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1.
Background
Biological experiments increasingly yield data representing large ensembles of interacting variables, making the application of advanced analytical tools a forbidding task. We present a method to extract networks of correlated activity, specifically from functional MRI data, such that: (a) network nodes represent voxels, and (b) the network links can be directed or undirected, representing temporal relationships between the nodes. The method provides a snapshot of the ongoing dynamics of the brain without sacrificing resolution, as the analysis is tractable even for very large numbers of voxels.Results
We find that, based on topological properties of the networks, the method provides enough information about the dynamics to discriminate between subtly different brain states. Moreover, the statistical regularities previously reported are qualitatively preserved, i.e. the resulting networks display scale-free and small-world topologies.Conclusion
Our method expands previous approaches to render large scale functional networks, and creates the basis for an extensive and -due to the presence of mixtures of directed and undirected links- richer motif analysis of functional relationships.2.
Minoo Ashtiani Ali Salehzadeh-Yazdi Zahra Razaghi-Moghadam Holger Hennig Olaf Wolkenhauer Mehdi Mirzaie Mohieddin Jafari 《BMC systems biology》2018,12(1):80
Background
Numerous centrality measures have been introduced to identify “central” nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures.Results
We used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network’s topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities.Conclusions
The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.3.
Background
Network alignment is one of the most common biological network comparison methods. Aligning protein-protein interaction (PPI) networks of different species is of great important to detect evolutionary conserved pathways or protein complexes across species through the identification of conserved interactions, and to improve our insight into biological systems. Global network alignment (GNA) problem is NP-complete, for which only heuristic methods have been proposed so far. Generally, the current GNA methods fall into global heuristic seed-and-extend approaches. These methods can not get the best overall consistent alignment between networks for the opinionated local seed. Furthermore These methods are lost in maximizing the number of aligned edges between two networks without considering the original structures of functional modules.Methods
We present a novel seed selection strategy for global network alignment by constructing the pairs of hub nodes of networks to be aligned into multiple seeds. Beginning from every hub seed and using the membership similarity of nodes to quantify to what extent the nodes can participate in functional modules associated with current seed topologically we align the networks by modules. By this way we can maintain the functional modules are not damaged during the heuristic alignment process. And our method is efficient in resolving the fatal problem of most conventional algorithms that the initialization selected seeds have a direct influence on the alignment result. The similarity measures between network nodes (e.g., proteins) include sequence similarity, centrality similarity, and dynamic membership similarity and our algorithm can be called Multiple Hubs-based Alignment (MHA).Results
When applying our seed selection strategy to several pairs of real PPI networks, it is observed that our method is working to strike a balance, extending the conserved interactions while maintaining the functional modules unchanged. In the case study, we assess the effectiveness of MHA on the alignment of the yeast and fly PPI networks. Our method outperforms state-of-the-art algorithms at detecting conserved functional modules and retrieves in particular 86% more conserved interactions than IsoRank.Conclusions
We believe that our seed selection strategy will lead us to obtain more topologically and biologically similar alignment result. And it can be used as the reference and complement of other heuristic methods to seek more meaningful alignment results.4.
Background
Cellular behaviors are governed by interaction networks among biomolecules, for example gene regulatory and signal transduction networks. An often used dynamic modeling framework for these networks, Boolean modeling, can obtain their attractors (which correspond to cell types and behaviors) and their trajectories from an initial state (e.g. a resting state) to the attractors, for example in response to an external signal. The existing methods however do not elucidate the causal relationships between distant nodes in the network.Results
In this work, we propose a simple logic framework, based on categorizing causal relationships as sufficient or necessary, as a complement to Boolean networks. We identify and explore the properties of complex subnetworks that are distillable into a single logic relationship. We also identify cyclic subnetworks that ensure the stabilization of the state of participating nodes regardless of the rest of the network. We identify the logic backbone of biomolecular networks, consisting of external signals, self-sustaining cyclic subnetworks (stable motifs), and output nodes. Furthermore, we use the logic framework to identify crucial nodes whose override can drive the system from one steady state to another. We apply these techniques to two biological networks: the epithelial-to-mesenchymal transition network corresponding to a developmental process exploited in tumor invasion, and the network of abscisic acid induced stomatal closure in plants. We find interesting subnetworks with logical implications in these networks. Using these subgraphs and motifs, we efficiently reduce both networks to succinct backbone structures.Conclusions
The logic representation identifies the causal relationships between distant nodes and subnetworks. This knowledge can form the basis of network control or used in the reverse engineering of networks.5.
