PANDAS creator explains how to move the very first steps in Python data analysis.

The very first thing that comes to mind when the words statistics and programming are associated is definetly R. The R language represents the most used coding language in statistics and data analysis. But, what if you had to embedd the statistic part of your work in a bigger scripting project? What if you need to use the output of your statistical analysis as an input for your script?

Python users can enjoy Pandas

There are many solutions actually. You can use pipelines to connect different scripts, or you can use “bridges”, libraries designed to connect R scripts with other languages, such as R to Python or JRI for Java. Python users have another option, the very famous library PANDAS, that imports the R phylosophy in a full python library.

I don’t think that many readers out there are totally unaware of this library. Anyways, I still remember that you can have a look and download PANDAS from the officlal website.

A very simple 3H seminar!

For those who are starting to use this library and want to move their very first steps, the video embedded on the top can be a good tool. Wes Mckinney, PANDAS creator, gives a hands-on introduction to manipulating and analyzing large and small structured data sets in Python using the pandas library. So, if you have 3 hours to spend on this, you are very very welcome (WTF Wes!?!?).

Something about Wes Mckinney

I have heard about him since a while and really looks like a proven authority on Python data analysis. San Francisco- based python hacker and enterprouer, he’s also the author of the Python for data analysis book. I often keep an eye on his work on his blog and his twitter profile.


Evolutionary computing and 3D Virtual Creature Evolution. Check this seminar out.

I think I will definitely have to set up a seminars- dedicated category soon, since there are tons of interesting seminars around. In this one, Evolutionary Computing is explained. Evolutionary Computing is a branch of Artificial Intelligence computing that imports the principles of biological evolution in programming. The best known Evolutionary Computing issue are doubtless genetic algorithms, wich are becoming from year to year more involved in the implementation of algorithms for sequence alignment and structural prediction.

Lee Graham provides a good overview of Evolutionary Computing for the Ottawa Skeptics meeting. This video is quite dated, but still interesting. In order to explain his 3D Virtual Creature Evolution project, Graham gives a very good demonstration of the characteristics and potentials of evolutionary computing.

Basically, I’ve always thought that Evolutionary Computing represents a striking exception in modern science. There is a dogma in Scientific investigation that can be compared to the Molecular Biology Central Dogma. As in Molecular Biology it was originally stated that information flows from DNA to RNA to Proteins. we can say that in Science application is meant to flow from mathematical and base- sciences to Biology. The Molecular Biology Dogma has been denied by the revealing of reverse transcription mechanisms, and this “Science Application Dogma” has been denied by Evolutionary Computing since, at this time, is Logical Mathematics that takes advantage of theoretical principles defined in biology.

You can find other interesting videos in Leo Garham’s youtube channel.

Openworm. Understanding C. elegans by computational modeling

Simple and mysterious. The million times cited nematode Caenorabditis elegans is a small organism with only few hundreds cells in a constant number and a very simple structure. It is the perfect model organism for several wet-lab applications, from molecular biology to neurosciences. Anyways, the basis of its biology are not fully understood yet. An amazing attempt to insight the mechanism of this small worm it is carried on with the Openworm Project. A Java- based environment with a Python scripting interface is designed to build a theoretical model of a living C. elengans. The approach is of the bottom- up kind: observing informations that emerge from a simulation of data derived from scientific experiments in order to build a credible computer model. The software is maintained by a community in a full Open Source philosophy. Collaborations of individuals, researchers and philanthropists is very welcome.

So, if you are a java or python developer, a wet lab scientist, or maybe just a bit courious, consider to join this project. For a preview, the team organizes a Google Plus Hangout every two week. Everyone is invited.

The 6 books you must consider for your very first steps into databases.

Basically one would really avoid this. Biology is already quite hard to keep in mind, and you don’t really need of informatics to keep your brain busy. But, by definition, a bioinformatician is someone who match the two subjects and the best thing to do is to do it the best you can. Learning a powerful programming language such as Python, Ruby or Perl, getting used with markup- languages (XML and derivates) and learning databases are the three things a biologist must do to call himself bioinformatician.

