Betweenness Centrality Values Research Articles

Approximation of Interactive Betweenness Centrality in Large Complex Networks

The analysis of real-world systems through the lens of complex networks often requires a node importance function. While many such views on importance exist, a frequently used global node importance measure is betweenness centrality, quantifying the number of times a node occurs on all shortest paths in a network. This centrality of nodes often significantly depends on the presence of nodes in the network; once a node is missing, e.g., due to a failure, other nodes’ centrality values can change dramatically. This observation is, for instance, important when dismantling a network: instead of removing the nodes in decreasing order of their static betweenness, recomputing the betweenness after a removal creates tremendously stronger attacks, as has been shown in recent research. This process is referred to as interactive betweenness centrality. Nevertheless, very few studies compute the interactive betweenness centrality, given its high computational costs, a worst-case runtime complexity of O(N∗∗4) in the number of nodes in the network. In this study, we address the research questions, whether approximations of interactive betweenness centrality can be obtained with reduction of computational costs and how much quality/accuracy needs to be traded in order to obtain a significant reduction. At the heart of our interactive betweenness approximation framework, we use a set of established betweenness approximation techniques, which come with a wide range of parameter settings. Given that we are interested in the top-ranked node(s) for interactive dismantling, we tune these methods accordingly. Moreover, we explore the idea of batch removal, where groups of top-k ranked nodes are removed before recomputation of betweenness centrality values. Our experiments on real-world and random networks show that specific variants of the approximate interactive betweenness framework allow for a speedup of two orders of magnitude, compared to the exact computation, while obtaining near-optimal results. This work contributes to the analysis of complex network phenomena, with a particular focus on obtaining scalable techniques.

Identification and role of opinion leaders in information diffusion for online discussion network

Social networks are playing a vibrant role in spreading information among its users. With the ongoing boom in technology, social networks are becoming very popular nowadays and are made up of a multitude of users. Some of these users may have a strong influence on the other network users depending on their uncommon elevated values of betweenness centrality (BC). In the online discussion network such as Twitter, the extremely important users are called Opinion Leaders, who play a vital role in the spread of information in an efficient and fast way and keep the isolates interested in the online discussion network. One of the most significant problems in the associated sector is the identification of opinion leaders. In this paper, opinion leaders are chosen based on various centrality measures. The central users are identified based on their in-degree and out-degree links and are ranked within the network by their BC values. Furthermore, we analyze community evolution by using the standard Louvain algorithm. The experiment is performed on publicly available Higgs Boson data from Twitter. The conversation starter and influencer have been observed as an opinion leader for each network. These users have been observed to play a crucial part in the dissemination of information in an online discussion network.

Diffusion Delay Centrality: Decelerating Diffusion Processes Across Networks

This paper presents a methodology to identify agents who should be put into isolation to decelerate a diffusion process spreading through-out a network. We refer to this measure as to Diffusion Delay Centrality (DDC). We show that DDC assigns a high rank to agents acting as the gatekeepers of the fringe of the network. When these are isolated, the spreading of diffusion from the periphery to the core of the network (or vice versa) is prevented or, at least, decelerated. We also show that the ranking of nodes obtained from the DDC is predicted by standard measures of network centrality. Specifically, the ranking can be recovered using the difference in the values of betweenness and eigenvector centrality of network agents. We suggest that the findings presented in this paper might represent a useful tool for policies intending to reduce diffusion processes.

Mechanism differences between typical yin and typical yang personality individuals assessed by Five-Pattern Personality Inventory (FPPI): Evidence from resting-state brain functional networks

