Methods for causal inference from gene perturbation experiments and validation.

Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodology based on an invariance principle, called invariant causal prediction (ICP).

Near-optimal experimental design for model selection in systems biology.

Motivation: Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data.

Repeat or not repeat?--Statistical validation of tandem repeat prediction in genomic sequences.

Tandem repeats (TRs) represent one of the most prevalent features of genomic sequences. Due to their abundance and functional significance, a plethora of detection tools has been devised over the last two decades. Despite the longstanding interest, TR detection is still not resolved.