Genetics and genomics

Genetics and genomics is an exciting area of scientific research, growing in its importance. It will let us learn more about us and help cure many diseases. A good recent book on this subject is “Genetics and Genomics” by  Tom Strachan, Judith Goodship and Patrick Chinnery.

If you want to stay updated, the US National Center for Biotechnology Information (NCBI) makes available scientific research articles 

You may also want to check Google Scholar

The OMIM (Online Mendelian Inheritance in Man database) provides more information on single gene disorders

For even more info on gene disorders, the University of Washington’s Gene Reviews series are highly recommended and available at

Other very useful resources.
SNP (pronounced ‘snip’) is the short term for Single Nucleotide Polymorphism.
The importance of SNPs comes from their ability to influence disease risk, drug efficacy and side-effects, tell you about your ancestry, and predict aspects of how you look and even act. SNPs are probably the most important category of genetic changes influencing common diseases. And in terms of common diseases, 9 of the top 10 leading causes of death have a genetic component and thus most likely one or more SNPs influence your risk.

The Rs1815739 SNP for example will tell you whether you are likely a sprinter or an endurance athlete.

OpenSNP is a non-profit, open-source project that is about sharing genetical and phenotypic information. You can find info on SNPs

SNPedia is a wiki investigating human genetics. We share information about the effects of variations in DNA, citing peer-reviewed scientific publications.

ClinVar aggregates information about genomic variation and its relationship to human health.

Genecards is a searchable, integrative database that provides comprehensive, user-friendly information on all annotated and predicted human genes.

Free online books at the US National Center for Biotechnology Information (NCBI)

Mayo Clinic

Data Science books

What is data science and what you need to know to be a data scientist?
Joel Grus, in his book writes “There’s a joke that says a data scientist is someone who knows more statistics than a computer scientist and more computer science than a statistician. (I didn’t say it was a good joke.)”

Data Science from Scratch: First Principles with Python by Joel Grus
Doing Data Science: Straight Talk from the Frontline by Cathy O’Neil and Rachel Schutt
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney

Machine Learning
Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
Introduction to Machine Learning by Ethem Alpaydin
Introduction to Machine Learning by Alex Smola (free download)
Pattern Recognition and Machine Learning by Christopher Bishop
Programming Collective Intelligence Toby Segaran
Bayesian Reasoning and Machine Learning by David Barber
Bayesian Data Analysis by Andrew Gelman and John B. Carlin

Data Analysis
Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman and Jennifer Hill
The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie and Robert Tibshirani
Statistical Inference by George Casella
Advanced Data Analysis from an Elementary Point of View by Cosma Rohilla Shalizi (draft free download)

Statics and Probability
Introductory Statistics Barbara Illowsky,Susan Dean (free download)
OpenIntro Statistics by David M Diez, Christopher D Barr, Mine Cetinkaya-Rundel (free download)
Introduction to Probability by Charles M. Grinstead and J. Laurie Snell
Introduction to Probability Models by Sheldon M. Ross
A First Course in Probability by Sheldon Ross

Linear Algebra and Calculus
Active Calculus Matthew Boelkins, David Austin, Steven Schlicker (free download)
Convex Optimization by Stephen Boyd and Lieven Vandenberghe (free download)
Linear Algebra and Its Applications by Gilbert Strang
Linear Algebra by Jim Hefferon
Linear Algebra by David Cherney, Tom Denton and Andrew Waldron (free download)
Linear Algebra Done Wrong by Sergei Treil (free download)

Presenting the information
The Visual Display of Quantitative Information by Edward R. Tufte

The apps monetization problem

While app stores are a great distribution channel for developers that want to sell applications, it is not the best monetization tool if you want to get a living.

There are some mechanisms in the current model of the stores that limit developers on how and what they can charge for the application and services and this has an impact on the quality of the applications and limit their potential.

Ben Thomson of Stratechery nicely list 3 problems in his From Products to Platforms post.

The problem for iPad developers is three-fold:

  • First, the lack of trials means that genuinely superior apps are unable to charge higher prices because there is no way to demonstrate to consumers prior to purchase why they should pay more. Some apps can hack around this with in-app purchases, but purposely ruining the user experience is an exceedingly difficult way to demonstrate that your experience is superior
  • Secondly, the lack of a simple upgrade path (and upgrade pricing) makes it difficult to extract additional revenue from your best customers; it is far easier to get your fans to pay more than it is to find completely new customers forever. Again, developers can hack around this by simply releasing completely new apps, but it’s a poor experience at best and there is no way to reward return customers with better pricing, or, more critically, to communicate to them why they should upgrade
  • That there is the third point: Apple has completely intermediated the relationship between developers and their customers. Not only can developers not communicate news about upgrades (or again, hack around it with inappropriate notifications), they also can’t gain qualitative feedback that could inspire the sort of improvements that would make an upgrade attractive in the first place

Coase and the nature of the firm

Ronal Coase described how integration works in terms of transaction costs, in his famous on the Nature of the Firm:

“The existence of high transaction costs outside firms led to the emergence of the firm as we know it, and management as we know it….The reverse side of Coase’s argument is as important: If the (transaction) costs of exchanging value in the society at large go down drastically as is happening today, the form and logic of economic and organizational entities necessarily need to change! The core firm should now be small and agile, with a large network.”

technology and new business models have enabled a new disrupting ways to create firms.

