References of "Vlassis, Nikos 40021183"
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See detailFast Correspondences for Statistical Shape Models of Brain Structures
Bernard, Florian UL; Vlassis, Nikos UL; Gemmar, Peter et al

in SPIE Medical Imaging (2016, March)

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See detailImproved Parkinson’s disease classification from diffusion MRI data by Fisher vector descriptors
Salamanca Mino, Luis UL; Vlassis, Nikos UL; Diederich, Nico UL et al

in Improved Parkinson’s disease classification from diffusion MRI data by Fisher vector descriptors (2015, October)

Due to the complex clinical picture of Parkinson’s disease (PD), the reliable diagnosis of patients is still challenging. A promising approach is the structural characterization of brain areas affected in ... [more ▼]

Due to the complex clinical picture of Parkinson’s disease (PD), the reliable diagnosis of patients is still challenging. A promising approach is the structural characterization of brain areas affected in PD by diffusion magnetic resonance imaging (dMRI). Standard classification methods depend on an accurate non-linear alignment of all images to a common reference template, and are challenged by the resulting huge dimensionality of the extracted feature space. Here, we propose a novel diagnosis pipeline based on the Fisher vector algorithm. This technique allows for a precise encoding into a high-level descriptor of standard diffusion measures like the fractional anisotropy and the mean diffusivity, extracted from the regions of interest (ROIs) typically involved in PD. The obtained low dimensional, fixed-length descriptors are independent of the image alignment and boost the linear separability of the problem in the description space, leading to more efficient and accurate diagnosis. In a test cohort of 50 PD patients and 50 controls, the implemented methodology outperforms previous methods when using a logistic linear regressor for classification of each ROI independently, which are subsequently combined into a single classification decision. [less ▲]

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See detailFastMotif: Spectral Sequence Motif Discovery
Colombo, Nicolo UL; Vlassis, Nikos UL

in Bioinformatics (2015)

Motivation: Sequence discovery tools play a central role in several fields of computational biology. In the framework of Transcription Factor binding studies, most of the existing motif finding algorithms ... [more ▼]

Motivation: Sequence discovery tools play a central role in several fields of computational biology. In the framework of Transcription Factor binding studies, most of the existing motif finding algorithms are computationally demanding, and they may not be able to support the increasingly large datasets produced by modern high-throughput sequencing technologies. Results: We present FastMotif, a new motif discovery algorithm that is built on a recent machine learning technique referred to as Method of Moments. Based on spectral decompositions, our method is robust to model misspecifications and is not prone to locally optimal solutions. We obtain an algorithm that is extremely fast and designed for the analysis of big sequencing data. On HT-Selex data, FastMotif extracts motif profiles that match those computed by various state-of- the-art algorithms, but one order of magnitude faster. We provide a theoretical and numerical analysis of the algorithm’s robustness and discuss its sensitivity with respect to the free parameters. [less ▲]

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See detailVizBin - an application for reference-independent visualization and human-augmented binning of metagenomic data
Laczny, Cedric Christian UL; Sternal, Tomasz; Plugaru, Valentin UL et al

in Microbiome (2015)

Background Metagenomics is limited in its ability to link distinct microbial populations to genetic potential due to a current lack of representative isolate genome sequences. Reference-independent ... [more ▼]

Background Metagenomics is limited in its ability to link distinct microbial populations to genetic potential due to a current lack of representative isolate genome sequences. Reference-independent approaches, which exploit for example inherent genomic signatures for the clustering of metagenomic fragments (binning), offer the prospect to resolve and reconstruct population-level genomic complements without the need for prior knowledge. Results We present VizBin, a Java™-based application which offers efficient and intuitive reference-independent visualization of metagenomic datasets from single samples for subsequent human-in-the-loop inspection and binning. The method is based on nonlinear dimension reduction of genomic signatures and exploits the superior pattern recognition capabilities of the human eye-brain system for cluster identification and delineation. We demonstrate the general applicability of VizBin for the analysis of metagenomic sequence data by presenting results from two cellulolytic microbial communities and one human-borne microbial consortium. The superior performance of our application compared to other analogous metagenomic visualization and binning methods is also presented. Conclusions VizBin can be applied de novo for the visualization and subsequent binning of metagenomic datasets from single samples, and it can be used for the post hoc inspection and refinement of automatically generated bins. Due to its computational efficiency, it can be run on common desktop machines and enables the analysis of complex metagenomic datasets in a matter of minutes. The software implementation is available at https://claczny.github.io/VizBin under the BSD License (four-clause) and runs under Microsoft Windows™, Apple Mac OS X™ (10.7 to 10.10), and Linux. [less ▲]