Background
The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources available on line including experimental datasets and computational tools, the complex language of DHSs remains incompletely understood.Methods
Here, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) which combined Inception like networks with a gating mechanism for the response of multiple patterns and longterm association in DNA sequences to predict multi-scale DHSs in Arabidopsis, rice and Homo sapiens.Results
Our method obtains 0.961 area under curve (AUC) on Arabidopsis, 0.969 AUC on rice and 0.918 AUC on Homo sapiens.Conclusions
Our method provides an efficient and accurate way to identify multi-scale DHSs sequences by deep learning.6.
Background
The identification of genes responsible for human inherited diseases is one of the most challenging tasks in human genetics. Recent studies based on phenotype similarity and gene proximity have demonstrated great success in prioritizing candidate genes for human diseases. However, most of these methods rely on a single protein-protein interaction (PPI) network to calculate similarities between genes, and thus greatly restrict the scope of application of such methods. Meanwhile, independently constructed and maintained PPI networks are usually quite diverse in coverage and quality, making the selection of a suitable PPI network inevitable but difficult.Methods
We adopt a linear model to explain similarities between disease phenotypes using gene proximities that are quantified by diffusion kernels of one or more PPI networks. We solve this model via a Bayesian approach, and we derive an analytic form for Bayes factor that naturally measures the strength of association between a query disease and a candidate gene and thus can be used as a score to prioritize candidate genes. This method is intrinsically capable of integrating multiple PPI networks.Results
We show that gene proximities calculated from PPI networks imply phenotype similarities. We demonstrate the effectiveness of the Bayesian regression approach on five PPI networks via large scale leave-one-out cross-validation experiments and summarize the results in terms of the mean rank ratio of known disease genes and the area under the receiver operating characteristic curve (AUC). We further show the capability of our approach in integrating multiple PPI networks.Conclusions
The Bayesian regression approach can achieve much higher performance than the existing CIPHER approach and the ordinary linear regression method. The integration of multiple PPI networks can greatly improve the scope of application of the proposed method in the inference of disease genes.7.
8.
Jamie V. de Seymour Stephanie Tu Xiaoling He Hua Zhang Ting-Li Han Philip N. Baker Karolina Sulek 《Metabolomics : Official journal of the Metabolomic Society》2018,14(6):79
Introduction
Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms.Objective
Investigate the maternal hair metabolome for predictive biomarkers of ICP.Methods
The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography–mass spectrometry.Results
Of 105 metabolites detected in hair, none were significantly associated with ICP.Conclusion
Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.9.
Background
An artificial neural network approach was chosen to model the outcome of the complex signaling pathways in the gastro-intestinal tract and other peripheral organs that eventually produce the satiety feeling in the brain upon feeding.Methods
A multilayer feed-forward neural network was trained with sets of experimental data relating concentration-time courses of plasma satiety hormones to Visual Analog Scales (VAS) scores. The network successfully predicted VAS responses from sets of satiety hormone data obtained in experiments using different food compositions.Results
The correlation coefficients for the predicted VAS responses for test sets having i) a full set of three satiety hormones, ii) a set of only two satiety hormones, and iii) a set of only one satiety hormone were 0.96, 0.96, and 0.89, respectively. The predicted VAS responses discriminated the satiety effects of high satiating food types from less satiating food types both in orally fed and ileal infused forms.Conclusions
From this application of artificial neural networks, one may conclude that neural network models are very suitable to describe situations where behavior is complex and incompletely understood. However, training data sets that fit the experimental conditions need to be available.10.
