## Workshop in NYC on challenges in microbiome data analysis + poster of my work

I just got back from an excellent conference which was organised by the Simons foundation, Eric Alm’s lab and the Bonneau lab. It was mainly about the technical details of microbiome data analysis and the statistical challenges this kind of data presents us with. We heard some amazing talks about how to deal with compositional data (almost all microbiome data is inherently compositional due the sampling procedure), …

Workshop in NYC on challenges in microbiome data analysis + poster of my work

## Newton’s method with 10 lines of Python

I’m starting a new series of blog posts, called “XY in less than 10 lines of Python“. This first one is about Newton’s method, which is an old numerical approximation technique that could be used to find the roots of complex polynomials and any differentiable function. Let’s say we have a complicated polynomial: and we want to find its roots. Unfortunately we know from the Galois …

Newton’s method with 10 lines of Python

## Solving “real world” problems with SymPy

SymPy is an amazing library for symbolic mathematics in Python. It’s like Mathematica, and its online shell version along with SymPy Gamma  is pretty much like Wolfram Alpha (WA). OK, I know you can ask WA some pretty cool questions, but let’s face it, most of use just want to find the derivative of a function, or simplify an expression, and not …

Solving “real world” problems with SymPy

## MIFS – parallelized Mutual Information based Feature Selection module

danielhomola Blog 1 Comment

TL,DR: I wrapped up three mutual information based feature selection methods in a scikit-learn like module. You can find it on my GitHub. It is very easy to use, you can run the example.py or import it into your project and apply it to your data like any other scikit-learn method.

Mutual information based filter methods The following bit is adopted …

MIFS – parallelized Mutual Information based Feature Selection module