Shotgun metagenomic analysis of the human associated microbiome provides a rich set of microbial features for prediction and biomarker discovery in the context of human diseases and health conditions. However, the use of such high-resolution microbial features presents new challenges, and validated computational tools for learning tasks are lacking. Moreover, classification rules have scarcely been validated in independent studies, posing questions about the generality and generalization of disease-predictive models across cohorts. In this paper, we comprehensively assess approaches to metagenomics-based prediction tasks and for quantitative assessment of the strength of potential microbiome-phenotype associations. We develop a computational framework for prediction tasks using quantitative microbiome profiles, including species-level relative abundances and presence of strain-specific markers. A comprehensive meta-analysis, with particular emphasis on generalization across cohorts, was performed in a collection of 2424 publicly available metagenomic samples from eight large-scale studies. Cross-validation revealed good disease-prediction capabilities, which were in general improved by feature selection and use of strain-specific markers instead of species-level taxonomic abundance. In cross-study analysis, models transferred between studies were in some cases less accurate than models tested by within-study cross-validation. Interestingly, the addition of healthy (control) samples from other studies to training sets improved disease prediction capabilities. Some microbial species (most notably Streptococcus anginosus) seem to characterize general dysbiotic states of the microbiome rather than connections with a specific disease. Our results in modelling features of the "healthy" microbiome can be considered a first step toward defining general microbial dysbiosis. The software framework, microbiome profiles, and metadata for thousands of samples are publicly available at http://segatalab.cibio.unitn.it/tools/metaml.

Given a gene expression data matrix where each cell is the expression level of a gene under a certain condition, biclustering is the problem of searching for a subset of genes that co regulate and co express only under a subset of conditions. As some genes can belong to different functional categories, searching for non-redundant overlapping biclusters is an important problem in biclustering. However, most recent algorithms can only either produce disjoint biclusters or redundant biclusters with significant overlap. In other words, these algorithms do not allow users to specify the maximum overlap between the biclusters. In this paper, we propose a novel algorithm which can generate K overlapping biclusters where the maximum overlap between them is below a predefined threshold. Unlike the other approaches which often generate all biclusters at once, our algorithm produces the biclusters sequentially, where each newly generated bicluster is guaranteed to be different from the previous ones but can still overlap with them. The experiments on real datasets confirm that different meaningful overlapping biclusters are successfully discovered. Besides, under the same constraints, our algorithm returns much larger and higher-quality biclusters compared to those of the other state-of-the art algorithms.

Supervised alternative clusterings is the problem of finding a set of clusterings which are of high quality and different from a given negative clustering. The task is therefore a clear multi-objective optimization problem. Optimizing two conflicting objectives requires dealing with trade-offs. Most approaches in the literature optimize these objectives sequentially or indirectly, resulting in solutions which are dominated. We develop a multi-objective algorithm, called COGNAC, able to optimize the objectives directly and simultaneously and producing solutions approximating the Pareto front. COGNAC performs the recombination operator at the cluster level instead of the object level as in traditional genetic algorithms. It can accept arbitrary clustering quality and dissimilarity objectives and provide solutions dominating those of other state-of-the-art algorithms. COGNAC can also be used to generate a sequence of alternative clusterings, each of which is guaranteed to be different from all previous ones.