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See detailGenePEN: analysis of network activity alterations in complex diseases via the pairwise elastic net
Vlassis, Nikos UL; Glaab, Enrico UL

in Statistical Applications in Genetics and Molecular Biology (2015), 14(2), 221-224

Complex diseases are often characterized by coordinated expression alterations of genes and proteins which are grouped together in a molecular network. Identifying such interconnected and jointly altered ... [more ▼]

Complex diseases are often characterized by coordinated expression alterations of genes and proteins which are grouped together in a molecular network. Identifying such interconnected and jointly altered gene/protein groups from functional omics data and a given molecular interaction network is a key challenge in bioinformatics. <br />We describe GenePEN, a penalized logistic regression approach for sample classification via convex optimization, using a newly designed Pairwise Elastic Net penalty that favors the selection of discriminative genes/proteins according to their connectedness in a molecular interaction graph. An efficient implementation of the method finds provably optimal solutions on high-dimensional omics data in a few seconds and is freely available at http://lcsb-portal.uni.lu/bioinformatics.Complex diseases are often characterized by coordinated expression alterations of genes and proteins which are grouped together in a molecular network. Identifying such interconnected and jointly altered gene/protein groups from functional omics data and a given molecular interaction network is a key challenge in bioinformatics. <br />We describe GenePEN, a penalized logistic regression approach for sample classification via convex optimization, using a newly designed Pairwise Elastic Net penalty that favors the selection of discriminative genes/proteins according to their connectedness in a molecular interaction graph. An efficient implementation of the method finds provably optimal solutions on high-dimensional omics data in a few seconds and is freely available at http://lcsb-portal.uni.lu/bioinformatics. [less ▲]

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See detailCommunity-integrated omics links dominance of a microbial generalist to fine-tuned resource usage
Muller, Emilie UL; Pinel, Nicolas; Laczny, Cedric Christian UL et al

in Nature Communications (2014)

Microbial communities are complex and dynamic systems that are primarily structured according to their members’ ecological niches. To investigate how niche breadth (generalist versus specialist lifestyle ... [more ▼]

Microbial communities are complex and dynamic systems that are primarily structured according to their members’ ecological niches. To investigate how niche breadth (generalist versus specialist lifestyle strategies) relates to ecological success, we develop and apply an integrative workflow for the multi-omic analysis of oleaginous mixed microbial communities from a biological wastewater treatment plant. Time- and space-resolved coupled metabolomic and taxonomic analyses demonstrate that the community-wide lipid accumulation phenotype is associated with the dominance of the generalist bacterium Candidatus Microthrix spp. By integrating population-level genomic reconstructions (reflecting fundamental niches) with transcriptomic and proteomic data (realised niches), we identify finely tuned gene expression governing resource usage by Candidatus Microthrix parvicella over time. Moreover, our results indicate that the fluctuating environmental conditions constrain the accumulation of genetic variation in Candidatus Microthrix parvicella likely due to fitness trade-offs. Based on our observations, niche breadth has to be considered as an important factor for understanding the evolutionary processes governing (microbial) population sizes and structures in situ. [less ▲]

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See detailPolytopic uncertainty for linear systems: New and old complexity results
Vlassis, Nikos UL; Jungers, Raphaël

in Systems & Control Letters (2014), 67

We survey the problem of deciding the stability or stabilizability of uncertain linear systems whose region of uncertainty is a polytope. This natural setting has applications in many fields of applied ... [more ▼]

We survey the problem of deciding the stability or stabilizability of uncertain linear systems whose region of uncertainty is a polytope. This natural setting has applications in many fields of applied science, from Control Theory to Systems Engineering to Biology. We focus on the algorithmic decidability of this property when one is given a particular polytope. This setting gives rise to several different algorithmic questions, depending on the nature of time (discrete/continuous), the property asked (stability/stabilizability), or the type of uncertainty (fixed/switching). Several of these questions have been answered in the literature in the last thirty years. We point out the ones that have remained open, and we answer all of them, except one which we raise as an open question. In all the cases, the results are negative in the sense that the questions are NP-hard. As a byproduct, we obtain complexity results for several other matrix problems in Systems and Control. [less ▲]