BackgroundMost studies assessing brain–personality mechanisms have used Western personality questionnaires. However, Western personality questionnaires may not objectively reflect the personality characteristics of individuals in Eastern cultures such as China. Hence, we adopted the functional magnetic resonance imaging (fMRI) and the Chinese localized scale, FPPI, to explore the brain mechanisms differences of typical yin and typical yang personalities of individuals in China. Methods30 typical yin personality participants (TYI) and 34 typical yang personality participants (TYA) were enrolled according to the FPPI. The group differences of the functional brain networks among 90 specific brain regions were mapped using fMRI data and then analyzed by the conventional network metrics (CNM) and frequency subgraph mining (FSM). ResultsThe CNM and FSM differences between two typical personality groups were traced to the frontal, temporal, and parietal cortices. The yin group, reflecting the rich emotions and feelings of individuals, showed higher betweenness centrality (BCi) and nodal efficiency (Ei) values in putamen and middle frontal gyrus. The yang group, reflecting active behaviors and tendency to adapting to the changing surroundings, showed higher BCi and Ei values in precuneus, posterior cingulate gyrus, and inferior parietal lobule, brain areas in the default mode network (DMN). ConclusionThese results supplied evidence for the neurobiological differences between typical yin and typical yang personality participants based on Chinese culture. These results also provide a new perspective to help researchers understand brain mechanism differences between yin and yang personality groups in the Chinese culture.

Optimizing node infiltrations in complex networks by a local search based heuristic

Over the past few years, the task of conceiving effective interventions on complex networks has arisen as different optimization problems. An interesting intervention scheme that has many important real-world applications is to introduce (infiltrate) in a network a certain number of new nodes and connect them to certain existing nodes with the aim of making them as central as possible. The idea is that they should occupy strategic positions in the network to gather a lot of information or to decisively influence others. In this work, we present an optimization problem that concerns the selection of nodes in a network with which to link each of a particular number of infiltrated nodes in order to maximize the lower betweenness centrality value obtained by them. This metric evaluates the participation of the nodes in the communications through the shortest paths of the network and it has been widely used as centrality measure in analyzing social networks. To address the problem, we propose a local search based heuristic whose performance is driven by two search strategies; a constructive greedy procedure that is employed to create an initial solution and a local improvement method that makes use of two neighborhood operators designed for exploring the search space of this problem. It should be noted that the development of metaheuristics for tackling combinatorial problems involving betweenness centrality is very challenging, because this measure is notoriously expensive to compute. That is why we have made two design decisions: firstly, to conceive a very simple metaheuristic approach and secondly, to incorporate in this proposal recent betweenness centrality update techniques that substantially reduce the number of shortest paths which should be re-computed when a network is changed. The performance of our optimizer, with respect to other metaheuristic models from the literature that can be adapted to face this problem, such as a randomized greedy multi-start algorithm and a steady-state genetic algorithm, is empirically shown.

The effects of high-frequency repetitive transcranial magnetic stimulation on resting-state functional connectivity in obese adults.

We conducted a 4-week randomized, sham-controlled, single-blind, parallel-group trial to examine the effect of repetitive transcranial magnetic stimulation (rTMS) delivered to the left dorsolateral prefrontal cortex (DLPFC) on functional brain connectivity and body weight in adults with obesity. Of the 45 volunteers with obesity, aged between 18 and 70 years (body mass index [BMI] ≥25 kg/m2 according to the obesity criterion for an Asian population), 36 participants (54.1 ± 11.0 years, BMI 30.2 ± 3.5 kg/m2 , 77.8% female) completed the 4 weeks of follow-up, undergoing two resting state fMRI scans (20 in the real stimulation group and 16 in the sham stimulation group). A total of eight sessions of high-frequency rTMS targeting the left DLPFC were provided over a period of 4 weeks (5-second trains with 25-second inter-train intervals, 10 Hz, 110% motor threshold; 2000 pulses over 20 minutes). Participants in the real stimulation group showed significantly greater weight loss from baseline following the eight session of rTMS (-2.53 ± 2.41 kg vs 0.38 ± 1.13 kg, P < 0.01). For intrinsic brain connectivity comparisons, the between-ness centrality values within the right frontoparietal network tended to increase with rTMS, and a significant interaction effect was identified for time (pre vs post) × rTMS (real vs sham) in the right frontoparietal network (P = 0.031, FDR corrected). We observed that rTMS selectively increased resting state functional connectivity within the right frontoparietal network. Our findings suggest that high-frequency rTMS to the left DLPFC might strengthen the frontoparietal network that orchestrates top-down inhibitory control to reduce food intake.

