2,200 research outputs found
Differential analysis of biological networks
In cancer research, the comparison of gene expression or DNA methylation
networks inferred from healthy controls and patients can lead to the discovery
of biological pathways associated to the disease. As a cancer progresses, its
signalling and control networks are subject to some degree of localised
re-wiring. Being able to detect disrupted interaction patterns induced by the
presence or progression of the disease can lead to the discovery of novel
molecular diagnostic and prognostic signatures. Currently there is a lack of
scalable statistical procedures for two-network comparisons aimed at detecting
localised topological differences. We propose the dGHD algorithm, a methodology
for detecting differential interaction patterns in two-network comparisons. The
algorithm relies on a statistic, the Generalised Hamming Distance (GHD), for
assessing the degree of topological difference between networks and evaluating
its statistical significance. dGHD builds on a non-parametric permutation
testing framework but achieves computationally efficiency through an asymptotic
normal approximation. We show that the GHD is able to detect more subtle
topological differences compared to a standard Hamming distance between
networks. This results in the dGHD algorithm achieving high performance in
simulation studies as measured by sensitivity and specificity. An application
to the problem of detecting differential DNA co-methylation subnetworks
associated to ovarian cancer demonstrates the potential benefits of the
proposed methodology for discovering network-derived biomarkers associated with
a trait of interest
An introduction to spectral distances in networks (extended version)
Many functions have been recently defined to assess the similarity among
networks as tools for quantitative comparison. They stem from very different
frameworks - and they are tuned for dealing with different situations. Here we
show an overview of the spectral distances, highlighting their behavior in some
basic cases of static and dynamic synthetic and real networks
An inferential framework for biological network hypothesis tests
Background
Networks are ubiquitous in modern cell biology and physiology. A large literature exists for inferring/proposing biological pathways/networks using statistical or machine learning algorithms. Despite these advances a formal testing procedure for analyzing network-level observations is in need of further development. Comparing the behaviour of a pharmacologically altered pathway to its canonical form is an example of a salient one-sample comparison. Locating which pathways differentiate disease from no-disease phenotype may be recast as a two-sample network inference problem. Results
We outline an inferential method for performing one- and two-sample hypothesis tests where the sampling unit is a network and the hypotheses are stated via network model(s). We propose a dissimilarity measure that incorporates nearby neighbour information to contrast one or more networks in a statistical test. We demonstrate and explore the utility of our approach with both simulated and microarray data; random graphs and weighted (partial) correlation networks are used to form network models. Using both a well-known diabetes dataset and an ovarian cancer dataset, the methods outlined here could better elucidate co-regulation changes for one or more pathways between two clinically relevant phenotypes. Conclusions
Formal hypothesis tests for gene- or protein-based networks are a logical progression from existing gene-based and gene-set tests for differential expression. Commensurate with the growing appreciation and development of systems biology, the dissimilarity-based testing methods presented here may allow us to improve our understanding of pathways and other complex regulatory systems. The benefit of our method was illustrated under select scenarios
A multi-species functional embedding integrating sequence and network structure
A key challenge to transferring knowledge between species is that different species have fundamentally different genetic architectures. Initial computational approaches to transfer knowledge across species have relied on measures of heredity such as genetic homology, but these approaches suffer from limitations. First, only a small subset of genes have homologs, limiting the amount of knowledge that can be transferred, and second, genes change or repurpose functions, complicating the transfer of knowledge. Many approaches address this problem by expanding the notion of homology by leveraging high-throughput genomic and proteomic measurements, such as through network alignment. In this work, we take a new approach to transferring knowledge across species by expanding the notion of homology through explicit measures of functional similarity between proteins in different species. Specifically, our kernel-based method, HANDL (Homology Assessment across Networks using Diffusion and Landmarks), integrates sequence and network structure to create a functional embedding in which proteins from different species are embedded in the same vector space. We show that inner products in this space and the vectors themselves capture functional similarity across species, and are useful for a variety of functional tasks. We perform the first whole-genome method for predicting phenologs, generating many that were previously identified, but also predicting new phenologs supported from the biological literature. We also demonstrate the HANDL embedding captures pairwise gene function, in that gene pairs with synthetic lethal interactions are significantly separated in HANDL space, and the direction of separation is conserved across species. Software for the HANDL algorithm is available at http://bit.ly/lrgr-handl.Published versio
Modelling and recognition of protein contact networks by multiple kernel learning and dissimilarity representations
Multiple kernel learning is a paradigm which employs a properly constructed chain of kernel functions able to simultaneously analyse different data or different representations of the same data. In this paper, we propose an hybrid classification system based on a linear combination of multiple kernels defined over multiple dissimilarity spaces. The core of the training procedure is the joint optimisation of kernel weights and representatives selection in the dissimilarity spaces. This equips the system with a two-fold knowledge discovery phase: by analysing the weights, it is possible to check which representations are more suitable for solving the classification problem, whereas the pivotal patterns selected as representatives can give further insights on the modelled system, possibly with the help of field-experts. The proposed classification system is tested on real proteomic data in order to predict proteins' functional role starting from their folded structure: specifically, a set of eight representations are drawn from the graph-based protein folded description. The proposed multiple kernel-based system has also been benchmarked against a clustering-based classification system also able to exploit multiple dissimilarities simultaneously. Computational results show remarkable classification capabilities and the knowledge discovery analysis is in line with current biological knowledge, suggesting the reliability of the proposed system
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