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See detailAlignment-free Visualization of Metagenomic Data by Nonlinear Dimension Reduction
Laczny, Cedric Christian UL; Pinel, Nicolás; Vlassis, Nikos UL et al

in Scientific Reports (2014)

The visualization of metagenomic data, especially without prior taxonomic identification of reconstructed genomic fragments, is a challenging problem in computational biology. An ideal visualization ... [more ▼]

The visualization of metagenomic data, especially without prior taxonomic identification of reconstructed genomic fragments, is a challenging problem in computational biology. An ideal visualization method should, among others, enable clear distinction of congruent groups of sequences of closely related taxa, be applicable to fragments of lengths typically achievable following assembly, and allow the efficient analysis of the growing amounts of community genomic sequence data. Here, we report a scalable approach for the visualization of metagenomic data that is based on nonlinear dimension reduction via Barnes-Hut Stochastic Neighbor Embedding of centered log-ratio transformed oligonucleotide signatures extracted from assembled genomic sequence fragments. The approach allows for alignment-free assessment of the data-inherent taxonomic structure, and it can potentially facilitate the downstream binning of genomic fragments into uniform clusters reflecting organismal origin. We demonstrate the performance of our approach by visualizing community genomic sequence data from simulated as well as groundwater, human-derived and marine microbial communities. [less ▲]

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See detailFast reconstruction of compact context-specific metabolic network models
Vlassis, Nikos UL; Pacheco, Maria Irene UL; Sauter, Thomas UL

in PLoS Computational Biology (2014), 10(1), 1003424

Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can ... [more ▼]

Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present FASTCORE, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. FASTCORE takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue), and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and FASTCORE iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, FASTCORE can form the backbone of many future metabolic network reconstruction algorithms. [less ▲]

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See detailSpectral Sequence Motif Discovery
Colombo, Nicolo UL; Vlassis, Nikos UL

E-print/Working paper (2014)

Sequence discovery tools play a central role in several fields of computational biology. In the framework of Transcription Factor binding studies, motif finding algorithms of increasingly high performance ... [more ▼]

Sequence discovery tools play a central role in several fields of computational biology. In the framework of Transcription Factor binding studies, motif finding algorithms of increasingly high performance are required to process the big datasets produced by new high-throughput sequencing technologies. Most existing algorithms are computationally demanding and often cannot support the large size of new experimental data. We present a new motif discovery algorithm that is built on a recent machine learning technique, referred to as Method of Moments. Based on spectral decompositions, this method is robust under model misspecification and is not prone to locally optimal solutions. We obtain an algorithm that is extremely fast and designed for the analysis of big sequencing data. In a few minutes, we can process datasets of hundreds of thousand sequences and extract motif profiles that match those computed by various state-of-the-art algorithms. [less ▲]

Detailed reference viewed: 50 (3 UL)
See detailCommunity integrated omics links the dominance of a microbial generalist to fine-tuned resource usage
Muller, Emilie UL; Pinel, Nicolás; Laczny, Cedric Christian UL et al

Scientific Conference (2014)

Microbial communities are complex and dynamic systems that are influenced by stochastic-neutral processes but are mainly structured by resource availability and usage. High-resolution “meta-omics” offer ... [more ▼]

Microbial communities are complex and dynamic systems that are influenced by stochastic-neutral processes but are mainly structured by resource availability and usage. High-resolution “meta-omics” offer exciting prospects to investigate microbial populations in their native environment. In particular, integrated meta-omics, by allowing simultaneous resolution of fundamental niches (genomics) and realised niches (transcriptomics, proteomics and metabolomics), can resolve microbial lifestyles (generalist versus specialist lifestyle strategies) in situ. We have recently developed the necessary wet- and dry-lab methodologies to carry out systematic molecular measurements of microbial consortia over space and time, and to integrate and analyse the resulting data at the population-level. We applied these methods to oleaginous mixed microbial communities located on the surface of anoxic biological wastewater treatment tanks to investigate how niche breadth (generalist versus specialist lifestyle strategies) relates to community-level phenotypes and ecological success (i.e. population size). Coupled metabolomics and 16S rRNA gene-based deep sequencing demonstrate that the community-wide lipid accumulation phenotype is associated with the dominance of Candidatus Microthrix parvicella. By integrating population-level genomic reconstructions with transcriptomic and proteomic data, we found that the dominance of this microbial generalist population results from finely tuned resource usage and optimal foraging behaviour. Moreover, the fluctuating environmental conditions constrain the accumulation of variations, leading to a genetically homogeneous population likely due to fitness trade-offs. By integrating metagenomic, metatranscriptomic, metaproteomic and metabolomic information, we demonstrate that natural microbial population sizes and structures are intricately linked to resource usage and that differing microbial lifestyle strategies may explain the varying degrees of within-population genetic heterogeneity observed in metagenomic datasets. Elucidating the exact mechanism driving fitness trade-offs, e.g., antagonistic pleiotropy or others, will require additional integrated omic datasets to be generated from samples taken over space and time. Based on our observations, niche breadth and lifestyle strategies (generalists versus specialists) have to be considered as important factors for understanding the evolutionary processes governing microbial population sizes and structures in situ. [less ▲]