Yes, ok, but how to do it? The standard procedure a computer scientist would enthusiastically prospect you is to find all the information for free and on the web. And he would be right. After all, the hacker philosophy is pretty clear about that: take all the free information you can find, even if this can be quite hard. But, since many of us are romantically devoted to books, and since not everybody is willing to spend time in a fight to the death against information entropy, sometimes release few money for a book it is not that bad idea.

Here are reported some books that can be useful to deepen into the world of databases. Building a database is a really boring thing you might really need. Handling big information is needed for the majority of genomics and structural studies. Building a database, beeing able to build querys or developing scripts to reason with the major genomic or protein databases out there could be really useful and time- saving.

Introduction to Database and Knowledge-base Systems
By S. Krishna

With this book you’ll learn the basics of database theory. Very easy to read and exhaustive.

SQL in a Nutshell A Desktop Quick Reference
By K. Klein

Maybe the best guide to understand Structured Query Language.

SAMS Teach Yourself SQL in 10 Minutes
By B. Forta

A concise guide to SQL. The text is organized into lessons. Easy to read and exhaustive.

MySQL Cookbook
By P. DuBois

900 pages to have a very complete overview on the open source DBMS MySQL. Maybe the best MySQL book around.

Instant PostgreSQL Starter
By D. K. Lyons

Move your first steps in the world of the open source DBMS PostgreSQL.

Database Annotation in Molecular Biology: Principles and Practice
By A. M. Lesk

A very exhaustive guide to Biological databases. Useful for database curators and users.

If you know more, please comment this.

Neural Networking: Online Social Content Easier to Recall Than Printed Info

Recollecting trivial and sometimes dull Facebook posts is easier than recalling the same information in a book. It also takes less effort to remember posted patter than someone’s face, according to new research.

The result could be due to the colloquial and largely spontaneous nature of Facebook posts. Whereas books and newspapers typically are combed over by fact-checkers and carefully rewritten by editors, Facebook posts tend to be free flowing and more closely resemble speech. “It’s a new way of thinking about memory,” says John Wixted, an experimental psychologist at the University of California, San Diego, who was not involved in the research. “Our minds are naturally prepared to encode what is naturally produced.”

If memories are the product of evolution, then the ability to remember socially derived conversations may have provided an advantage that helped early humans survive, he adds.

The study involved three different experiments with a sample that largely included undergraduate females and controlled for such factors as the use of emoticons, variations in character size and emotional content. What the research team found didn’t make sense—at first.

Laura Mickes, a cognitive psychologist at U.C. San Diego and lead author of the study, says colleagues in her department were amazed by the consistency of the results. “To our surprise the microblogs, the Facebook posts, are much more memorable than one would expect,” Mickes says. “People mostly think they’re mundane and would be easily forgotten.”

Even accounting for associative thinking—such as, “that is something my friend Emily would post”—the social networking site still had a pronounced effect on the extent to which information was remembered by study subjects. Facebook’s advantage over books and faces is on the same scale as the advantage that the average person has over the memory-impaired, Mickes wrote in the January 2013 Memory & Cognition. Both Mickes and Wixted agree that additional experiments are needed before these findings can be applied broadly, largely due to the lack of diversity among the study subjects.

Still, the implications are profound. Marketing firms could use Facebook-like advertisements to increase brand recognition. Teachers, too, might incorporate shorter, more colloquial sentences on study guides and in textbooks to raise test scores. The applications could be extensive: “I think there are implications for the way we teach, for how we advertise, how we generally communicate,” Mickes says. “There are already professors who are into tech who have incorporated social media into their classrooms.”

According to the study, Facebook users in total post more than 30 million times per hour. Whether it’s easier on the brain, that’s a lot to remember.