Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning.

The function of most genes is unknown. The best results in automated function prediction are obtained with machine learning-based methods that combine multiple data sources, typically sequence derived features, protein structure and interaction data. Even though there is ample evidence showing that a gene's function is not independent of its location, the few available examples of gene function prediction based on gene location rely on sequence identity between genes of different organisms and are thus subjected to the limitations of the relationship between sequence and function.

Bernoulli and binomial proliferation on evolutionary graphs.

In this paper we introduce random proliferation models on graphs. We consider two types of particles: type-1/mutant/invader/red particles proliferates on a population of type-2/wild-type/resident/blue particles. Unlike the well-known Moran model on graphs -as introduced in Lieberman et al.

An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach.

Background: Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Previously, we had trained an ensemble machine learning model to assign a probability of having synaptic function to every protein-coding gene in Drosophila melanogaster. This approach resulted in the publication of a catalogue of 893 genes which we postulated to be very enriched in genes with a still undocumented synaptic function.

Putative synaptic genes defined from a Drosophila whole body developmental transcriptome by a machine learning approach.

Background: Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Although roughly a thousand genes are expected to be important for this function in Drosophila melanogaster, just a few hundreds of them are known so far.

Results: In this work we trained three learning algorithms to predict a "synaptic function" for genes of Drosophila using data from a whole-body developmental transcriptome published by others.

A stochastic microscopic model for the dynamics of antigenic variation.

We present a novel model that describes the within-host evolutionary dynamics of parasites undergoing antigenic variation. The approach uses a multi-type branching process with two types of entities defined according to their relationship with the immune system: clans of resistant parasitic cells (i.e.