WorldCat Identities

Prulj, Nataa

Overview
Works: 25 works in 38 publications in 2 languages and 168 library holdings
Genres: Academic theses 
Roles: Editor, Author, Contributor
Classifications: R858, 610.285
Publication Timeline
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Most widely held works by Nataa Prulj
Analyzing network data in biology and medicine : an interdisciplinary textbook for biological, medical and computational scientists( )

9 editions published in 2019 in English and held by 124 WorldCat member libraries worldwide

"The increased and widespread availability of large network data resources in recent years has resulted in a growing need for effective methods for their analysis. The challenge is to detect patterns that provide a better understanding of the data. However, this is not a straightforward task because of the size of the data sets and the computer power required for the analysis. The solution is to devise methods for approximately answering the questions posed, and these methods will vary depending on the data sets under scrutiny. This ... text introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, before discussing the thought processes and creativity involved in the analysis of large-scale biological and medical data sets, using a wide range of real-life examples. Bringing together leading experts, this text provides an ... introduction to and insight into the interdisciplinary field of network data analysis in biomedicine."--Provided by publisher
Analyzing large biological networks : protein-protein interactions example by Nataša Prz̆ulj( Book )

2 editions published between 2005 and 2006 in English and held by 6 WorldCat member libraries worldwide

Understanding the inner workings of the cell constitutes the foremast fundamental problem of modern biology. The information contained in large protein-protein interaction (PPI) networks is being exploited for understanding the cell and developing new drugs. Currently available PPI networks of model organisms, containing thousands of nodes (proteins) and tens of thousands of edges (interactions), are noisy and largely incomplete. PPI networks of higher organisms will be much larger. As these data sets grow, it is important that our models keep representing the data well, since the models can be used for data cleaning and experimental planning. We measure local structural properties of a PPI network by finding and counting all instances of 3-, 4-, and 5-node connected induced subgraphs, called graphlets. We compare the graphlet frequency distributions of the PPI and various model networks with the same number of nodes and edges as the PPI network. Using this measure of local network structure, we show that PPI networks are better modeled by geometric random graphs than by previously proposed models, including scale-free networks. In a geometric random graph, nodes correspond to uniformly randomly distributed points in a metric space; two nodes are adjacent in the graph if they are close enough in the metric space. We use this new model to develop efficient and scalable heuristic algorithms for estimating graphlet frequency distribution patterns in PPI and geometric random networks. The currently accepted scale-free model of PPI networks is based on global statistical properties of PPI networks. However, global measures are very weak, since qualitatively different graphs can have equal values in these measures. It is possible that the observed global proper ties of PPI networks are an artifact of noisy, high-throughput experimental techniques used to detect PPIs, as well as the incompleteness of the data. Fortunately, some parts of PPI networks have been extensively studied due to their importance for basic biological function and human disease. We expect the local structural properties of these highly studied portions of PPI networks to give a much better indication of the true structure of PPI networks. Therefore, we use a sensitive, local structure approach
Minimal hereditary dominating pair graphs by Nataša Prz̆ulj( Book )

3 editions published between 2000 and 2001 in English and held by 5 WorldCat member libraries worldwide

Towards a data-integrated cell by Noël Malod-Dognin( )

2 editions published in 2019 in English and held by 3 WorldCat member libraries worldwide

We are increasingly accumulating molecular data about a cell. The challenge is how to integrate them within a unified conceptual and computational framework enabling new discoveries. Hence, we propose a novel, data-driven concept of an integrated cell, iCell. Also, we introduce a computational prototype of an iCell, which integrates three omics, tissue-specific molecular interaction network types. We construct iCells of four cancers and the corresponding tissue controls and identify the most rewired genes in cancer. Many of them are of unknown function and cannot be identified as different in cancer in any specific molecular network. We biologically validate that they have a role in cancer by knockdown experiments followed by cell viability assays. We find additional support through Kaplan-Meier survival curves of thousands of patients. Finally, we extend this analysis to uncover pan-cancer genes. Our methodology is universal and enables integrative comparisons of diverse omics data over cells and tissues
GraphCrunch: A tool for large network analyses by Tijana Milenković( )

1 edition published in 2008 in English and held by 2 WorldCat member libraries worldwide

