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1.
Background
Human cancers are complex ecosystems composed of cells with distinct molecular signatures. Such intratumoral heterogeneity poses a major challenge to cancer diagnosis and treatment. Recent advancements of single-cell techniques such as scRNA-seq have brought unprecedented insights into cellular heterogeneity. Subsequently, a challenging computational problem is to cluster high dimensional noisy datasets with substantially fewer cells than the number of genes.Methods
In this paper, we introduced a consensus clustering framework conCluster, for cancer subtype identification from single-cell RNA-seq data. Using an ensemble strategy, conCluster fuses multiple basic partitions to consensus clusters.Results
Applied to real cancer scRNA-seq datasets, conCluster can more accurately detect cancer subtypes than the widely used scRNA-seq clustering methods. Further, we conducted co-expression network analysis for the identified melanoma subtypes.Conclusions
Our analysis demonstrates that these subtypes exhibit distinct gene co-expression networks and significant gene sets with different functional enrichment.2.
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Background
Genome-scale metabolic models provide an opportunity for rational approaches to studies of the different reactions taking place inside the cell. The integration of these models with gene regulatory networks is a hot topic in systems biology. The methods developed to date focus mostly on resolving the metabolic elements and use fairly straightforward approaches to assess the impact of genome expression on the metabolic phenotype.Results
We present here a method for integrating the reverse engineering of gene regulatory networks into these metabolic models. We applied our method to a high-dimensional gene expression data set to infer a background gene regulatory network. We then compared the resulting phenotype simulations with those obtained by other relevant methods.Conclusions
Our method outperformed the other approaches tested and was more robust to noise. We also illustrate the utility of this method for studies of a complex biological phenomenon, the diauxic shift in yeast.5.
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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.7.
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Background
Gene-environment interactions are often mediated though gene networks in which gene expression products interact with other network components to dictate network activity levels, which in turn determines the fitness of the host cell in specific environments. Even though a gene network is the right context for studying gene-environment interactions, we have little understanding on how systematic genetic perturbations affects fitness in the context of a gene network.Results
Here we examine the effect of combinatorial gene dosage alterations on gene network activity and cellular fitness. Using the galactose utilization pathway as a model network in diploid yeast, we reduce the copy number of four regulatory genes (GAL2, GAL3, GAL4, GAL80) from two to one, and measure the activity of the perturbed networks. We integrate these results with competitive fitness measurements made in six different rationally-designed environments containing different galactose concentrations representing the natural induction spectrum of the galactose network. In the lowest galactose environment, we find a nonlinear relationship between gene expression and fitness while high galactose environments lead to a linear relationship between the two with a saturation regime reached at a sufficiently high galactose concentration. We further uncover environment-specific relevance of the different network components for dictating the relationship between the network activity and organismal fitness, indicating that none of the network components are redundant.Conclusions
These results provide experimental support to the hypothesis that dynamic changes in the environment throughout natural evolution is key to structuring natural gene networks in a multi-component fashion, which robustly provides protection against population extinction in different environments.10.
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Ferran Casbas Pinto Srinivarao Ravipati David A. Barrett T. Charles Hodgman 《Metabolomics : Official journal of the Metabolomic Society》2017,13(7):81
Introduction
It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.Objectives
This work simplifies this process.Methods
A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.Results
The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.Conclusion
This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.13.
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.14.
Background
Many essential cellular processes, such as cellular metabolism, transport, cellular metabolism and most regulatory mechanisms, rely on physical interactions between proteins. Genome-wide protein interactome networks of yeast, human and several other animal organisms have already been established, but this kind of network reminds to be established in the field of plant.Results
We first predicted the protein protein interaction in Arabidopsis thaliana with methods, including ortholog, SSBP, gene fusion, gene neighbor, phylogenetic profile, coexpression, protein domain, and used Naïve Bayesian approach next to integrate the results of these methods and text mining data to build a genome-wide protein interactome network. Furthermore, we adopted the data of GO enrichment analysis, pathway, published literature to validate our network, the confirmation of our network shows the feasibility of using our network to predict protein function and other usage.Conclusions
Our interactome is a comprehensive genome-wide network in the organism plant Arabidopsis thaliana, and provides a rich resource for researchers in related field to study the protein function, molecular interaction and potential mechanism under different conditions.15.
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.16.
Shih-Kuang Yang Yu-Chao Wang Chun-Cheih Chao Yung-Jen Chuang Chung-Yu Lan Bor-Sen Chen 《BMC medical genomics》2010,3(1):19
Background
Development in systems biology research has accelerated in recent years, and the reconstructions for molecular networks can provide a global view to enable in-depth investigation on numerous system properties in biology. However, we still lack a systematic approach to reconstruct the dynamic protein-protein association networks at different time stages from high-throughput data to further analyze the possible cross-talks among different signaling/regulatory pathways.Methods
In this study we integrated protein-protein interactions from different databases to construct the rough protein-protein association networks (PPANs) during TNFα-induced inflammation. Next, the gene expression profiles of TNFα-induced HUVEC and a stochastic dynamic model were used to rebuild the significant PPANs at different time stages, reflecting the development and progression of endothelium inflammatory responses. A new cross-talk ranking method was used to evaluate the potential core elements in the related signaling pathways of toll-like receptor 4 (TLR-4) as well as receptors for tumor necrosis factor (TNF-R) and interleukin-1 (IL-1R).Results
The highly ranked cross-talks which are functionally relevant to the TNFα pathway were identified. A bow-tie structure was extracted from these cross-talk pathways, suggesting the robustness of network structure, the coordination of signal transduction and feedback control for efficient inflammatory responses to different stimuli. Further, several characteristics of signal transduction and feedback control were analyzed.Conclusions
A systematic approach based on a stochastic dynamic model is proposed to generate insight into the underlying defense mechanisms of inflammation via the construction of corresponding signaling networks upon specific stimuli. In addition, this systematic approach can be applied to other signaling networks under different conditions in different species. The algorithm and method proposed in this study could expedite prospective systems biology research when better experimental techniques for protein expression detection and microarray data with multiple sampling points become available in the future.17.
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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.19.
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Binhua Tang Xuechen Wu Ge Tan Su-Shing Chen Qing Jing Bairong Shen 《BMC systems biology》2010,4(Z2):S3