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

Fitting a geometric graph to a protein-protein interaction network

Finding a good network null model for protein-protein interaction (PPI) networks is a fundamental issue. Such a model would provide insights into the interplay between network structure and biological function as well as into evolution. Also, network (graph) models are used to guide biological experiments and discover new biological features. It has been proposed that geometric random graphs are a good model for PPI networks. In a geometric random graph, nodes correspond to uniformly randomly distributed points in a metric space and edges (links) exist between pairs of nodes for which the corresponding points in the metric space are close enough according to some distance norm. Computational experiments have revealed close matches between key topological properties of PPI networks and geometric random graph models. In this work, we push the comparison further by exploiting the fact that the geometric property can be tested for directly. To this end, we develop an algorithm that takes PPI interaction data and embeds proteins into a low-dimensional Euclidean space, under the premise that connectivity information corresponds to Euclidean proximity, as in geometric-random graphs.We judge the sensitivity and specificity of the fit by computing the area under the Receiver Operator Characteristic (ROC) curve. The network embedding algorithm is based on multi-dimensional scaling, with the square root of the path length in a network playing the role of the Euclidean distance in the Euclidean space. The algorithm exploits sparsity for computational efficiency, and requires only a few sparse matrix multiplications, giving a complexity of O(N2) where N is the number of proteins.The algorithm has been verified in the sense that it successfully rediscovers the geometric structure in artificially constructed geometric networks, even when noise is added by re-wiring some links. Applying the algorithm to 19 publicly available PPI networks of various organisms indicated that: (a) geometric effects are present and (b) two-dimensional Euclidean space is generally as effective as higher dimensional Euclidean space for explaining the connectivity. Testing on a high-confidence yeast data set produced a very strong indication of geometric structure (area under the ROC curve of 0.89), with this network being essentially indistinguishable from a noisy geometric network. Overall, the results add support to the hypothesis that PPI networks have a geometric structure

Fitting a geometric graph to a protein-protein interaction network

Finding a good network null model for protein-protein interaction (PPI) networks is a fundamental issue. Such a model would provide insights into the interplay between network structure and biological function as well as into evolution. Also, network (graph) models are used to guide biological experiments and discover new biological features. It has been proposed that geometric random graphs are a good model for PPI networks. In a geometric random graph, nodes correspond to uniformly randomly distributed points in a metric space and edges (links) exist between pairs of nodes for which the corresponding points in the metric space are close enough according to some distance norm. Computational experiments have revealed close matches between key topological properties of PPI networks and geometric random graph models. In this work, we push the comparison further by exploiting the fact that the geometric property can be tested for directly. To this end, we develop an algorithm that takes PPI interaction data and embeds proteins into a low-dimensional Euclidean space, under the premise that connectivity information corresponds to Euclidean proximity, as in geometric-random graphs.We judge the sensitivity and specificity of the fit by computing the area under the Receiver Operator Characteristic (ROC) curve. The network embedding algorithm is based on multi-dimensional scaling, with the square root of the path length in a network playing the role of the Euclidean distance in the Euclidean space. The algorithm exploits sparsity for computational efficiency, and requires only a few sparse matrix multiplications, giving a complexity of O(N2) where N is the number of proteins.The algorithm has been verified in the sense that it successfully rediscovers the geometric structure in artificially constructed geometric networks, even when noise is added by re-wiring some links. Applying the algorithm to 19 publicly available PPI networks of various organisms indicated that: (a) geometric effects are present and (b) two-dimensional Euclidean space is generally as effective as higher dimensional Euclidean space for explaining the connectivity. Testing on a high-confidence yeast data set produced a very strong indication of geometric structure (area under the ROC curve of 0.89), with this network being essentially indistinguishable from a noisy geometric network. Overall, the results add support to the hypothesis that PPI networks have a geometric structure

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

Affirmation of principles and improved corporate governance in Serbia - business ethics and corporate social responsibility

The aim of this chapter is to examine whether corporate governance in Serbia is based on affirmation of responsible and ethical conduct. The concept of corporate governance refers to the system by which companies are managed and controlled in order to generate long term economic value for its shareholders, while respecting the interests of stakeholders and society as a whole. Companies’ responsibility towards their stakeholders is recognized as a principle of good corporate governance. In this chapter, we present results of the poll on the managers’ attitudes towards business ethics and corporate social responsibility in Serbian business environment. We found out that managers are uniformed in the belief that companies have responsibilities towards their stakeholders: employees, business partners, suppliers, customers, community and environment. In addition, they are of the opinion that ethical behaviour and business success could go along, and also that immoral conduct is not justified in business. Nevertheless, most of examined managers see current business environment in Serbia as an uncompromising struggle. We can conclude that while managers’ attitudes towards business form a solid basis for the affirmation of principles and improved corporate governance, the perception of business environment as an uncompromising struggle indicate that current business practice in Serbia, in fact, hinder ethical and responsible conduct and reflect its opposite