Tim O’really Networks and the Nature of the Firm

Esko Kilppi The Future of Firms. Is There an App for That?


Albert Wenger Networks, Firms and Markets

On the world we live in

As the year ends, some interesting articles and books about the world we live in.

The Economics of Inclusion Ricardo Hausmann
A Year of Divergence  Mohamed A. El-Erian
Make No Mistake: The Machines Are Coming Nouriel Roubini

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson and Andrew McAfee
Capital in the Twenty-First Century by Thomas Piketty

Genomics is the future

Just got the results from 23andme. It delivered some interesting results and accurate information I already had. Despite the current discussions with the FDA, this is certainly a very important tool for the progress of medicine and science in general. It is also curious to discover whether you have ancestry from different countries. 

An indepth view of the company from FastCpompany, INSIDE 23ANDME FOUNDER ANNE WOJCICKI’S $99 DNA REVOLUTION,

23andme values:

23andMe was founded to empower individuals and develop new ways of accelerating research. The members of 23andMe have come together because we believe in the combined potential of genetics and the Internet to have a significant, positive impact.

  • We believe that having the means to access one’s genetic information is good.
  • We believe that your genetic information should be controlled by you.
  • We believe that people’s similarities are just as important as their differences.
  • We believe that the value of your genetic information will increase over time.
  • We encourage dialogue on the ethical, social and policy implications of personalized genetic services.
  • We believe in giving everyone the opportunity to contribute to improving human understanding.


I personally believe in the value of what they are doing and the future is more personalized medicine and tailored drugs. Eric Topol has written a beautiful book, The Creative Destruction of Medicine in which the importance of genomics is highlighted. However, we must also recognize the risks that genomics has regarding our privacy and personal information.

The The Sports Gene: Inside the Science of Extraordinary Athletic Performance by David Epstein shows how genomics helps understand some of the characteristics of different athletes. It is not all about genes, but they play an important role in sport performance.

Recently the FDA has asked 23andme to conform to their regulations and understand the consequences of distributing their information to people that take their tests. Bloomberg, FDA Tells Google-Backed 23andMe to Halt DNA Test Service, the New York Times F.D.A. Orders Genetic Testing Firm to Stop Selling DNA Analysis Service. FastCompany, WHY 23ANDME TERRIFIES HEALTH INSURANCE COMPANIES. Scientific American 23andMe Is Terrifying, But Not for the Reasons the FDA Thinks. Forbes Why The FDA Can’t Be Flexible With 23andMe, By Law.

A set of opinions on the topic opinions nicely summed up by David Dobbs FDA Muzzles 23andMe After Talks Break Down,

Amazon, a company with a long term view

A lot of articles and analysis on Amazon, a company with increasing revenues but with low profits and operating even at loss in certain quarters. A strategy that focuses on the long term view, world domination and expansion.

A great recent book is The Everything Store by Brad Stone.

A take from famous VC, Fred Wilson Profitless Prosperity

A view from Benedict Evans Amazon’s profits

an article from the Atlantic, The Amazon Mystery: What America’s Strangest Tech Company Is Really Up To

Blog posts by a former Amazon employee, Eugene Wei Amazon and the “profitless business model” fallacy and  Amazon, Apple, and the beauty of low margins

Slate magazine Amazon’s Jeff Bezos Is Like King Midas in Reverse

Digital Convergence and business models

The interesting fact about digital convergence is that many things can change, disrupting existing players,  and industries.
Established business models are not anymore valid and are challenged.

A simple example.

Apple has just announced that the upgrade to the new version of their OS Mavericks will be FREE, and people that will buy their devices will also get the iWork productivity suite for FREE (Page, Numbers and Keynote). Money are made on the hardware.

In the meantime, Google is giving all its software for FREE and eventually selling its hardware at cost, making money with ads.

Amazon, is selling its hardware at cost, making money on the content sold through its devices.

Microsoft makes money on the software that all the others are now giving away for FREE.

And the FREE software or the FREE hardware is actually great, not bad copies of the paid versions. Let’s be clear, nothing is really FREE. They are only monetizing in a different way using asymmetrical business models, that is giving something for free, while making money with something else.

That’s the challenge for Microsoft that used to get paid for what it sold, software. And probably that’s the reason it has acquired Nokia. With hardware and software it can decide to monetize in different ways. Will it be enough?