Bipartite graphs in systems biology and medicine: a survey of methods and applications.
Ontology highlight
ABSTRACT:
SUBMITTER: Pavlopoulos GA
PROVIDER: S-EPMC6333914 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Biological systems are composed of numerous components of which proteins are of particularly high functional significance. Network models are useful abstractions for studying these components in context. Network representations display molecules as nodes and their interactions as edges. Because they are difficult to directly measure, functional edges are frequently inferred from suitably structured datasets consisting of the accurate and consistent quantification of network nodes under a multitude of perturbed conditions. For the precise quantification of a finite list of proteins across a wide range of samples, targeted proteomics exemplified by selected/multiple reaction monitoring (SRM, MRM) mass spectrometry has proven useful and has been applied to a variety of questions in systems biology and clinical studies. Here, we survey the literature of studies using SRM-MS in systems biology and clinical proteomics. Systems biology studies frequently examine fundamental questions in network biology, whereas clinical studies frequently focus on biomarker discovery and validation in a variety of diseases including cardiovascular disease and cancer. Targeted proteomics promises to advance our understanding of biological networks and the phenotypic significance of specific network states and to advance biomarkers into clinical use.
| S-EPMC4758406 | biostudies-other
Project description:BACKGROUND: The goal of information integration in systems biology is to combine information from a number of databases and data sets, which are obtained from both high and low throughput experiments, under one data management scheme such that the cumulative information provides greater biological insight than is possible with individual information sources considered separately. RESULTS: Here we present PathSys, a graph-based system for creating a combined database of networks of interaction for generating integrated view of biological mechanisms. We used PathSys to integrate over 14 curated and publicly contributed data sources for the budding yeast (S. cerevisiae) and Gene Ontology. A number of exploratory questions were formulated as a combination of relational and graph-based queries to the integrated database. Thus, PathSys is a general-purpose, scalable, graph-data warehouse of biological information, complete with a graph manipulation and a query language, a storage mechanism and a generic data-importing mechanism through schema-mapping. CONCLUSION: Results from several test studies demonstrate the effectiveness of the approach in retrieving biologically interesting relations between genes and proteins, the networks connecting them, and of the utility of PathSys as a scalable graph-based warehouse for interaction-network integration and a hypothesis generator system. The PathSys's client software, named BiologicalNetworks, developed for navigation and analyses of molecular networks, is available as a Java Web Start application at http://brak.sdsc.edu/pub/BiologicalNetworks.
| S-EPMC1409799 | biostudies-other
Project description:ODE simulations of chemical systems perform poorly when some of the species have extremely low concentrations. Stochastic simulation methods, which can handle this case, have been impractical for large systems due to computational complexity. We observe, however, that when modeling complex biological systems: (1) a small number of reactions tend to occur a disproportionately large percentage of the time, and (2) a small number of species tend to participate in a disproportionately large percentage of reactions. We exploit these properties in LOLCAT Method, a new implementation of the Gillespie Algorithm. First, factoring reaction propensities allows many propensities dependent on a single species to be updated in a single operation. Second, representing dependencies between reactions with a bipartite graph of reactions and species requires only storage for reactions, rather than the required for a graph that includes only reactions. Together, these improvements allow our implementation of LOLCAT Method to execute orders of magnitude faster than currently existing Gillespie Algorithm variants when simulating several yeast MAPK cascade models.
| S-EPMC2798956 | biostudies-literature
Project description:High-throughput experimental techniques for generating genomes, transcriptomes, proteomes, metabolomes, and interactomes have provided unprecedented opportunities to interrogate biological systems and human diseases on a global level. Systems biology integrates the mass of heterogeneous high-throughput data and predictive computational modeling to understand biological functions as system-level properties. Most human diseases are biological states caused by multiple components of perturbed pathways and regulatory networks rather than individual failing components. Systems biology not only facilitates basic biological research but also provides new avenues through which to understand human diseases, identify diagnostic biomarkers, and develop disease treatments. At the same time, systems biology seeks to assist in drug discovery, drug optimization, drug combinations, and drug repositioning by investigating the molecular mechanisms of action of drugs at a system's level. Indeed, systems biology is evolving to systems medicine as a new discipline that aims to offer new approaches for addressing the diagnosis and treatment of major human diseases uniquely, effectively, and with personalized precision.
