Integration of molecular network data reconstructs Gene Ontology.

Motivation: Recently, a shift was made from using Gene Ontology (GO) to evaluate molecular network data to using these data to construct and evaluate GO. Dutkowski et al. provide the first evidence that a large part of GO can be reconstructed solely from topologies of molecular networks. Motivated by this work, we develop a novel data integration framework that integrates multiple types of molecular network data to reconstruct and update GO. We ask how much of GO can be recovered by integrating various molecular interaction data. Results: We introduce a computational framework for integration of various biological networks using penalized non-negative matrix tri-factorization (PNMTF). It takes all network data in a matrix form and performs simultaneous clustering of genes and GO terms, inducing new relations between genes and GO terms (annotations) and between GO terms themselves. To improve the accuracy of our predicted relations, we extend the integration methodology to include additional topological information represented as the similarity in wiring around non-interacting genes. Surprisingly, by integrating topologies of bakers’ yeasts protein–protein interaction, genetic interaction (GI) and co-expression networks, our method reports as related 96% of GO terms that are directly related in GO. The inclusion of the wiring similarity of non-interacting genes contributes 6% to this large GO term association capture. Furthermore, we use our method to infer new relationships between GO terms solely from the topologies of these networks and validate 44% of our predictions in the literature. In addition, our integration method reproduces 48% of cellular component, 41% of molecular function and 41% of biological process GO terms, outperforming the previous method in the former two domains of GO. Finally, we predict new GO annotations of yeast genes and validate our predictions through GIs profiling. Availability and implementation: Supplementary Tables of new GO term associations and predicted gene annotations are available at http://bio-nets.doc.ic.ac.uk/GO-Reconstruction/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Patient-specific data fusion for cancer stratification and personalised treatment

According to Cancer Research UK, cancer is a leading cause of death accounting for more than one in four of all deaths in 2011. The recent advances in experimental technologies in cancer research have resulted in the accumulation of large amounts of patient-specific datasets, which provide complementary information on the same cancer type. We introduce a versatile data fusion (integration) framework that can effectively integrate somatic mutation data, molecular interactions and drug chemical data to address three key challenges in cancer research: stratification of patients into groups having different clinical outcomes, prediction of driver genes whose mutations trigger the onset and development of cancers, and repurposing of drugs treating particular cancer patient groups. Our new framework is based on graph-regularised non-negative matrix tri-factorization, a machine learning technique for co-clustering heterogeneous datasets. We apply our framework on ovarian cancer data to simultaneously cluster patients, genes and drugs by utilising all datasets.We demonstrate superior performance of our method over the state-of-the-art method, Network-based Stratification, in identifying three patient subgroups that have significant differences in survival outcomes and that are in good agreement with other clinical data. Also, we identify potential new driver genes that we obtain by analysing the gene clusters enriched in known drivers of ovarian cancer progression. We validated the top scoring genes identified as new drivers through database search and biomedical literature curation. Finally, we identify potential candidate drugs for repurposing that could be used in treatment of the identified patient subgroups by targeting their mutated gene products. We validated a large percentage of our drug-target predictions by using other databases and through literature curation

Fusion and community detection in multi-layer graphs

Relational data arising in many domains can be represented by networks (or graphs) with nodes capturing entities and edges representing relationships between these entities. Community detection in networks has become one of the most important problems having a broad range of applications. Until recently, the vast majority of papers have focused on discovering community structures in a single network. However, with the emergence of multi-view network data in many real-world applications and consequently with the advent of multilayer graph representation, community detection in multi-layer graphs has become a new challenge. Multi-layer graphs provide complementary views of connectivity patterns of the same set of vertices. Fusion of the network layers is expected to achieve better clustering performance. In this paper, we propose two novel methods, coined as WSSNMTF (Weighted Simultaneous Symmetric Non-Negative Matrix Tri-Factorization) and NG-WSSNMTF (Natural Gradient WSSNMTF), for fusion and clustering of multi-layer graphs. Both methods are robust with respect to missing edges and noise. We compare the performance of the proposed methods with two baseline methods, as well as with three state-of-the-art methods on synthetic and three real-world datasets. The experimental results indicate superior performance of the proposed methods