Temporal betweenness centrality in dynamic graphs

Measures of centrality of vertices in a network are usually defined solely on the basis of the network structure. In highly dynamic networks, where vertices appear and disappear and their connectivity constantly changes, we need to redefine our measures of centrality to properly capture the temporal dimension of the network structure evolution, as well as the dynamic processes over the network. Betweenness centrality (BC), one of the most studied measures, defines the importance of a vertex as a mediator between available communication paths. BC value of a node is expressed as the fraction of the shortest paths passing through this node. In other words, given the context that information flow follows the shortest paths, a node with higher BC potentially has greater influence on the information flow in the network. In temporal dynamic graphs, a communication path should be seen as a path both in space (i.e., the network structure) and in time (i.e., the network evolution). Toward this goal, in this paper we propose the bi-objective notion of shortest–fastest path (SFP) in temporal graphs, which considers both space and time as a linear combination governed by a parameter. Based on this notion, we then define a novel temporal betweenness centrality (TBC) metric, which is highly sensitive to the observation interval and the importance of space and time distances of the vertices, that can provide better understanding of the communication mediators in temporal networks. We devise efficient algorithms for exactly computing all-pairs SFPs and the corresponding BC values both in a single graph window and sliding graph windows. We also present a distributed implementation of our approach on Apache Spark which shows great solution effectiveness and efficiency. We provide a thorough experimentation on a large variety of datasets. An application to the analysis of information propagation proves that our notion of TBC outperforms static BC in the task of identifying the best vertices for propagating information.

Signature microRNAs and long noncoding RNAs in laryngeal cancer recurrence identified using a competing endogenous RNA network.

The aim of the present study was to identify novel microRNA (miRNA) or long noncoding RNA (lncRNA) signatures of laryngeal cancer recurrence and to investigate the regulatory mechanisms associated with this malignancy. Datasets of recurrent and nonrecurrent laryngeal cancer samples were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus database (GSE27020 and GSE25727) to examine differentially expressed miRNAs (DE-miRs), lncRNAs (DE-lncRs) and mRNAs (DEGs). miRNA-mRNA and lncRNA-miRNA networks were constructed by investigating the associations among these RNAs in various databases. Subsequently, the interactions identified were combined into a competing endogenous RNA (ceRNA) regulatory network. Feature genes in the miRNA-mRNA network were identified via topological analysis and a recursive feature elimination algorithm. A support vector machine (SVM) classifier was established using the betweenness centrality values in the miRNA-mRNA network, consisting of 32 optimal feature-coding genes. The classification effect was tested using two validation datasets. Furthermore, coding genes in the ceRNA network were examined via pathway enrichment analyses. In total, 21 DE-lncRs, 507 DEGs and 55 DE-miRs were selected. The SVM classifier exhibited an accuracy of 94.05% (79/84) for sample classification prediction in the TCGA dataset, and 92.66 and 91.07% in the two validation datasets. The ceRNA regulatory network comprised 203 nodes, corresponding to mRNAs, miRNAs and lncRNAs, and 346 lines, corresponding to the interactions among RNAs. In particular, the interactions with the highest scores were HLA complex group 4 (HCG4)-miR-33b, HOX transcript antisense RNA (HOTAIR)-miR-1-MAGE family member A2 (MAGEA2), EMX2 opposite strand/antisense RNA (EMX2OS)-miR-124-calcitonin related polypeptide α (CALCA) and EMX2OS-miR-124-γ-aminobutyric acid type A receptor γ2 subunit (GABRG2). Gene enrichment analysis of the genes in the ceRNA network identified that 11 pathway terms and 16 molecular function terms were significantly enriched. The SVM classifier based on 32 feature coding genes exhibited high accuracy in the classification of laryngeal cancer samples. miR-1, miR-33b, miR-124, HOTAIR, HCG4 and EMX2OS may be novel biomarkers of recurrent laryngeal cancer, and HCG4-miR-33b, HOTAIR-miR-1-MAGEA2 and EMX2OS-miR-124-CALCA/GABRG2 may be associated with the molecular mechanisms regulating recurrent laryngeal cancer.

