Analysis Of Co-occurrence Networks Research Articles

Analysis and Classification of Word Co-Occurrence Networks From Alzheimer’s Patients and Controls

In this paper we construct word co-occurrence networks from transcript data of controls and patients with potential Alzheimer’s disease using the ADReSS challenge dataset of spontaneous speech. We examine measures of the structure of these networks for significant differences, finding that networks from Alzheimer’s patients have a lower heterogeneity and centralization, but a higher edge density. We then use these measures, a network embedding method and some measures from the word frequency distribution to classify the transcripts into control or Alzheimer’s, and to estimate the cognitive test score of a participant based on the transcript. We find it is possible to distinguish between the AD and control networks on structure alone, achieving 66.7% accuracy on the test set, and to predict cognitive scores with a root mean squared error of 5.675. Using the network measures is more successful than using the network embedding method. However, if the networks are shuffled we find relatively few of the measures are different, indicating that word frequency drives many of the network properties. This observation is borne out by the classification experiments, where word frequency measures perform similarly to the network measures.

Mapping Artificial Intelligence in Education Research: a Network‐based Keyword Analysis

In this study, we review 1830 research articles on artificial intelligence in education (AIED), with the aim of providing a holistic picture of the knowledge evolution in this interdisciplinary research field from 2010 to 2019. A novel three-step approach in the analysis of the keyword co-occurrence networks (KCN) is proposed to identify the knowledge structure, knowledge clusters and trending keywords within AIED over time. The results reveal considerable research diversity in the AIED field, centering around two sustained themes: intelligent tutoring systems (2010-19) and massive open online courses (since 2014). The focal educational concerns reflected in AIED research are: (1) online learning; (2) game-based learning; (3) collaborative learning; (4) assessment; (5) affect; (6) engagement; and (7) learning design. The highly connected keywords relevant to analytic techniques within this field include natural language processing, educational data mining, learning analytics and machine learning. Neural network, deep learning, eye tracking, and personalized learning are trending keywords in this field as they have emerged with key structural roles in the latest two-year period analyzed. This is the first article providing a systematic review of a large body of literature on artificial intelligence in education, and in it we uncover the underlying patterns of knowledge connectivity within the field, as well as provide insight into its future development. The three-step multi-scale (macro, meso, micro) framework proposed in this study can also be applied to map the knowledge development in other scientific research areas.

Analysis of human mitochondrial genome co-occurrence networks of Asian population at varying altitudes

Networks have been established as an extremely powerful framework to understand and predict the behavior of many large-scale complex systems. We studied network motifs, the basic structural elements of networks, to describe the possible role of co-occurrence of genomic variations behind high altitude adaptation in the Asian human population. Mitochondrial DNA (mtDNA) variations have been acclaimed as one of the key players in understanding the biological mechanisms behind adaptation to extreme conditions. To explore the cumulative effects of variations in the mitochondrial genome with the variation in the altitude, we investigated human mt-DNA sequences from the NCBI database at different altitudes under the co-occurrence motifs framework. Analysis of the co-occurrence motifs using similarity clustering revealed a clear distinction between lower and higher altitude regions. In addition, the previously known high altitude markers 3394 and 7697 (which are definitive sites of haplogroup M9a1a1c1b) were found to co-occur within their own gene complexes indicating the impact of intra-genic constraint on co-evolution of nucleotides. Furthermore, an ancestral ‘RSRS50’ variant 10,398 was found to co-occur only at higher altitudes supporting the fact that a separate route of colonization at these altitudes might have taken place. Overall, our analysis revealed the presence of co-occurrence interactions specific to high altitude at a whole mitochondrial genome level. This study, combined with the classical haplogroups analysis is useful in understanding the role of co-occurrence of mitochondrial variations in high altitude adaptation.

New Insights into the Ecology and Physiology of Methanomassiliicoccales from Terrestrial and Aquatic Environments.