| S-EPMC4457580 | biostudies-literature
Project description:In this paper, we discuss parameterized algorithms for variants of the partial vertex cover problem. Recall that in the classical vertex cover problem (VC), we are given a graph
| S-EPMC7254907 | biostudies-literature
Project description:BackgroundExperimental datasets are becoming larger and increasingly complex, spanning different data domains, thereby expanding the requirements for respective tool support for their analysis. Networks provide a basis for the integration, analysis and visualization of multi-omics experimental datasets.ResultsHere we present VANTED (version 2), a framework for systems biology applications, which comprises a comprehensive set of seven main tasks. These range from network reconstruction, data visualization, integration of various data types, network simulation to data exploration combined with a manifold support of systems biology standards for visualization and data exchange. The offered set of functionalities is instantiated by combining several tasks in order to enable users to view and explore a comprehensive dataset from different perspectives. We describe the system as well as an exemplary workflow.ConclusionsVANTED is a stand-alone framework which supports scientists during the data analysis and interpretation phase. It is available as a Java open source tool from http://www.vanted.org.
| S-EPMC3610154 | biostudies-literature
Project description:Graph theoretical concepts are useful for the description and analysis of interactions and relationships in biological systems. We give a brief introduction into some of the concepts and their areas of application in molecular biology. We discuss software that is available through the Bioconductor project and present a simple example application to the integration of a protein-protein interaction and a co-expression network.
| S-EPMC1995545 | biostudies-literature
Project description:The traditional paradigm of cardiovascular disease research derives insight from large-scale, broadly inclusive clinical studies of well-characterized pathologies. These insights are then put into practice according to standardized clinical guidelines. However, stagnation in the development of new cardiovascular therapies and variability in therapeutic response implies that this paradigm is insufficient for reducing the cardiovascular disease burden. In this state-of-the-art review, we examine 3 interconnected ideas we put forth as key concepts for enabling a transition to precision cardiology: 1) precision characterization of cardiovascular disease with machine learning methods; 2) the application of network models of disease to embrace disease complexity; and 3) using insights from the previous 2 ideas to enable pharmacology and polypharmacology systems for more precise drug-to-patient matching and patient-disease stratification. We conclude by exploring the challenges of applying a precision approach to cardiology, which arise from a deficit of the required resources and infrastructure, and emerging evidence for the clinical effectiveness of this nascent approach.
| S-EPMC6034501 | biostudies-literature
Project description:The substantial development of high-throughput biotechnologies has rendered large-scale multi-omics datasets increasingly available. New challenges have emerged to process and integrate this large volume of information, often obtained from widely heterogeneous sources. Kernel methods have proven successful to handle the analysis of different types of datasets obtained on the same individuals. However, they usually suffer from a lack of interpretability since the original description of the individuals is lost due to the kernel embedding. We propose novel feature selection methods that are adapted to the kernel framework and go beyond the well-established work in supervised learning by addressing the more difficult tasks of unsupervised learning and kernel output learning. The method is expressed under the form of a non-convex optimization problem with a ℓ1 penalty, which is solved with a proximal gradient descent approach. It is tested on several systems biology datasets and shows good performances in selecting relevant and less redundant features compared to existing alternatives. It also proved relevant for identifying important governmental measures best explaining the time series of Covid-19 reproducing number evolution during the first months of 2020. The proposed feature selection method is embedded in the R package mixKernel version 0.8, published on CRAN. Installation instructions are available at http://mixkernel.clementine.wf/.
| S-EPMC8900155 | biostudies-literature
Project description:Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.
| S-EPMC7959646 | biostudies-literature