Network-based centrality measures and physical demands in football regarding player position: Is there a connection? A preliminary study

ABSTRACTThis study’s main objective is to analyse the relationship between network-based centrality measures and physical demands in elite football players. Thirty-six matches from La Liga, the Spanish league, were analysed in the 2017/18 season. The analysis of networks formed by team players passing the ball included: degree-prestige (DP), degree-centrality (DC), betweenness-centrality (BC), page-rank (PRP) and closeness-centrality (IRCC). A video-based system was used for analysing total distance (TDpos) and distance run >21Km/h (TD21pos) when the team was in possession of the ball. A magnitude-based inference and correlation analysis were applied. There were different styles of play, team-A was characterized by greater ball circulation (e.g. higher values of DP, DC, BC and IRCC) while team-B used a more direct game (lower values in centrality-metrics except with PRP). Furthermore, TDpos was higher in team-A than in team-B, but those differences disappeared for TD21pos between teams with the exception of the forwards. Finally, the correlation among centrality measures and physical performance were higher in team-B. Coaches could identify the key opponents and players who are linked to them, allowing to adjust performance strategies. Furthermore, interaction patterns between teammates can be used to identify preferential paths of cooperation and to take decisions regarding these relations in order to optimize team performance.

A topological analysis of growth in the Zurich road network

The functioning of a city depends upon physical structures and services. Over time, a city evolves and the layout of its infrastructure is altered, providing a new arrangement of services for citizens. When designing road infrastructure, planners usually add or change existing roads, looking only at local consequences and ignoring the effects on the whole system. This results in an infrastructure that is developed without a clear understanding of how those alterations affect the global topology. We studied the development of road infrastructure in Zurich, Switzerland, modelled as a network. Our objectives were to 1) assess how non-spatial network topological metrics can identify changes in infrastructure over time and 2) determine any patterns of change in the spatial distribution of those metrics. We analyzed three types of betweenness centrality (BC) metrics with different weighting methods that depended upon structural properties, distance, and population distribution within the urban area. The study resulted in four main findings. First, the number of nodes and edges, together with the network diameter characterized the non-spatial aspect of development. Second, traditional connectivity metrics (alpha, beta, gamma indices) did not yield any changes in time due to their dependence on the average degree, which remained quite constant in infrastructure networks. Third, areas of high BC extended from 1955 to 2012 into a Y-shaped configuration, driven by the development of the main overarching national freeway system. Closeness centrality results show similar patterns. Fourth, the distribution of the 1955 normalized betweenness centrality values transformed into a more heavy-tail distribution in 2012, which implicated that the most critical nodes became even more critical. Future work will enhance this analysis by using more realistic assumptions and incorporating traffic survey data.

Potential connectivity between marine protected areas in the Mesoamerican Reef for two species of virtual fish larvae: Lutjanus analis and Epinephelus striatus

The potential connectivity network of marine protected areas (MPAs) with spawning aggregations sites in the Mesoamerican Barrier Reef system (MBRS) was examined through simulated dispersal of two study cases, Lutjanus analis and Epinephelus striatus larvae. The analysis was based on a particle tracking model incorporating four years of daily horizontal surface currents from the global scale HYCOM 1/12°. We carried out two approaches, the first one used the period of larval duration (PLD) and the second the time of flexion of the notochord as the limit time of dispersion of the particles. Additionally, peak spawning seasons (December-Febraury for E. striatus and March-July for L. analis), lunar phases and georeferenced spawning aggregations sites were considered for releasing the virtual larvae. Our findings suggest that in general, the network of MPAs of the MBRS are well connected, however, the connectivity network structure presented variations in the relative role of each MPA, and their played role will depend on the specie reproductive period, the oceanographic conditions and the assumed biological approximation (such as PLD or flexion). Our analysis suggested a regionalization of the MBRS. A southern region (Bacalar Chico, Roatan and Sapodilla) with low degree of connectivity, moderate self-recruitment rate, and low values of betweenness centrality. A second southern region (Turneff Island, Lighthouse Atoll, Glover Reef Atoll and Gladen Spit) with high rate of self-recruitment (up to 20%) that could be related to self-recruitment due to retention mechanism of the Honduras Gyre, with high values of source of larvae, function as bridges that connect the south zone with the north of the system. A third region (Mahahual and Xcalak) showing sink of larvae, low larval arrival rate but self-recruitment, low values of betweenness centrality area influenced by slow and variable currents and the presence of a southward flow. And a fourth region (Chinchorro, Sian Ka’an, Puerto Morelos and Cozumel) with moderate fast current speed (Yucatan Current), low self-recruitment, larval sink, bridge with the Gulf of Mexico and Florida.

