decided to put up a short and subjective list with some of the resources I’d recommend for this purpose. I’ve used some of these resources when I started out with ML. Practically, there are unlimited free resources online. You just have to search, pick something, and start putting in the work, which is probably one of the most important aspects of learning and developing any skill.

Since most of these resources involve knowledge of programming (especially Python), I am assuming you have decent skills. If you don’t, I’d suggest learning to program first. I’ll write a post about that in the future, but until then, you could start, hands-on, with the free Sololearn platform.

The following resources include, but are not limited to books, courses, lectures, posts, and Jupyter notebooks, just to name a few.

I Walked 7.55 Kilometers per Day for a Month – [Insights]

May 2017 is ranked number #2 for the monthly average steps I walked since the default pedometer has been running continuously on my phone.

So, in May 2017 I completed an average of 7.55 kilometers or 9,667 steps per day, which burned, according to the pedometer, an additional 347 kcals, on top of the rest of my ‘energy out’ for each day. However, estimates like this are notoriously inaccurate.

Number #1 on the list is September 2014, when I completed an average of 10,256 steps.

What I’ve Been Reading Recently – My Bookshelf #11

The last time I wrote this type of post was at the beginning of 2017 when I was telling you about the 105 books I read in 2016.

It’s less likely I’ll achieve that type of performance in 2017, and that’s fine. I still prioritize on reading and listening to books everyday. So, that’s my only important metric for now.

Similar to what I said in my last post, the majority of the books that go through my hands are non-fiction: science books, programming books, textbooks, biographies, and the like).

I enjoy reading on my tablet because it has a stylus-pen and the app I read the books on has a lot of features that allow for annotations, highlights, exporting, synching, and other conveniences that make the reading experience a breeze. A detailed explanation of this can be found here.

I find a lot of pleasure in reading physical books too. As a matter of fact, I managed to read 11 books during a recent stay in New York, which lasted for a couple of weeks; and that happened within a very busy and hectic daily schedule. This goes to show the importance of prioritization for goal accomplishment. I have to mention that I completely stopped reading on my tablet while in NYC.

Machine Learning with Scikit-Learn – The Cancer Dataset [Work in Progress]

If you’ve been following my Youtube channel, you know I’ve been doing machine learning tutorials on the cancer dataset that comes preloaded with scikit-learn in Python.

The tutorials follow a path similar to what’s in Andreas Muller and Sarah Guido’s book on machine learning. This allows me not only to pass on knowledge to others who are interested in the details, but also to strengthen my own knowledge of these concepts.

24 video tutorials in and I realize that this is going to be a long series. I want to take the appropriate time to look into the details and to apply the very specifics for each algorithm and concept as we explore the cancer dataset.

Afterthoughts of a 36-hour Fast – [May 2017]

I recently traveled to Boston and I decided to do a 36 hour fast. Over the past few weeks I’ve deliberately not been doing IF consistently and I’ve been eating big at night, right before bed, with no observable changes in physique.

How a Computer can Recognize an Image and Tell you what it Sees – [Desktop App]

49 lines of code. That’s all it takes to make a computer look at an image and tell you what it sees in a lifelike sounding voice.

I don’t have to reinvent the wheel. Others came before me; they made amazing creations. I can stand on their shoulders and praise their work.

To be relevant to programming, I don’t have to write a powerful algorithm for computer vision or one for speech synthesis from scratch. I can, but that’s not what I want to do with my time, especially if there are many out there that you can readily use.

What would it be like if every time you need to write code you have to program in assembler or at other lower levels of abstraction? You’d have to write thousands of lines of code for the most basic output, like a print statement for example.

Now this doesn’t mean that I’m all against that. On the contrary, I am contextually driven. I like to program my own stuff from lower levels of abstraction when I need something very specific and custom-made, which has not already been done by others.

The Five Most Mutated Genes in Cancers – [A 2017 ICGC Perspective]

The International Cancer Genome Consortium (ICGC) has a portal that currently (May 2017) hosts data from 70 cancer projects spanned across 16 countries.

Here are a few descriptors of the data (as of current):

– 19,305 donors
– 31 tumor types in 21 primary tumor sites
– data types include: simple somatic mutations (SSM), structural somatic mutations, copy number somatic mutations (CNSM), sequence and array based gene expression data, methylation data, protein expression data, etc.

This is big data because it comprises of ~163,000 files in ~1.2 PB (petabytes), which is the equivalent of 1,200 terabytes or 1,200,000 gigabytes. A lot of A,C,T,G sequences…

The portal is a great platform in of itself, in that you can do advanced searches and ‘onsite’ data analyses, genome browsing, and much more. So, if you like numbers (like me), you can literally spend countless hours trying to make sense of this ever growing ocean of data.

The purpose of this post is not to go deep though; I may do that in later posts. Here, I’m only going to talk about the top 5 mutated genes with high impact (simple somatic mutations) across all cancers from 10,648 donors.

Voice Input App in Python – Code Release and Overview [May 2017]

I built the following application, primarily, for convenience…

There are times when I don’t wanna type on my laptop. And I didn’t know of any general purpose, simple, minimal application that could do voice recognition and text input in the most basic form, as in: listen to my voice, paste what I just said, so I don’t have to type it.

I use this app in social media replies as well as when I post updates on different channels.

And, to be completely honest, there are times when I eat chocolate while watching scientific lectures (positive reinforcement). Some of these lectures spark spontaneous thoughts that I want to share, and the only ‘clean’ way to do it is by voice: one hand is used for chocolate manipulation, while the other for handling the mouse.

So, this gave me a solid reason to build this application.

On the Potential Benefits of Physiologic Stressors – Cold Exposure

The following is an excerpt from Chapter 2 of my book Stress and Adaptation in Physiology. This chapter is about stressors and adaptive responses, and this specific excerpt is about the timing and duration of exposure to stressors. These parameters often make the difference between a poison and a medication.

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