Members of the archaeal order Methanomassiliicoccales are methanogens mainly associated with animal digestive tracts. However, environmental members remain poorly characterized as no representatives not associated with a host have been cultivated so far. In this study, metabarcoding screening combined with quantitative PCR analyses on a collection of diverse non-host-associated environmental samples revealed that Methanomassiliicoccales were very scarce in most terrestrial and aquatic ecosystems. Relative abundance of Methanomassiliicoccales and substrates/products of methanogenesis were monitored during incubation of environmental slurries. A sediment slurry enriched in Methanomassiliicoccales was obtained from a freshwater sample. It allowed the reconstruction of a high-quality metagenome-assembled genome (MAG) corresponding to a new candidate species, for which we propose the name of Candidatus ‘Methanomassiliicoccus armoricus MXMAG1’. Comparison of the annotated genome of MXMAG1 with the published genomes and MAGs from Methanomassiliicoccales belonging to the 2 known clades (‘free-living’/non-host-associated environmental clade and ‘host-associated’/digestive clade) allowed us to explore the putative physiological traits of Candidatus ‘M. armoricus MXMAG1’. As expected, Ca. ‘Methanomassiliicoccus armoricus MXMAG1’ had the genetic potential to produce methane by reduction of methyl compounds and dihydrogen oxidation. This MAG encodes for several putative physiological and stress response adaptations, including biosynthesis of trehalose (osmotic and temperature regulations), agmatine production (pH regulation), and arsenic detoxication, by reduction and excretion of arsenite, a mechanism that was only present in the ‘free-living’ clade. An analysis of co-occurrence networks carried out on environmental samples and slurries also showed that Methanomassiliicoccales detected in terrestrial and aquatic ecosystems were strongly associated with acetate and dihydrogen producing bacteria commonly found in digestive habitats and which have been reported to form syntrophic relationships with methanogens.

A Study on Differences between Simplified and Traditional Chinese Based on Complex Network Analysis of the Word Co-Occurrence Networks.

Currently, most work on comparing differences between simplified and traditional Chinese only focuses on the character or lexical level, without taking the global differences into consideration. In order to solve this problem, this paper proposes to use complex network analysis of word co-occurrence networks, which have been successfully applied to the language analysis research and can tackle global characters and explore the differences between simplified and traditional Chinese. Specially, we first constructed a word co-occurrence network for simplified and traditional Chinese using selected news corpora. Then, the complex network analysis methods were performed, including network statistics analysis, kernel lexicon comparison, and motif analysis, to gain a global understanding of these networks. After that, the networks were compared based on the properties obtained. Through comparison, we can obtain three interesting results: first, the co-occurrence networks of simplified Chinese and traditional Chinese are both small-world and scale-free networks. However, given the same corpus size, the co-occurrence networks of traditional Chinese tend to have more nodes, which may be due to a large number of one-to-many character/word mappings from simplified Chinese to traditional Chinese; second, since traditional Chinese retains more ancient Chinese words and uses fewer weak verbs, the traditional Chinese kernel lexicons have more entries than the simplified Chinese kernel lexicons; third, motif analysis shows that there is no difference between the simplified Chinese network and the corresponding traditional Chinese network, which means that simplified and traditional Chinese are semantically consistent.

Microbial community and interspecies interaction during grazing of ark shell bivalve (Scapharca subcrenata) in a full-scale bioremediation system of mariculture effluents

A novel biological approach using ark shell bivalves as potential species for remediation of effluents was studied to determine the microbial community interspecies interaction and nutrient cycling in a restoration system of mariculture effluents. A field study showed that Scapharca subcrenata was the main driver of the microbial community's interspecies-interaction (PERMANOVA, R = 0.0572, P = 0.005) in the treatment zone (TZ). Analysis of co-occurrence networks based on random matrix theory (RMT) indicated that the network's complexity parameters were enhanced in the TZ and disrupted in the control zone (CZ) due to eutrophic disturbances. Concurrently, the TZ was correlated with more profound network modifications (i.e., higher modularity, total nodes (n), cohesion, and proportion of positive links), suggesting that S. subcrenata influenced microbial interspecies interactions in the system. Similarly, the co-occurring networks of generalists Proteobacteria (OTU2037) at genus Anaerospora and Actinobacteria (OTU9660) at genus Candidatus aquiluna for anaerobic ammonia-oxidation (ANAMMOX) were highly significant in the TZ. The top-down and bottom-up forces of S. subcrenata influenced the removal efficiency of nitrogenous compounds by reducing 81.51% of nitrite (NO2−-N), 84.61% of total ammonium nitrogen (TAN) and 72.78% of nitrate (NO3−-N). Generally, the introduction of ark shell bivalve (S. subcrenata) to the system as a biofilter provides a very low-cost bioremediation technology that could be one of the best restorations and remediation tools for mariculture effluents.