Detailed reference viewed: 105 (9 UL)
See detailCommunity integrated omics links the dominance of a microbial generalist to fine-tuned resource usage
Muller, Emilie UL; Pinel, Nicolás; Laczny, Cedric Christian UL et al

Poster (2014)

Microbial communities are complex and dynamic systems that are influenced by stochastic-neutral processes but are mainly structured by resource availability and usage. High-resolution “meta-omics” offer ... [more ▼]

Microbial communities are complex and dynamic systems that are influenced by stochastic-neutral processes but are mainly structured by resource availability and usage. High-resolution “meta-omics” offer exciting prospects to investigate microbial populations in their native environment. In particular, integrated meta-omics, by allowing simultaneous resolution of fundamental niches (genomics) and realised niches (transcriptomics, proteomics and metabolomics), can resolve microbial lifestyles strategies (generalist versus specialist) in situ. We have recently developed the necessary wet- and dry-lab methodologies to carry out systematic molecular measurements of microbial consortia over space and time, and to integrate and analyse the resulting data at the population-level. We applied these methods to oleaginous mixed microbial communities located on the surface of anoxic biological wastewater treatment tanks to investigate how niche breadth (generalist versus specialist strategies) relates to community-level phenotypes and ecological success (i.e. population size). Coupled metabolomics and 16S rRNA gene-based deep sequencing demonstrate that the community-wide lipid accumulation phenotype is associated with the dominance of Candidatus Microthrix parvicella. By integrating population-level genomic reconstructions with transcriptomic and proteomic data, we found that the dominance of this microbial generalist population results from finely tuned resource usage and optimal foraging behaviour. Moreover, the fluctuating environmental conditions constrain the accumulation of variations, leading to a genetically homogeneous population likely due to fitness trade-offs. By integrating metagenomic, metatranscriptomic, metaproteomic and metabolomic information, we demonstrate that natural microbial population sizes and structures are intricately linked to resource usage and that differing microbial lifestyle strategies may explain the varying degrees of within-population genetic heterogeneity observed in metagenomic datasets. Elucidating the exact mechanism driving fitness trade-offs, e.g., antagonistic pleiotropy or others, will require additional integrated omic datasets to be generated from samples taken over space and time. Based on our observations, niche breadth and lifestyle strategies (generalists versus specialists) have to be considered as important factors for understanding the evolutionary processes governing microbial population sizes and structures in situ. [less ▲]

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See detailFASTGAPFILL: Efficient gap filling in metabolic networks
Thiele, Ines UL; Vlassis, Nikos UL; Fleming, Ronan MT UL

in Bioinformatics (2014), 30(17), 2529-2531

Motivation: Genome-scale metabolic reconstructions summarize current knowledge about a target organism in a structured manner and as such highlight missing information. Such gaps can be filled ... [more ▼]

Motivation: Genome-scale metabolic reconstructions summarize current knowledge about a target organism in a structured manner and as such highlight missing information. Such gaps can be filled algorithmically. Scalability limitations of available algorithms for gap filling hinder their application to compartmentalized reconstructions. Results:We present FASTGAPFILL, a computationally efficient,tractable extension to the COBRA toolbox that permits theidentification of candidate missing knowledge from a universal biochemical reaction database (e.g., KEGG) for a given (compart-mentalized) metabolic reconstruction. The stoichiometric consistency of the universal reaction database and of the metabolic reconstruction can be tested for permitting the computation of biologically more relevant solutions. We demonstrate the efficiency and scalability of fastGapFill on a range of metabolic reconstructions. [less ▲]

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See detailNetwork deregulation analysis in complex diseases via the pairwise elastic net
Vlassis, Nikos UL; Glaab, Enrico UL

in Proc 8th BeNeLux Bioinformatics Conference (2013)