Background: The recent explosion in biological and other real-world network data has created the need for improved tools for large network analyses. In addition to well established global network properties, several new mathematical techniques for analyzing local structural properties of large networks have been developed. Small over-represented subgraphs, called networkmotifs, have been introduced to identify simple building blocks of complex networks. Small induced subgraphs, called graphlets, have been used todevelop Žnetwork signaturesŽ that summarize network topologies. Based on these network signatures, two new highly sensitive measures of network local structural similarities were designed: the relative graphlet frequency distance (RGF-distance) and the graphlet degree distribution agreement (GDD-agreement). Finding adequate null-models for biological networks is important in many research domains. Network properties are used to assess the fit of network models to the data. Various network models have been proposed. To date, there does not exist a software tool that measures the above mentioned local network properties. Moreover, none of the existing tools compare real-world networks against a series of network models with respect tothese local as well as a multitude of global network properties. Results: Thus, we introduce GraphCrunch, a software tool that finds well-fitting network models by comparing large real-world networks against random graph models according to various network structural similarity measures. It has unique capabilities of finding computationally expensive RGF-distance and GDD-agreement measures. In addition, it computes several standard global network measures and thus supports the largest variety of network measures thus far. Also, it is the first software tool that compares real-world networks against a series of network models and that has built-in parallel computing capabilities allowing for a user specified list of machines on whichto perform compute intensive searches for local network properties. Furthermore, GraphCrunch is easily extendible to include additional network measures and models. Conclusions: GraphCrunch is a software tool that implements the latest research on biological network models and properties: itcompares real-world networks against a series of random graph models with respect to a multitude of local and global network properties. We present GraphCrunch as a comprehensive, parallelizable, and easily extendible softwaretool for analyzing and modeling large biological networks. The software is open-source and freely available at http://www.ics.uci.edu/~bio-nets/graphcrunch/. It runs under Linux, MacOS, andWindows Cygwin. In addition, it has an easy to use on-line web user interface that is available from the above web page
Genetic mapping of a leaf rust resistance gene in the former Yugoslavian barley landrace MBR1012 by J König( )

1 edition published in 2012 in English and held by 2 WorldCat member libraries worldwide

GraphCrunch 2: Software tool for network modeling, alignment and clustering by Oleksii Kuchaiev( )

1 edition published in 2011 in English and held by 2 WorldCat member libraries worldwide

Background: Recent advancements in experimental biotechnology have produced large amounts of proteinprotein interaction (PPI) data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravityhas helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable Žnetwork properties,Ž such as the degree distribution, or the clustering coefficient. An important special case of network comparisonis the network alignment problem. Analogous to sequence alignment, this problem asks to find the ŽbestŽ mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important networkanalysis problem that can uncover relationships between interaction patterns and phenotype. Results: We introduce the GraphCrunch 2 software tool,which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAALŽ) for purely topological network alignment. GRAAL can align any pair of networks andexposes large, dense, contiguous regions of topological and functional similarities far larger than any other existing tool. Finally, GraphCruch 2 implements an algorithm for clustering nodes within a network based solely on their topological similarities. Using GraphCrunch 2, we demonstrate that eukaryotic and viral PPI networks may belong to different graph model familiesand show that topology-based clustering can reveal important functional similarities between proteins within yeast and human PPI networks. Conclusions: GraphCrunch 2 is a software tool that implements the latest research on biological network analysis. It parallelizes computationally intensive tasks to fully utilize the potential of modern multi-core CPUs. It is opensource and freely available for research use. It runs under the Windowsand Linux platforms
Prenos plačilnega prometa iz APP v banke in njegov vpliv na poslovanje podjetij : diplomsko delo by Nataša Prz̆ulj( Book )

1 edition published in 2001 in Slovenian and held by 2 WorldCat member libraries worldwide

Author Correction: Towards a data-integrated cell by Noël Malod-Dognin( )

1 edition published in 2019 in English and held by 2 WorldCat member libraries worldwide

Precision medicine - A promising, yet challenging road lies ahead by Noël Malod-Dognin( )

2 editions published in 2018 in English and held by 2 WorldCat member libraries worldwide

Precision medicine proposes to individualize the practice of medicine based on patients' genetic backgrounds, their biomarker characteristics and other omics datasets. After outlining the key challenges in precision medicine, namely patient stratification, biomarker discovery and drug repurposing, we survey recent developments in high-throughput technologies and big biological datasets that shape the future of precision medicine. Furthermore, we provide an overview of recent data-integrative approaches that have been successfully used in precision medicine for mining medical knowledge from big-biological data, and we highlight modeling and computing issues that such integrative approaches will face due to the ever-growing nature of big-biological data. Finally, we raise attention to the challenges in translational medicine when moving from research findings to approved medical practices
Predicting disease associations via biological network analysis by Kai Sun( )

1 edition published in 2014 in English and held by 2 WorldCat member libraries worldwide