Does fire disturbance affect ant community structure? Insights from spatial co-occurrence networks.

The coexistence of several species involves a complex mix of positive and negative interactions that can be represented as networks. As much as other ecological features, patterns of multispecies co-occurrence are susceptible to anthropogenic disturbance. In ant communities, wildfires may enhance competitive interactions by benefiting active, aggressive species, and by increasing encounter probabilities through decreased space availability. We explored ant co-occurrence patterns by analysing the macro and microscopic structure of their interaction networks in burned and unburned habitats. We built co-occurrence networks using significant aggregations and segregations between species pairs as positive and negative interactions, respectively. We described aggregate network properties and microscopic structural changes by comparing species and interactions turnover between burned and unburned sites. We found no differences in the macroscopic structure of co-occurrence networks between different fire regimes. However, we detected changes in the composition of both species and negative interactions. Interaction turnover between networks of different habitats was mostly explained by rewiring of interactions between shared species rather than by species replacement. Our results reflected changes in ant communities in response to fire although there were no changes in global structural patterns. These changes in species and negative interactions suggest modifications in species roles translated into changes in the spatial distribution of ant species. The analysis of species co-occurrence networks is a useful tool to detect and visualize patterns in ant communities and to understand the mechanisms underlying the effects of disturbance on biodiversity.

Improving the Inference of Co-Occurrence Networks in the Bovine Rumen Microbiome.

The importance of the composition and signature of rumen microbial communities has gained increasing attention. One of the key techniques was to infer co-abundance networks through correlation analysis based on relative abundances. While substantial insights and progress have been made, it has been found that due to the compositional nature of data, correlation analysis derived from relative abundance could produce misleading results and spurious associations. In this study, we proposed the use of a framework including a compendium of two correlation measures and three dissimilarity metrics in an attempt to mitigate the compositional effect in the inference of significant associations in the bovine rumen microbiome. We tested the framework on rumen microbiome data including both 16S rRNA and KEGG genes associated with methane production in cattle. Based on the identification of significant positive and negative associations supported by multiple metrics, two co-occurrence networks, e.g., co-presence and mutual-exclusion networks, were constructed. Significant modules associated with methane emissions were identified. In comparison to previous studies, our analysis demonstrates that deriving microbial associations based on the correlations between relative abundances may not only lead to missing information but also produce spurious associations. To bridge together different co-presence and mutual-exclusion relations, a multiplex network model has been proposed for integrative analysis of co-occurrence networks which has great potential to support the prediction of animal phytotypes and to provide additional insights into biological mechanisms of the microbiome associated with the traits.

Review of Research Trends and Methods in Nano Environmental, Health, and Safety Risk Analysis.

Despite the many touted benefits of nanomaterials, concerns remain about their possible environmental, health, and safety (EHS) risks in terms of their toxicity, long-term accumulation effects, or dose-response relationships. The published studies on EHS risks of nanomaterials have increased significantly over the past decade and half, with most focused on nanotoxicology. Researchers are still learning about health consequences of nanomaterials and how to make environmentally responsible decisions regarding their production. This article characterizes the scientific literature on nano-EHS risk analysis to map the state-of-the-art developments in this field and chart guidance for the future directions. First, an analysis of keyword co-occurrence networks is investigated for nano-EHS literature published in the past decade to identify the intellectual turning points and research trends in nanorisk analysis studies. The exposure groups targeted in emerging nano-EHS studies are also assessed. System engineering methods for risk, safety, uncertainty, and system reliability analysis are reviewed, followed by detailed descriptions where applications of these methods are utilized to analyze nanomaterial EHS risks. Finally, the trends, methods, future directions, and opportunities of system engineering methods in nano-EHS research are discussed. The analysis of nano-EHS literature presented in this article provides important insights on risk assessment and risk management tools associated with nanotechnology, nanomanufacturing, and nano-enabled products.

