dynamic biomechanical simulation of thoracic cage

Introduction A client asked me to build a dynamic engineering model of the thoracic cage with which we can run “what if?” scenarios against. Applying Newton’s laws of motion and Runge-Kutta, I produced the following results. Following the videos presented below, I partially detail my methodology and propose steps for improving the work: Results The […]

Bayesian method for filtering out mRNA turnover rate bias from siRNA knockdown measurements

Abstract siRNA performance prediction calculations for a given siRNA may be divided into two broad categories: functions of the siRNA’s sequence, hereafter referred to as “intrinsic” properties of the siRNA, and functions of the target mRNA, hereafter referred to as “extrinsic” properties of the siRNA. When training a statistical or machine learning model to select […]

how I make a living: what is bioinformatics? (part #1)

I’m constantly asked to explain what I do for a living. Here is an attempt to do so in laypersons’ terms. I’ll assume my readers are non-scientists and non-engineers, but that they’ve taken a high school biology class. “Bioinformatics” is the application of mathematics and computer science to biological data, particularly molecular biology data. By […]

church to bar ratio, by U.S. county (3rd edition)

Church to bar ratio by county from U.S. Census Bureau data: The brighter the color, the higher the church to bar ratio. Counties missing data necessary for the computation are shown in black. Method From the 2013 County Business Patterns data published at http://www.census.gov/econ/cbp/download/, I extracted the number of establishments in each county that have […]

DIY Twitter analytics (part 3: hashtag network)

I’ve been mathematically analyzing my Twitter feed to determine how best to position my tweets for maximum impact, and have been documenting the work on this blog. While I’ve not come to any brilliant conclusions yet, I’ve made progress. My first post on the subject described clustering my followers by their hashtag use to see […]

DIY Twitter analytics (part 2: correlations)

I’ve been working with the Twitter API to develop my own Twitter analytics tool chain, and have been documenting the results on this blog. My last post on the subject described clustering my followers by their hashtag use to see whose tweets are most like mine. My goal of this project is to figure out best […]

DIY Twitter analytics (part 1: clustering related users)

I’ve started working with the Twitter API to develop my own Twitter analytics tool chain. My goals are to figure out who the influencers in my subjects are, figure out how best to position my tweets, etc. I could certainly pay for this service, but then I wouldn’t learn any new technical skills in the […]

graph database for heterogeneous biological data

To assist with a project I’m working on, I recently implemented a substantial portion of DisGeNET as a graph database. Furthermore, I added MeSH, OMIM, Entrez, and GO into the database to facilitate linking of data between these sources. Here I briefly describe these data sources, describe graph databases, and then show how use of […]

gene annotation database with MongoDB

After reading Datanami’s recent post “9 Must-Have Skills to Land Top Big Data Jobs in 2015” [1], I decided to round out my NoSQL knowledge by learning MongoDB. I have previously reported NoSQL work with Neo4j on this blog, where I discussed building a gene annotation graph database [2]. Here I build a similar gene […]

clustering stocks by price correlation (part 2)

In my last post, “clustering stocks by price correlation (part 1)“, I performed hierarchical clustering of NYSE stocks by correlation in weekly closing price. I expected the stocks to cluster by industry, and found that they did not. I proposed several explanations for this observation, including that perhaps I chose a poor distance metric for […]