Identification of prognostic biomarkers of prostate cancer with long non-coding RNA-mediated competitive endogenous RNA network.

Prostate cancer (PCa) is the second most frequently diagnosed male cancer, and no treatments exist for effective inhibition of metastatic spread of PCa. Long non-coding RNA (lncRNA) plays key roles in pathogenesis and development of various cancers through competing with endogenous RNAs (ceRNAs), but at present research on lncRNA functions in PCa is still very limited. Hence, this aspect was investigated using bioinformatics methods. Firstly, the functional lncRNA-mediated ceRNA network associated with PCa was constructed by the multi-step computational approach. Then the cytoscape software was used to analyze the node degree and betweenness centrality (BC) value of lncRNAs and mRNAs in the interaction. Finally, the lncRNAs were screened in the central region of the network by the node degree and BC value, and the functional enrichment of mRNAs was evaluated with the Gene Ontology (GO) database. In our results, LINC00476, MALAT1, SNHG11, LINC00649, and ILF3-AS1 are the lncRNAs which have the most nodes and higher BC values and considered as prognostic markers in PCa. GO analysis suggested that the function of screened lncRNAs was obviously focused on intracellular receptor signaling pathway, which indicated these lncRNAs might be potential biomarkers for diagnosis, evaluation and gene-targeted therapy of PCa.

Betweenness centrality as predictor for forces in granular packings.

A load applied to a jammed frictional granular system will be localized into a network of force chains making inter-particle connections throughout the system. Because such systems are typically under-constrained, the observed force network is not unique to a given particle configuration, but instead varies upon repeated formation. In this paper, we examine the ensemble of force chain configurations created under repeated assembly in order to develop tools to statistically forecast the observed force network. In experiments on a gently suspended 2D layer of photoelastic particles, we subject the assembly to hundreds of repeated cyclic compressions. As expected, we observe the non-unique nature of the force network, which differs for each compression cycle, by measuring all vector inter-particle contact forces using our open source PeGS software. We find that total pressure on each particle in the system correlates to its betweenness centrality value extracted from the geometric contact network. Thus, the mesoscale network structure is a key control on individual particle pressures.

Statistical determination of crucial taxa indicative of pollution gradients in sediments of Lake Taihu, China

In order to accurately monitor the changes in a freshwater ecosystem in response to anthropogenic stressors, microbe–environment correlations and microbe–microbe interactions were combined to determine crucial indicator taxa in contaminated sediments. The diversity, composition, and co–occurrence pattern of bacterial communities in 23 sediment samples collected from Lake Taihu were explored using 16S rRNA amplicon sequencing analysis. Fisher's exact test showed that the cluster analyses of samples could show a direct correlation between the relative abundance of bacterial communities and the physicochemical properties of the sediment (P < 0.0001), suggesting that bacterial communities can be used to monitor contamination gradients in freshwater sediments. According to the microbe–environment correlation, 24 orders and 60 families were initially identified via indicator species analysis as indicator taxa of different pollution levels. The co–occurrence network further showed that topological features of bacterial communities were clearly different at different pollution levels, although the diversity and composition of bacterial communities displayed similarities between minimally and moderately polluted sites. Indicator taxa were then screened for keystone species, which co–occurrence relationships showed the high degree and low betweenness centrality values (i.e. degree >5, betweenness centrality <1000) of the network. Nine orders and 13 families were finally extracted as crucial indicator taxa of the different pollution levels in eutrophic Lake Taihu. Obtaining crucial indicator taxa from environmental sequences allows to trace increasing levels of pollution in aquatic ecosystems and provides a novel mean to monitor watersheds sensitive to anthropic influences.