Constructing a Boolean implication network to study the interactions between environmental factors and OTUs

Mining relationships between microbes and the environment they live in are crucial to understand the intrinsic mechanisms that govern cycles of carbon, nitrogen and energy in a microbial community. Building upon next‐generation sequencing technology, the selective capture of 16S rRNA genes has enabled the study of co‐occurrence patterns of microbial species from the viewpoint of complex networks, yielding successful descriptions of phenomena exhibited in a microbial community. However, since the effects of such environmental factors as temperature or soil conditions on microbes are complex, reliance on the analysis of co‐occurrence networks alone cannot elucidate such complicated effects underlying microbial communities. In this study, we apply a statistical method, which is called Boolean implications for metagenomic studies (BIMS) for extracting Boolean implications (IF‐THEN relationships) to capture the effects of environmental factors on microbial species based on 16S rRNA sequencing data. We first demonstrate the power and effectiveness of BIMS through comprehensive simulation studies and then apply it to a 16S rRNA sequencing dataset of real marine microbes. Based on a total of 6,514 pairwise relationships identified at a low false discovery rate (FDR) of 0.01, we construct a Boolean implication network between operational taxonomic units (OTUs) and environmental factors. Relationships in this network are supported by literature, and, most importantly, they bring biological insights into the effects of environmental factors on microbes. We next apply BIMS to detect three‐way relationships and show the possibility of using this strategy to explain more complex relationships within a microbial community.

Analysis of co-occurrence networks with clique occurrence information

Most of co-occurrence networks only record co-occurrence relationships between two entities, and ignore the weights of co-occurrence cliques whose size is bigger than two. However, this ignored information may help us to gain insight into the co-occurrence phenomena of systems. In this paper, we analyze co-occurrence networks with clique occurrence information (CNCI) thoroughly. First, we describe the components of CNCIs and discuss the generation of clique occurrence information. And then, to illustrate the importance and usefulness of clique occurrence information, several metrics, i.e. single occurrence rate, average size of maximal co-occurrence cliques and four types of co-occurrence coefficients etc., are given. Moreover, some applications, such as combining co-occurrence frequency with structure-oriented centrality measures, are also discussed.

Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates

BackgroundThe increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval.ResultsWe extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified.ConclusionsThe extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsa.

Identification and Analysis of Co-Occurrence Networks with NetCutter

BackgroundCo-occurrence analysis is a technique often applied in text mining, comparative genomics, and promoter analysis. The methodologies and statistical models used to evaluate the significance of association between co-occurring entities are quite diverse, however.Methodology/Principal FindingsWe present a general framework for co-occurrence analysis based on a bipartite graph representation of the data, a novel co-occurrence statistic, and software performing co-occurrence analysis as well as generation and analysis of co-occurrence networks. We show that the overall stringency of co-occurrence analysis depends critically on the choice of the null-model used to evaluate the significance of co-occurrence and find that random sampling from a complete permutation set of the bipartite graph permits co-occurrence analysis with optimal stringency. We show that the Poisson-binomial distribution is the most natural co-occurrence probability distribution when vertex degrees of the bipartite graph are variable, which is usually the case. Calculation of Poisson-binomial P-values is difficult, however. Therefore, we propose a fast bi-binomial approximation for calculation of P-values and show that this statistic is superior to other measures of association such as the Jaccard coefficient and the uncertainty coefficient. Furthermore, co-occurrence analysis of more than two entities can be performed using the same statistical model, which leads to increased signal-to-noise ratios, robustness towards noise, and the identification of implicit relationships between co-occurring entities. Using NetCutter, we identify a novel protein biosynthesis related set of genes that are frequently coordinately deregulated in human cancer related gene expression studies. NetCutter is available at http://bio.ifom-ieo-campus.it/NetCutter/).ConclusionOur approach can be applied to any set of categorical data where co-occurrence analysis might reveal functional relationships such as clinical parameters associated with cancer subtypes or SNPs associated with disease phenotypes. The stringency of our approach is expected to offer an advantage in a variety of applications.