## Papers

## Latent Process Decomposition

Latent Process Decomposition might be one interesting unsupervised analysis approach to try on the FunGenES data. The thing is something like clustering with a model which is slightly more sophisticated than the traditional "mixture". The authors kindly provide the code and some impressive examples of successful application of the method in their paper, so although the conceptual part of the algorithm is heavily mathematical, it might be possible to just try running it on the data with a reasonably small effort.

## Apriori Revisited

A paper by T. de Bie et al. describes one very stylish application of the Apriori algorithm for detection of transcription regulatory modules. The idea is in the smart statement of the problem, which is the following:

Find the

maximalsets of genes that all shareat least rcommon regulators,at least mcommon motifs, and have pairwise correlation ofat least c.

It turns out that the sets of genes of interest naturally satisfy the same properties as the frequent sets in the Apriori algorithm, so it's rather easy to adapt the algorithm for this context.

## An Improved Map of Conserved Regulatory Sites

You might still remember this paper by Harbison et al. that reported some high-quality S.cerevisiae TF binding sites. Well, now there's a followup by Maclsaac et al.. This time the authors used phylogenetic conservation based algorithms (PhyloCon and Converge) to search for binding sites, and reportedly got even better results than before.

Moreover, the authors provide a nice Python package TAMO for performing basic PWM-matching tasks.