Background: Understanding the relationship between diseases based on the underlying biological mechanisms is one of the greatest challenges in modern biology and medicine. Exploring disease-disease associations by using system-level biological data is expected to improve our current knowledge of disease relationships, which may lead to further improvements in disease diagnosis, prognosis and treatment. Results: We took advantage of diverse biological data including disease-gene associations and a large-scale molecular network to gain novel insights into disease relationships. We analysed and compared four publicly available disease-gene association datasets, then applied three disease similarity measures, namely annotation-based measure, function-based measure and topology-based measure, to estimate the similarity scores between diseases. We systematically evaluated disease associations obtained by these measures against a statistical measure of comorbidity which was derived from a large number of medical patient records. Our results show that the correlation between our similarity measures and comorbidity scores is substantially higher than expected at random, confirming that our similarity measures are able to recover comorbidity associations. We also demonstrated that our predicted disease associations correlated with disease associations generated from genome-wide association studies significantly higher than expected at random. Furthermore, we evaluated our predicted disease associations via mining the literature on PubMed, and presented case studies to demonstrate how these novel disease associations can be used to enhance our current knowledge of disease relationships. Conclusions: We present three similarity measures for predicting disease associations. The strong correlation between our predictions and known disease associations demonstrates the ability of our measures to provide novel insights into disease relationships
Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets by Hsiang Ho( )

1 edition published in 2010 in English and held by 2 WorldCat member libraries worldwide

Abstract Background: RNA-mediated interference (RNAi)-based functional genomics is a systems-level approach to identify novel genes that control biological phenotypes. Existing computational approaches can identify individual genes from RNAi datasets that regulate a given biological process. However, currently available methods cannot identify which RNAi screen "hits" are novel components of well-characterized biological pathways known to regulate the interrogated phenotype. In this study, we describe a method to identify genes from RNAi datasets that are novel components of known biological pathways. We experimentally validate our approach in the context ofa recently completed RNAi screen to identify novel regulators of melanogenesis. Results: In this study, we utilize a PPI network topology-basedapproach to identify targets within our RNAi dataset that may becomponents of known melanogenesis regulatory pathways. Our computational approach identifies a set of screen targets that cluster topologically in a human PPI network with the known pigment regulator Endothelin receptor type B (EDNRB). Validation studies reveal that these genes impact pigment production and EDNRB signaling in pigmented melanoma cells (MNT-1) and normal melanocytes. Conclusions: We present an approach that identifies novel components of well-characterized biological pathways from functional genomics datasets that could not have been identified by existing statistical and computational approaches
Informatizacija managementa odnosov z odjemalci v storitvenem podjetju : magistrsko delo by Nataša Prz̆ulj( Book )

1 edition published in 2010 in Slovenian and held by 2 WorldCat member libraries worldwide

Geometric local structure in biological networks by Nataša Prz̆ulj( )

1 edition published in 2007 in English and held by 1 WorldCat member library worldwide

The recent explosion in biological and other realworld network data has created the need for improved tools for large network analyses. Several new mathematical techniques for analyzing local structural properties of large networks have recently been developed. Our work introduces small induced subgraphs of large networks, called graphlets. We use graphlets to develop Žnetwork signaturesŽ that quantify local structural properties of a network. Based on these network signatures, we design two novel Žnetwork agreementŽ measures. These measures lead us to new, well-fitting geometric graph models of biological networks. Models are in turn used to design efficient heuristics
Systematic protein-protein interaction mapping for clinically relevant human GPCRs( )

1 edition published in 2017 in English and held by 1 WorldCat member library worldwide

G-protein-coupled receptors (GPCRs) are the largest family of integral membrane receptors with key roles in regulating signaling pathways targeted by therapeutics, but are difficult to study using existing proteomics technologies due to their complex biochemical features. To obtain a global view of GPCR-mediated signaling and to identify novel components of their pathways, we used a modified membrane yeast two-hybrid (MYTH) approach and identified interacting partners for 48 selected fulllength human ligand-unoccupied GPCRs in their native membrane environment. The resulting GPCR interactome connects 686 proteins by 987 unique interactions, including 299 membrane proteins involved in a diverse range of cellular functions. To demonstrate the biological relevance of the GPCR interactome, we validated novel interactions of the GPR37, serotonin 5-HT4d, and adenosine ADORA2A receptors. Our data represent the first large-scale interactome mapping for human GPCRs and provide a valuable resource for the analysis of signaling pathways involving this druggable family of integral membrane proteins
Critical review on clinoptilolite safety and medical applications in vivo( )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

Unique and outstanding physical and chemical properties of zeolite materials make them extremely useful in a variety of applications including agronomy, ecology, manufacturing, and industrial processes. Recently, a more specific application of one naturally occurring zeolite material, clinoptilolite, has been widely studied in veterinary and human medicine. Due to a number of positive effects on health, including detoxification properties, the usage of clinoptilolite-based products in vivo has increased enormously. However, concerns have been raised in the public about the safety of clinoptilolite materials for in vivo applications. Here, we review the scientific literature on the health effects and safety in medical applications of different clinoptilolite-based materials and propose some comprehensive, scientifically-based hypotheses on possible biological mechanisms underlying the observed effects on the health and body homeostasis. We focus on the safety of the clinoptilolite material and the positive medical effects related to detoxification, immune response, and the general health status
Discovering disease-disease associations by fusing systems-level molecular data( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