Osobine jarog ječma u Panonskoj zoni

Environmental conditions in the Pannonian zone can be characterized with moderate high temperature and partially water deficit during grain filling of spring barley, although low temperature and water deficit are possible also in period till anthesis. This study was conducted to evaluate the variation of the duration of the period from emergence to anthesis (VP), duration of grain filling period (GFP), plant height (PH), spikes number m-2 (SN), grains number spike-1 (GN), thousand grains weight (GW) and yield (YIL) in spring two-rowed barley in conditions of the Pannonian zone. All three factors; genotype, environment and the interaction GxY affected the studied traits. Average VP was 777 GDD, GFP 782 GDD, PH 78 cm, SN 523, GN 28.2, GW 43.2 g and YIL 6.26 t ha-1. Variation across varieties was higher than across growing seasons. Heritability varied from 0.66 for YIL to 0.94 for VP and GFP. This study confirmed that a sufficiently large genetic variability must be base for selecting appropriate varieties for the Pannonian zone conditions. In order to determine high yielding and quality barley extensive research in relation to breeding, variety choice for production and growing practice must be done.Ekološki uslovi u Panonskoj zoni odlikuju se umereno visokim temperaturama i delimičnim deficitom vode tokom perioda nalivanja zrna jarog ječma, mada su niske temperature i deficit vode mogući i u periodu do cvetanja. U sedmogodišnjim istraživanjima proučavano je variranje nekih fizioloških, morfoloških i produktivnih osobina jarog dvoredog ječma u uslovima Panonske zone, na lokalitetu Novi Sad. Prosečna dužina perioda od nicanja do cvetanja- vegetativni period, iznosila je 777°C sume aktivnih temperatura, perioda nalivanja zrna 782°C, visina stabljike 78 cm, broj klasova po m-2 523, broj zrna po klasu 28,2, masa hiljadu zrna 43,2 g i prinos zrna 6,26 t ha-1. Heritabilnost je varirala od 0,66 za prinos do 0,94 za vegetativni period i period nalivanja zrna. Varijabilnost svih ispitivanih osobina bila je određena genotipom, godinom i interakcijom genotip x godina. Ova istraživanja su potvrdila da je izbor odgovarajućih sorti za agroekološke uslove Panonske zone moguć iz dovoljno široke genetičke varijabilnosti. Intenzivan oplemenjivački rad, testiranje velikog broja sortu radi izbora najpovoljnijih za određeno područje i definisanje adekvatne tehnologije proizvodnje osnova su za postizanje visokog prinosa i dobrog kvaliteta pivskog ječma

Doing business in responsible manner: the case of Italian investors on the Serbian market

The assumption of this project is that in addition to the profit and benefits for owners, corporations also affect the society and its development. Concurrently, the common concept is that socially responsible business positively affects corporation’s financial results. However, management is not always aware of the correlation between corporation’s long-term performance and respect for ethical standards and is often oriented to achieving short term goals. The question of ethics in business is especially present in the times of crisis, the crises we are facing now. In developed market economies, questions of ethical business are the focal points of interest, leading to creation of certain rules and standards. However, in an underdeveloped and societies in transition ethical business is often in the shadow of questions about profitability. Therefore, we believe that it is very important to discover how social responsible business is seen by corporations from developed countries that are operating in Republic of Serbia. Since Italy is one of the leading investors in the Serbian market, our goal is to explore relationship of Italian corporation towards their employees, natural environment and society

Dry matter and nitrogen accumulation and use in spring barley

During growth, kernel of cereals can be provided with carbohydrate and nitrogen (N) from the translocation of pre-anthesis accumulated reserves stored either in the vegetative plant parts or from current assimilation during kernel development. This study was conducted to assess the effects of nitrogen level and cultivars on dry matter and N accumulation and mobilization during pre-anthesis and post-anthesis. Twenty two-rowed spring barley (Hordeum vulgare L.) cultivars were grown on a non-calcareous chernozem soil in four growing seasons (1995-1998) at Novi Sad (45degrees20'N, 15degrees51'E, 86 in a.s.l.) at two nitrogen levels. Dry matter accumulation before anthesis ranged from less than 50% in unfavorable to 90% in favorable growing conditions. Dry matter translocation occurred in favorable growing conditions only. Pre-anthesis accumulated N represented 57-92% and 54-129% of total N at maturity at the low and high N levels, respectively. Translocated N represented 41-85% and 37-153% of grain N at the low and high N level, respectively. N losses occurred in favorable growing conditions when anthesis N exceeded 150 kg/ha. N accumulation during grain filling was in negative correlation with dry matter and N accumulation before anthesis. The N harvest index was 0.57-0.63 and 0.71-0.74 in unfavorable and favorable growing conditions, respectively. Selection of genotypes with a higher ability of pre-anthesis reserve utilization or genotypes with longer leaf area duration after anthesis may be two possible solutions in spring barley breeding for Mediterranean growing conditions