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Modern Research: Faster Is Different

Faster is different. It sounds strange at first because we expect faster to be better. We expect faster to be more. If we can analyze data faster, we can analyze more data. If we can network faster, we can network with more people. Faster is more, which is better, but more is different.

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A Data Application for the _______ Genome Project

The ability to reuse and repurpose - exaptation - is often a catalyst for exciting breakthroughs. The Astronomical Medicine Project (yes, astronomical medicine) was founded on the realization that space phenomena could be visualized using MRI software, like highly irregular brains. The first private space plane, designed by Burt Ratan, reenters the atmosphere using wings inspired by a badminton birdie. Anecdotes like this abound in many fields, and the principle applies to working with data and creating data applications, as much as it does any innovation. 

To demonstrate and give our users a running start at successfully repurposing something, we want to share an editable data application, the Taco Cuisine Genome AtlasWe held an internal hackathon in which teams had a day to design and build a xap. (A xap is what we call data application built with our platform. Learn a bit more about our dataflow programming environment here.) One team took algorithms and visualizations created for a cancer research application and applied them to tacos. Application users can identify, according to multiple ingredients, specific tacos and where to find them.

The best part is that this wasn't entirely an act of frivolity. Repurposing healthcare and life sciences tools on different, albeit mundane, data led to a potential improvement for the cancer research application - a map visualization of clinical trials for specific cancer types. 

It can't be said enough. New perspective is a key catalyst for innovation. 

So, we've made this xap available for the public to kickstart other work. Explore itbuild off it, and apply it to your own data. You can also learn the basics of how it's done.

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Alleviating Uncertainty in Bayesian Inference with MCMC sampling and Metropolis-Hastings

Bayesian inference is a statistical method used to update a prior belief based on new evidence, an extremely useful technique with innumerable applications. Uncertainty about probabilities that are hard to quantify is one of the challenges of Bayesian inference, but there is a solution that is exciting for its cross-disciplinary origins and the elegant chain of ideas of which it is composed.

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When Earth is Like an Egg: 3D Terrain Visualization

Some of the most satisfying breakthroughs happen when technology gets used in a way it was never intended. While working with our graphic design group at Sasaki on ways to generate a dot pattern for a decorative screen, we came across some open-source software called StippleGen. Stippling is a way of creating an image by means of dots. StippleGen was created to optimize stippling for, among other things, egg painting. The software does a great job of laying out dots with greater density on the darker areas of the image while keeping a comfortable spacing between the dots. What's more, the voronoi algorithm it uses gives an irregular, organic pattern. The ah-ha moment came when I realized this could be applied to a different problem, visualizing terrain; specifically, optimizing terrain meshes in 3D software based off elevation data (a.k.a. Digital Elevation Model (DEM)).

 

Here's a typical use of StippleGen:

 
Used to create this: 
 
 

So how do we get from eggs to terrain? A given terrain, unlike an egg, is typically a mix of high variation areas, like canyons, with more uniform areas, like plains or plateaus. A typical DEM heightmap can be seen in the following image (top left) alongside some more familiar, human-readable representations of the same terrain that you might see on maps. Shaded relief is a useful trick for representing terrain in 2D where the terrain appears to be lit from one side.

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A Data Application to Foretell the Next Silicon Valley?

Can we predict what the next hub of tech entrepreneurship will be? Could we pinpoint where the next real estate boom will be and invest there? Thanks to advances in machine learning and easier access to public data through Open Data initiatives, we can now explore these types of questions.

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Making Service-Oriented Architecture Serve Data Applications

Bloor Group CEO Eric Kavanagh chatted with David King, CEO and founder of Exaptive recently. Their discussion looked at the ways in which service-oriented architecture (SOA) has and has not fulfilled it's promise, especially as it applies to working with data. Take a listen or read the transcript. 

Eric Kavanagh:  Ladies and gentlemen, hello and welcome back once again to Inside Analysis. My name is Eric Kavanagh. I’ll be your host for today’s conversation with David King. He is founder and chief executive officer of a very cool company called Exaptive. David, welcome to the show.

Dave King: Thanks for having me.

Eric: Sure thing. First of all, I’d like to just throw out a couple quick thoughts to frame the discussion here. I’m familiar with what you are doing at Exaptive, and I think it’s absolutely fascinating. In this world of enterprise software, we have these huge organizations, these very large companies, IBM, SAP, Oracle and SAS, and they’re just obviously prodigious companies building enterprise software. There’s a lot of great stuff that’s come out of that. No doubt about it, but of course, there are some pretty significant constraints. One is cost. A lot of that stuff is pretty expensive, but there are other  walls that have been built up. Some are virtual, some are metaphorical, and they make the whole process of really digging into data and analyzing data somewhat cumbersome I think.

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Data Science Wanderlust: Analyzing Global Health with Protein Sequences

Fifteen years ago, I had the unique opportunity to go on Semester at Sea, an around-the-world trip on a converted cruise ship that combined college coursework stops at nine countries on four continents. This once in a lifetime trip instilled in me a strong sense of wanderlust and a deep desire to give back to the global community.

Every Journey Begins with a Single Step

Fast-forward to a few months ago, when I joined Exaptive on an exciting new project. A large NGO enlisted us to analyze a massive set of historical data for countries. The goal: to develop a better, more granular means of grouping countries than the outdated and crude approach of "developed" and "developing." This large, complex, messy dataset and thorny problem were a great fit for my background in artificial intelligence and data science.

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The True Meaning of Catalyst, Crescendo, and Adaptation

People sometimes ask me what our company’s name means and why we chose it. The explanation often leads to discussions about similar but different terms. So I thought I’d use this blog post to explain, hopefully illuminate and while I’m at it, to correct some usage that’s bugged me for some time. Actually, let’s start right there.

I'm not sure where accuracy becomes pedantry, but there are two words - catalyst and crescendo - that instantly make my ears prick up when I hear them, only because I've heard them used incorrectly for long. One is from science and one from music, two things I tend to obsess about.

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