Integrative methods for analyzing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face

Analysis of floating-head heat exchanger bolts failure

As-received floating-head heat exchanger bolts were broken (BB) and deposite-coated. The aim was to estimate a cause of their failure. The new bolts of the same material were used as a reference material (reference bolt – RB). After visual and radiographic examination, their chemical composition, structure and room-temperature mechanical properties were determined and compared. Comparison was made with the values set by standard, as well. Afterwards, fractography was performed on fractured surfaces of tensile specimens and originally (during exploitation) BBs to try to get an impression about bolts failure mechanism. Qualitative analysis of deposite was employed in order to confirm was there any possible influence of surroundings during their failure in terms of corrosion-assisted cracking. Chemical composition of RB and BB materials was analyzed by use of spectrophotometry and structure properties with light optical microscope (LOM). Fractured surfaces of tensile specimens and of BBs, as well as deposite chemistry, were analyzed by use of Scanning Electron Microscopy with Energy Dispersive System (SEM-EDS). BBs had an approximately three times higher sulphur content and lesser manganese content, lower ductility and higher strength values comparing to those of the RBs. Generally, fracture surfaces of both, RB and BB tensile specimens have a similar rosette-like macro-appearance. The only difference is that the radial marks in the case of the RBs are rougher. The surface has a more fibrous area and shear lip presence. Fracture mode can be characterized as dimple rupture and micromechanism as microvoid coalescence. In the case of BB tensile specimens, the mixed presence of dimples and cleavage facets was noticed. The macrofractography of originally broken surfaces shows a rough and complex topography of fracture surfaces indicating on a possibility that bolts failure has been a result of complex loading conditions. Presence of sulphur- and chlorine-containing particles on the fracture surfaces of BBs and in deposite reveals a possibility that failure was environmentally-assisted

Probabilistic Random Walk Models for Comparative Network Analysis

Graph-based systems and data analysis methods have become critical tools in many fields as they can provide an intuitive way of representing and analyzing interactions between variables. Due to the advances in measurement techniques, a massive amount of labeled data that can be represented as nodes on a graph (or network) have been archived in databases. Additionally, novel data without label information have been gradually generated and archived. Labeling and identifying characteristics of novel data is an important first step in utilizing the valuable data in an effective and meaningful way. Comparative network analysis is an effective computational means to identify and predict the properties of the unlabeled data by comparing the similarities and differences between well-studied and less-studied networks. Comparative network analysis aims to identify the matching nodes and conserved subnetworks across multiple networks to enable a prediction of the properties of the nodes in the less-studied networks based on the properties of the matching nodes in the well-studied networks (i.e., transferring knowledge between networks). One of the fundamental and important questions in comparative network analysis is how to accurately estimate node-to-node correspondence as it can be a critical clue in analyzing the similarities and differences between networks. Node correspondence is a comprehensive similarity that integrates various types of similarity measurements in a balanced manner. However, there are several challenges in accurately estimating the node correspondence for large-scale networks. First, the scale of the networks is a critical issue. As networks generally include a large number of nodes, we have to examine an extremely large space and it can pose a computational challenge due to the combinatorial nature of the problem. Furthermore, although there are matching nodes and conserved subnetworks in different networks, structural variations such as node insertions and deletions make it difficult to integrate a topological similarity. In this dissertation, novel probabilistic random walk models are proposed to accurately estimate node-to-node correspondence between networks. First, we propose a context-sensitive random walk (CSRW) model. In the CSRW model, the random walker analyzes the context of the current position of the random walker and it can switch the random movement to either a simultaneous walk on both networks or an individual walk on one of the networks. The context-sensitive nature of the random walker enables the method to effectively integrate different types of similarities by dealing with structural variations. Second, we propose the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) model. In the CUFID model, we construct an integrated network by inserting pseudo edges between potential matching nodes in different networks. Then, we design the random walk protocol to transit more frequently between potential matching nodes as their node similarity increases and they have more matching neighboring nodes. We apply the proposed random walk models to comparative network analysis problems: global network alignment and network querying. Through extensive performance evaluations, we demonstrate that the proposed random walk models can accurately estimate node correspondence and these can lead to improved and reliable network comparison results