Enhancing robustness of metro networks using strategic defense

The safety of metro networks has recently attracted great attention. In this study, we investigated how to allocate security resources in a metro network to enhance its robustness through a defense-attack approach. For each defense-attack model, the defense strategy was first applied to protect a certain proportion of influential nodes in the network and then an attack was executed on the protected network. The influential nodes were identified by eight strategies, one of which, the breadth-tree coefficient strategy, is proposed as a new approach. Simulations of five metro networks showed that attacks on nodes with the highest betweenness centrality value are the most effective for disrupting a metro network, while the robustness of the metro network can be enhanced more efficiently with the proposed breadth-tree coefficient strategy. We also found that the robustness of metro networks benefits less from scattered protected metro stations, since some neglected weakly connected nodes emerge among the optimal influencers. The study also provided insights into how topological features influence the robustness of metro networks. It demonstrated that loop lines and smaller transfer stations can provide more options for alternative routes and be less vulnerable to targeted attacks, which makes the metro network more robust in the face of accidents and terrorist attacks. Our study provides useful information for metro network managers to take decisive actions on how to allocate security resources to increase the robustness of their metro network and design a more robust metro system.

A study on the influence of Korean Middle School students' relationship through Science class applying STAD cooperative learning

In order to find out the influence of Korean Middle School Students' relationship by science class applying STAD collaborative learning, this study conducted a social network analysis and sought to analyze the communication networks within the group and identified the change process of the type. The subject of this study was 30 students of the second grade at the girls' middle school located in Korea's Metropolitan City. For five weeks, science class applying STAD Collaborative Learning was implemented in the ‘reproduction and generation’ chapter. First, the class social network analysis showed that all the prices of density, degree centrality, closeness centrality, and betweenness centrality have risen after science class applying STAD Collaborative Learning. Also, the classroom's relationship index has improved. In other words, STAD Collaborative Learning encouraged interaction among students. Second, in order to research popularity, students' centrality analysis through the class social network analysis showed that top-ranked students' values of density, degree centrality, closeness centrality, and betweenness centrality appeared commonly high after science class applying STAD Collaborative Learning. Third, the analysis of the communication network change within six groups showed that all channel type appeared most often and circle type also appeared anew after science class applying STAD Collaborative Learning. In other words, it was possible to exchange information freely and communicate with all members of the group through STAD Collaborative Learning.

English

We study graphs whose vertices possess the same value of betweenness centrality (which is defined as the sum of relative numbers of shortest paths passing through a given vertex). Extending previously known results of S. Gago, J. Hurajova, T. Madaras (2013), we show that, apart of cycles, such graphs cannot contain 2-valent vertices and, moreover, are 3-connected if their diameter is 2. In addition, we prove that the betweenness uniformity is satisfied in a wide graph family of semi-symmetric graphs, which enables us to construct a variety of nontrivial cubic betweenness-uniform graphs.

Feature genes in metastatic breast cancer identified by MetaDE and SVM classifier methods.

The aim of the present study was to investigate the feature genes in metastatic breast cancer samples. A total of 5 expression profiles of metastatic breast cancer samples were downloaded from the Gene Expression Omnibus database, which were then analyzed using the MetaQC and MetaDE packages in R language. The feature genes between metastasis and non-metastasis samples were screened under the threshold of P<0.05. Based on the protein-protein interactions (PPIs) in the Biological General Repository for Interaction Datasets, Human Protein Reference Database and Biomolecular Interaction Network Database, the PPI network of the feature genes was constructed. The feature genes identified by topological characteristics were then used for support vector machine (SVM) classifier training and verification. The accuracy of the SVM classifier was then evaluated using another independent dataset from The Cancer Genome Atlas database. Finally, function and pathway enrichment analyses for genes in the SVM classifier were performed. A total of 541 feature genes were identified between metastatic and non-metastatic samples. The top 10 genes with the highest betweenness centrality values in the PPI network of feature genes were Nuclear RNA Export Factor 1, cyclin-dependent kinase 2 (CDK2), myelocytomatosis proto-oncogene protein (MYC), Cullin 5, SHC Adaptor Protein 1, Clathrin heavy chain, Nucleolin, WD repeat domain 1, proteasome 26S subunit non-ATPase 2 and telomeric repeat binding factor 2. The cyclin-dependent kinase inhibitor 1A (CDKN1A), E2F transcription factor 1 (E2F1), and MYC interacted with CDK2. The SVM classifier constructed by the top 30 feature genes was able to distinguish metastatic samples from non-metastatic samples [correct rate, specificity, positive predictive value and negative predictive value >0.89; sensitivity >0.84; area under the receiver operating characteristic curve (AUROC) >0.96]. The verification of the SVM classifier in an independent dataset (35 metastatic samples and 143 non-metastatic samples) revealed an accuracy of 94.38% and AUROC of 0.958. Cell cycle associated functions and pathways were the most significant terms of the 30 feature genes. A SVM classifier was constructed to assess the possibility of breast cancer metastasis, which presented high accuracy in several independent datasets. CDK2, CDKN1A, E2F1 and MYC were indicated as the potential feature genes in metastatic breast cancer.

