artificial intelligence in fashion (part two: a first step)

In my last post, “artificial intelligence in fashion (part one: brainstorming)“, I produced a list of big ideas on how machine learning and artificial intelligence may be applied to the fashion industry. I addressed sizing, marketing, and design activities when brainstorming this list. This post doesn’t specifically cover an artificial intelligence solution, but it lays […]

artificial intelligence in fashion (part one: brainstorming)

Brainstorming as usual: Fashion dictums involve many IF-THEN-ELSE rules. One can convert this into a decision engine (inference engine). User specifies their body shape, and a recommendation engine selects suitable clothing for them, taking into account the user’s tastes. Upload an image of a dress you want to buy, and specify the dress’s given size. […]

rapidly identifying potential CRISPR/Cas9 off-target sites (part one)

Before we can score segments in the genome having a small number of mismatches to a CRISPR for their off-target risk, we must first find these segments. Searching for every possible mismatch permutation proves computationally expensive, so we apply the following heuristic: We only search for mismatches in the top positions relevant to CRISPR efficiency. […]

pseudo-harmonic FOREX prediction with machine learning (part one)

“Harmonic” trading methods seek patterns in the relationships between neighboring peaks and valleys in the time series. Particularly, harmonic traders seek pre-specified ratios in the price differences among a series of peaks and valleys. For example, a trader might observe the following pattern: Let A, B, C, D, and E be the points in the […]

picking stocks by graph database (part 2: machine learning)

In our last post, we demonstrated a graph database created to enable study of the stock market, particularly the study of causality relationships. So how to proceed from there? At this stage we want to pick winning stocks, not write an academic paper, so our focus turns toward practical machine learning. Source Data We start […]

picking stocks by graph database (part one)

Historical stock price data comes readily available at daily resolution. So we calculated the Granger causality for each pair of stocks we hold data for, at one and two day lags (testing the question “does daily percent change in volume for stock X Granger cause daily percent change in adjusted close price for stock Y?”). […]

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 […]

fast genomic coordinate comparison using PostgreSQL’s geometric operators

PostgreSQL provides operators for comparing geometric data types, for example for computing whether two boxes overlap or whether one box contains another. Such operators are quick compared to similar calculations implemented using normal comparison operators, which I’ll demonstrate below. Here I show use of such geometric data types and operators for determining whether one segment […]

Bayesian network modeling stock price change

Update 29 April 2018 I suspect this result is erroneous in that the graph often shows two arrows between any two given nodes, one inward and one outward. I’ll investigate this further and get back to you… – Emily Introduction Taking a cue from the systems biology folks, I decided to model stock price change […]