Pierre Mahé

TL;DR: In this article, the authors investigate the potential of modern, large-scale machine learning implementations for taxonomic affectation of next-generation sequencing reads based on their k-mers profile, and show that machine learning-based compositional approaches benefit from increasing the number of fragments sampled from reference genome to tune their parameters, up to a coverage of about 10, and from increasing k-mer size to about 12.

TL;DR: A new method to infer the composition of polymicrobial samples on the basis of a single mass spectrum using a penalized non-negative linear regression framework making use of species-specific prototypes, which can be derived directly from the routine reference database of pure spectra.

TL;DR: In this paper, a computer implemented method and an apparatus for comparing spans of text are disclosed, which includes computing a similarity measure between a first sequence of symbols representing a first text span and a second sequence representing a second text span as a function of the occurrences of optionally noncontiguous subsequences of symbols shared by the two sequences of symbols.

TL;DR: This work relies on a k-mer based genotyping scheme and a logistic regression model, thereby combining several k-mers into a probabilistic model, and demonstrates that stability selection is a powerful approach to investigate bacterial genotype-phenotype relationships.