Lymphatic marker podoplanin/D2-40 in human advanced cirrhotic liver- Re-evaluations of microlymphatic abnormalities

<p>Abstract</p> <p>Background</p> <p>From the morphological appearance, it was impossible to distinguish terminal portal venules from small lymphatic vessels in the portal tract even using histochemical microscopic techniques. Recently, D2-40 was found to be expressed at a high level in lymphatic endothelial cells (LECs). This study was undertaken to elucidate hepatic lymphatic vessels during progression of cirrhosis by examining the expression of D2-40 in LECs.</p> <p>Methods</p> <p>Surgical wedge biopsy specimens were obtained from non-cirrhotic portions of human livers (normal control) and from cirrhotic livers (LC) (Child A-LC and Child C-LC). Immunohistochemical (IHC), Western blot, and immunoelectron microscopic studies were conducted using D2-40 as markers for lymphatic vessels, as well as CD34 for capillary blood vessels.</p> <p>Results</p> <p>Imunostaining of D2-40 produced a strong reaction in lymphatic vessels only, especially in Child C-LC. It was possible to distinguish the portal venules from the small lymphatic vessels using D-40. Immunoelectron microscopy revealed strong D2-40 expression along the luminal and abluminal portions of the cell membrane of LECs in Child C-LC tissue.</p> <p>Conclusion</p> <p>It is possible to distinguish portal venules from small lymphatic vessels using D2-40 as marker. D2-40- labeling in lymphatic capillary endothelial cells is related to the degree of fibrosis in cirrhotic liver.</p

Effects of friction-welding parameters on the morphological properties of an al/cu bimetallic joint

The objective of this research is to consider the effects of certain parameters of the friction-welding process on the morphology of an aluminum/copper joint. The effect of the following parameters was monitored: the operating time, the operating pressure, the forging time and the forging pressure. The speed was constant during the binding process and reached 1500 min(-1). The preparation of the welding materials was performed in accordance with the industrial production conditions. With the SEM-EDS analysis, it was found that the morphology of the Al/Cu interface slightly changes when we change the distance from the rotation axis, irrespective of the combination of the friction-welding parameters. Apart from this, the joined effects of the operating pressure of 48 MPa and the forging pressure of 160 MPa caused a morphological change of the Al/Cu interface, while the forging time at the moment of the combined pressurizing effect significantly influenced the modification of the Al/Cu interface shape within a very narrow time interval of only a few seconds

Cracking caused by cutting of plasma-sprayed hydroxyapatite coatings and its relation to the structural features of coatings deposited at different initial substrate temperatures

The present study estimated the cracking phenomenon in as-plasma-sprayed hydroxylapatite coatings (HACs) after they were being subjected to the severe cutting conditions in the direction perpendicular to the coating/substrate interface. In order to evaluate the effects of substrate preheating on the occurrence of micro-cracks, the HACs were deposited at different initial substrate temperatures (T-S = 20, 100 and 200 degrees C). The changes in phase composition and HA splat morphology with T-S were observed and were correlated with the cracking occurrence. The results showed that severe cutting conditions introduced a localized cracking in the regions of HACs dominantly attributed to the brittle hydroxyl- deficient amorphous calcium phosphate (ACP) phase. This effect was particularly observable in the HACs deposited without preheating of substrate. On the other hand, the preheating of substrate reduced the presence of micro-cracks and caused insignificant changes in the average local phase composition. In HACs deposited with preheating of substrate, the HA splats (of which HACs are composed) were thinner and recrystallized HA regions seemed smaller in size and more evenly distributed. These results implied potentially important roles of the HA splat formation mechanism on the distribution of ACP and recrystallized HA regions in the as-plasma-sprayed HACs and the cracking resistance of HACs