Omics data complementarity underlines functional cross-communication in yeast by Noël Malod-Dognin( )

1 edition published in 2017 in English and held by 1 WorldCat member library worldwide

Mapping the complete functional layout of a cell and understanding the cross-talk between different processes are fundamental challenges. They elude us because of the incompleteness and noisiness of molecular data and because of the computational intractability of finding the exact answer. We perform a simple integration of three types of baker's yeast omics data to elucidate the functional organization and lines of cross-functional communication. We examine protein-protein interaction (PPI), co-expression (COEX) and genetic interaction (GI) data, and explore their relationship with the gold standard of functional organization, the Gene Ontology (GO). We utilize a simple framework that identifies functional cross-communication lines in each of the three data types, in GO, and collectively in the integrated model of the three omics data types; we present each of them in our new Functional Organization Map (FOM) model. We compare the FOMs of the three omics datasets with the FOM of GO and find that GI is in best agreement with GO, followed COEX and PPI. We integrate the three FOMs into a unified FOM and find that it is in better agreement with the FOM of GO than those of any omics dataset alone, demonstrating functional complementarity of different omics data
Protein-protein interactions making sense of networks via graph-theoretic modeling by Nataša Prz̆ulj( )

1 edition published in 2011 in English and held by 1 WorldCat member library worldwide

Motivation: High-throughput methods for detecting molecular interactions have produced large sets of biological network data with much more yet to come. Analogous to sequence alignment, efficient and reliable network alignment methods are expected to improve our understanding of biological systems. Unlike sequence alignment, network alignment is computationally intractable. Hence, devising efficient network alignment heuristics is currently a foremostchallenge in computational biology. Results: We introduce a novel network alignment algorithm, called Matching-based Integrative GRAph ALigner (MI-GRAAL), which can integrate any number and type of similarity measures between network nodes (e.g. proteins), including, but not limited to, any topological network similarity measure, sequence similarity, functional similarity and structural similarity. Hence, we resolve the ties in similaritymeasures and find a combination of similarity measures yielding the largest contiguous (i.e. connected) and biologically sound alignments. MI-GRAAL exposes the largest functional, connected regions of protein-protein interaction (PPI) network similarity to date: surprisingly, it reveals that 77.7% of proteins in the bakerʼs yeast high-confidence PPI network participatein such a subnetwork that is fully contained in the human highconfidence PPI network. This is the first demonstration that species as diverse as yeast and human contain so large, continuous regions of global network similarity. We apply MI-GRAALʼs alignments to predict functions of un-annotated proteins in yeast, human and bacteria validating our predictions in the literature. Furthermore, using network alignment scores for PPI networks of different herpes viruses, we reconstruct their phylogenetic relationship. This is the first time that phylogeny is exactly reconstructed from purely topological alignments of PPI networks
Minimal hereditary dominating pair graphs by Nataša Prz̆ulj( )

1 edition published in 2000 in English and held by 1 WorldCat member library worldwide

This thesis describes structural properties of 'hereditary dominating pair' ('HDP') and minimal HDP graphs. A ' dominating pair' ('DP') in a connected graph is a pair of vertices such that every path between them is dominating. A graph ' G' is HDP if every connected induced subgraph of 'G' has a DP. The class of HDP graphs includes all asteroidal triple-free (AT-free) graphs--already extensively studied--and some graphs containing asteroidal triples (ATs). A 'minimal' HDP graph 'H' contains an AT <math> <f> <fen lp="cub">x,y,z<rp post="cub"></fen></f> </math>, and satisfies the following: if <math> <f> <sc>P<sup><it>c</it></sup><inf><mit><it>a,b</it></mit></inf></sc> </f> </math> is the set of all induced paths between vertices 'a' and 'b' that avoid the neighborhood of a vertex 'c', then every vertex of 'H' belongs to a path in <math> <f> <sc>P<sup><it>z</it></sup><inf><it>x,y</it></inf></sc><sc> P<sup><it>y</it></sup><inf><it>x,z</it></inf></sc><sc>P<sup> <it>x</it></sup><inf><it>y,z</it></inf></sc></f> </math>. The position of DP vertices in minimal HDP graphs is determined, as well as some structural properties dictated by the position of DP vertices
 
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Analyzing network data in biology and medicine : an interdisciplinary textbook for biological, medical and computational scientists
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Alternative Names
Natasa Przulj onderzoeker

Prulj, Nataa

Pržulj, N.

Przulj, Natasa

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