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 […]

SWIG, C++, Python, and Monte-Carlo simulation

In the previous post, I introduced MCS-libre, my C++ library for Monte-Carlo simulation. Here I show how to access it from Python using the Simplified Wrapper and Interface Generator (SWIG), while in the process demonstrating how to use SWIG with C++ classes. First we download and decompress the MCS-libre library code: Next, we create a SWIG […]

monte-carlo simulation in C++ with MCS-libre

Monte-Carlo simulation is a sometimes elegant (and sometimes crude) method for simulating complex systems. Parameters that affect the system are selected from random distributions and the system response to these values is then calculated. Repeating this process many times produces often useful information about the system. The method is especially useful for examining non-linear systems […]