11.
Background
Metabolic disorders such as obesity and diabetes are diseases which develop gradually over time in an individual and through the perturbations of genes. Systematic experiments tracking disease progression at gene level are usually conducted giving a temporal microarray data. There is a need for developing methods to analyze such complex data and extract important proteins which could be involved in temporal progression of the data and hence progression of the disease.Results
In the present study, we have considered a temporal microarray data from an experiment conducted to study development of obesity and diabetes in mice. We have used this data along with an available Protein-Protein Interaction network to find a network of interactions between proteins which reproduces the next time point data from previous time point data. We show that the resulting network can be mined to identify critical nodes involved in the temporal progression of perturbations. We further show that published algorithms can be applied on such connected network to mine important proteins and show an overlap between outputs from published and our algorithms. The importance of set of proteins identified was supported by literature as well as was further validated by comparing them with the positive genes dataset from OMIM database which shows significant overlap.Conclusions
The critical proteins identified from algorithms can be hypothesized to play important role in temporal progression of the data.12.
Background
During the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue.Methods
We combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations.Results
We apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data.Conclusions
We clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation.13.
Background
Similar diseases are always caused by similar molecular origins, such as diasease-related protein-coding genes (PCGs). And the molecular associations reflect their similarity. Therefore, current methods for calculating disease similarity often utilized functional interactions of PCGs. Besides, the existing methods have neglected a fact that genes could also be associated in the gene functional network (GFN) based on intermediate nodes.Methods
Here we presented a novel method, InfDisSim, to deduce the similarity of diseases. InfDisSim utilized the whole network based on random walk with damping to model the information flow. A benchmark set of similar disease pairs was employed to evaluate the performance of InfDisSim.Results
The region beneath the receiver operating characteristic curve (AUC) was calculated to assess the performance. As a result, InfDisSim reaches a high AUC (0.9786) which indicates a very good performance. Furthermore, after calculating the disease similarity by the InfDisSim, we reconfirmed that similar diseases tend to have common therapeutic drugs (Pearson correlation γ2?=?0.1315, p?=?2.2e-16). Finally, the disease similarity computed by infDisSim was employed to construct a miRNA similarity network (MSN) and lncRNA similarity network (LSN), which were further exploited to predict potential associations of lncRNA-disease pairs and miRNA-disease pairs, respectively. High AUC (0.9893, 0.9007) based on leave-one-out cross validation shows that the LSN and MSN is very appropriate for predicting novel disease-related lncRNAs and miRNAs, respectively.Conclusions
The high AUC based on benchmark data indicates the method performs well. The method is valuable in the prediction of disease-related lncRNAs and miRNAs.14.
15.
Yahya Al-Abed Emma Gray Konrad Wolfe Gavin W Watters Jonathan M Philpott 《International Seminars in Surgical Oncology : ISSO》2008,5(1):14
Background
Hurthle cell carcinoma of the thyroid is a rare form of thyroid cancer. It may present as a low grade tumour or can present as a more aggressive metastatic carcinoma. Hurthle cell carcinoma has the highest incidence of metastasis among all differentiated thyroid cancers. Most commonly haematogenous spread to lungs, bones and brain, however spread to regional lymph nodes is not uncommon. The breast is a rare site for metastasis from extramammary sources. We present the first case of breast metastasis from Hurthle cell carcinoma of the thyroid.Case presentation
We report a 77 year old lady who had total thyroidectomy and bilateral neck dissection followed by radiotherapy for a high grade metastatic Hurthle cell carcinoma of the thyroid. Ten months later she presented to the breast clinic with left breast lump and a lump at the left axilla. Fine needle aspiration cytology of the lumps and histology after wide local excision of the breast lump confirmed metastatic Hurthle cell carcinoma.Conclusion
The presence of breast lumps in patients with history of extramammary cancer should raise the possibility of metastasis.16.