Support vector machine classifier for prediction of the metastasis of colorectal cancer.

Colorectal cancer (CRC) is one of the most common cancers and a major cause of mortality. The present study aimed to identify potential biomarkers for CRC metastasis and uncover the mechanisms underlying the etiology of the disease. The five datasets GSE68468, GSE62321, GSE22834, GSE14297 and GSE6988 were utilized in the study, all of which contained metastatic and non-metastatic CRC samples. Among them, three datasets were integrated via meta-analysis to identify the differentially expressed genes (DEGs) between the two types of samples. A protein-protein interaction (PPI) network was constructed for these DEGs. Candidate genes were then selected by the support vector machine (SVM) classifier based on the betweenness centrality (BC) algorithm. A CRC dataset from The Cancer Genome Atlas database was used to evaluate the accuracy of the SVM classifier. Pathway enrichment analysis was carried out for the SVM-classified gene signatures. In total, 358 DEGs were identified by meta-analysis. The top ten nodes in the PPI network with the highest BC values were selected, including cAMP responsive element binding protein 1 (CREB1), cullin 7 (CUL7) and signal sequence receptor 3 (SSR3). The optimal SVM classification model was established, which was able to precisely distinguish between the metastatic and non-metastatic samples. Based on this SVM classifier, 40 signature genes were identified, which were mainly enriched in protein processing in endoplasmic reticulum (e.g., SSR3), AMPK signaling pathway (e.g., CREB1) and ubiquitin mediated proteolysis (e.g., FBXO2, CUL7 and UBE2D3) pathways. In conclusion, the SVM-classified genes, including CREB1, CUL7 and SSR3, precisely distinguished the metastatic CRC samples from the non-metastatic ones. These genes have the potential to be used as biomarkers for the prognosis of metastatic CRC.

Heuristic Approach to Determining Cache Node Locations in Content-Centric Networks

Internet users consider content information to be useful, but the current Internet approach treats location information as more important as so ties the former to the latter. A Content-Centric Network (CCN) allows the user to obtain content without regard to its location. CCN caches the contents information at its intermediate nodes. The content is searched along the shortest path between the user and the node that has the original content. If any cache node is located on the shortest path, the content can be obtained from the nearest cache node, so far fewer hops are needed compared to the network without any cache node. However, this efficiency is not achieved if no cache node is located on the shortest path. One proposal sets cache nodes that broadcast their contents to surrounding nodes; the user is able to obtain the content from the cache node, rather than the node that has the original content, if the cache node is closer to the user. The location of the cache node affects the number of hops. We formulate an optimization problem that determines the locations of cache nodes to minimize the hop count as an integer linear programming (ILP) problem. Since large ILPs cannot be solved in practical time, we introduce a betweenness centrality (BC) approach that determines the location of cache nodes by computing the BC value of each node and ranks the nodes in descending BC order. The BC value denotes the ratio of the number of shortest paths between source-receiver pairs passing through the node to the total number of shortest paths between source-receiver pairs. Simulations show that the BC approach offers drastically reduced computation time, while the average number of hops is just 5.8% higher than that determined with the ILP approach.