Complex diseases like neurodegenerative or cancer disorders are characterized by deregulations in multiple genes and proteins. Previous research has shown that neighboring genes in a molecular network ... [more ▼]

Complex diseases like neurodegenerative or cancer disorders are characterized by deregulations in multiple genes and proteins. Previous research has shown that neighboring genes in a molecular network tend to undergo coordinated expression changes. We describe an approach that allows identifying such jointly differentially expressed genes from input expression data and a graph encoding pairwise functional associations between genes (such as protein interactions). We cast this as a feature selection problem in penalized two-class (cases vs. controls) classification, and we propose a novel Pairwise Elastic Net penalty that favors the selection of discriminative genes according to their connectedness in the interaction graph. Experiments on microarray gene expression data for Parkinson’s disease demonstrate marked improvements in feature grouping over competitive methods. [less ▲]

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See detailCondensing the omics fog of microbial communities
Muller, Emilie UL; Glaab, Enrico UL; May, Patrick UL et al

in Trends in Microbiology (2013), 21(7), 325333

Natural microbial communities are ubiquitous, complex, heterogeneous and dynamic. Here, we argue that the future standard for their study will require systematic omic measurements of spatially and ... [more ▼]

Natural microbial communities are ubiquitous, complex, heterogeneous and dynamic. Here, we argue that the future standard for their study will require systematic omic measurements of spatially and temporally resolved unique samples in line with a discovery-driven planning approach. Resulting datasets will allow the generation of solid hypotheses about causal relationships and, thereby, will facilitate the discovery of previously unknown traits of specific microbial community members. However, to achieve this, solid wet-lab, bioinformatic and statistical methodologies are required to have the promises of the emerging field of Eco-Systems Biology come to fruition. [less ▲]

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See detailTies between graph theory and biology
Blazewicz, Jacek; Kasprzak, Marta; Vlassis, Nikos UL

in Gross, Jonathan L.; Yellen, Jay; Zhang, Ping (Eds.) Handbook of Graph Theory (2013)

Last decades brought us a new scientific area of computational biology, placed at the junction of biology (especially molecular biology), computer science and mathematics. Its aim is to solve real-world ... [more ▼]

Last decades brought us a new scientific area of computational biology, placed at the junction of biology (especially molecular biology), computer science and mathematics. Its aim is to solve real-world problems arising in biology with the use of mathematical models and methods, and tools from computer science. Molecular biology, due to its rapid progress, yields more and more experimental data, possible to be processed on computers only. Efficient processing and advisable analysis must be accompanied by well suited models and methods, the ones coming from graph theory frequently appeared to be most useful. Here, the most interesting and breakthrough approaches of computational biology tied with graph theory are characterized. [less ▲]

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See detailFastcore: An algorithm for fast reconstruction of context-specific metabolic network models
Vlassis, Nikos UL; Pacheco, Maria Irene UL; Sauter, Thomas UL

in Proc. 8th BeNeLux Bioinformatics Conference (2013)

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See detailFunctional Genomics, Proteomics, Metabolomics and Bioinformatics for Systems Biology
Ballereau, S.; Glaab, Enrico UL; Kolodkin, Alexey UL et al

in Prokop, Ales; Csukás, Bela (Eds.) Systems Biology: Integrative Biology and Simulation Tools (2013)

This chapter introduces systems biology, its context, aims, concepts and strategies. It then describes approaches and methods used for collection of high-dimensional structural and functional genomics ... [more ▼]

This chapter introduces systems biology, its context, aims, concepts and strategies. It then describes approaches and methods used for collection of high-dimensional structural and functional genomics data, including epigenomics, transcriptomics, proteomics, metabolomics and lipidomics, and how recent technological advances in these fields have moved the bottleneck from data production to data analysis and bioinformatics. Finally, the most advanced mathematical and computational methods used for clustering, feature selection, prediction analysis, text mining and pathway analysis in functional genomics and systems biology are reviewed and discussed in the context of use cases. [less ▲]

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See detailFast reconstruction of compact context-specific metabolic network models
Vlassis, Nikos UL; Pacheco, Maria Irene UL; Sauter, Thomas UL

E-print/Working paper (2013)

Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can ... [more ▼]

Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present FASTCORE, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. FASTCORE takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue), and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and FASTCORE iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a chief rival method. Given its simplicity and its excellent performance, FASTCORE can form the backbone of many future metabolic network reconstruction algorithms. [less ▲]

Detailed reference viewed: 69 (14 UL)