N. Cesbron A.-L. Royer Y. Guitton A. Sydor B. Le Bizec G. Dervilly-Pinel 《Metabolomics : Official journal of the Metabolomic Society》2017,13(8):99
Introduction
Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.Objectives
In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.Methods
The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.Results
A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.Conclusion
The workflow generated repeatable and informative fingerprints for robust metabolome characterization.17.
Background
Reconstruction of protein-protein interaction or metabolic networks based on expression data often involves in silico predictions, while on the other hand, there are unspecific networks of in vivo interactions derived from knowledge bases.We analyze networks designed to come as close as possible to data measured in vivo, both with respect to the set of nodes which were taken to be expressed in experiment as well as with respect to the interactions between them which were taken from manually curated databasesResults
A signaling network derived from the TRANSPATH database and a metabolic network derived from KEGG LIGAND are each filtered onto expression data from breast cancer (SAGE) considering different levels of restrictiveness in edge and vertex selection.We perform several validation steps, in particular we define pathway over-representation tests based on refined null models to recover functional modules. The prominent role of the spindle checkpoint-related pathways in breast cancer is exhibited. High-ranking key nodes cluster in functional groups retrieved from literature. Results are consistent between several functional and topological analyses and between signaling and metabolic aspects.Conclusions
This construction involved as a crucial step the passage to a mammalian protein identifier format as well as to a reaction-based semantics of metabolism. This yielded good connectivity but also led to the need to perform benchmark tests to exclude loss of essential information. Such validation, albeit tedious due to limitations of existing methods, turned out to be informative, and in particular provided biological insights as well as information on the degrees of coherence of the networks despite fragmentation of experimental data.Key node analysis exploited the networks for potentially interesting proteins in view of drug target prediction.18.
Background
To analyze the p42.3 gene expression in gastric cancer (GC) cell, find the relationship between protein structure and function, establish the regulatory network of p42.3 protein molecule and then to obtain the optimal regulatory pathway.Methods
The expression of p42.3 gene was analyzed by RT-PCR, Western Blot and other biotechnologies. The relationship between the spatial conformation of p42.3 protein molecule and its function was analyzed using bioinformatics, MATLAB and related knowledge about protein structure and function. Furthermore, based on similarity algorithm of spatial layered spherical coordinate, we compared p42.3 molecule with several similar structured proteins which are known for the function, screened the characteristic nodes related to tumorigenesis and development, and established the multi variable relational model between p42.3 protein expression, cell cycle regulation and biological characteristics in the level of molecular regulatory networks. Finally, the optimal regulatory network was found by using Bayesian network.Results
(1) The expression amount of p42.3 in G1 and M phase was higher than that in S and G2 phase; (2) The space coordinate systems of different structural domains of p42.3 protein were established in Matlab7.0 software; (3) The optimal pathway of p42.3 gene in protein regulatory network in gastric cancer is Ras protein, Raf-1 protein, MEK, MAPK kinase, MAPK, tubulin, spindle protein, centromere protein and tumor.Conclusion
It is of vital significance for mechanism research to find out the action pathway of p42.3 in protein regulatory network, since p42.3 protein plays an important role in the generation and development of GC.19.
Background
Inference of gene networks from expression data is an important problem in computational biology. Many algorithms have been proposed for solving the problem efficiently. However, many of the available implementations are programming libraries that require users to write code, which limits their accessibility.Results
We have developed a tool called CyNetworkBMA for inferring gene networks from expression data that integrates with Cytoscape. Our application offers a graphical user interface for networkBMA, an efficient implementation of Bayesian Model Averaging methods for network construction. The client-server architecture of CyNetworkBMA makes it possible to distribute or centralize computation depending on user needs.Conclusions
CyNetworkBMA is an easy-to-use tool that makes network inference accessible to non-programmers through seamless integration with Cytoscape. CyNetworkBMA is available on the Cytoscape App Store at http://apps.cytoscape.org/apps/cynetworkbma.20.