Studies have shown that the number of microbes in humans is almost 10 times that of cells. These microbes have been proven to play an important role in a variety of physiological processes, such as enhancing immunity,...
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Studies have shown that the number of microbes in humans is almost 10 times that of cells. These microbes have been proven to play an important role in a variety of physiological processes, such as enhancing immunity, improving the digestion of gastrointestinal tract and strengthening metabolic function. In addition, in recent years, more and more research results have indicated that there are close relationships between the emergence of the human noncommunicable diseases and microbes, which provides a novel insight for us to further understand the pathogenesis of the diseases. An in-depth study about the relationships between diseases and microbes will not only contribute to exploring new strategies for the diagnosis and treatment of diseases but also significantly heighten the efficiency of new drugs development. However, applying the methods of biological experimentation to reveal the microbe-disease associations is costly and inefficient. In recent years, more and more researchers have constructed multiple computational models to predict microbes that are potentially associated with diseases. Here, we start with a brief introduction of microbes and databases as well as web servers related to them. Then, we mainly introduce four kinds of computational models, including score function-based models, network algorithm-based models, machine learning-based models and experimental analysis-based models. Finally, we summarize the advantages as well as disadvantages of them and set the direction for the future work of revealing microbe-disease associations based on computational models. We firmly believe that computational models are expected to be important tools in large-scale predictions of disease-related microbes.
Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules with a variety of biological functions. Studies have shown that circRNAs are involved in a variety of biological processes and p...
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Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules with a variety of biological functions. Studies have shown that circRNAs are involved in a variety of biological processes and play an important role in the development of various complex diseases, so the identification of circRNA-disease associations would contribute to the diagnosis and treatment of diseases. In this review, we summarize the discovery, classifications and functions of circRNAs and introduce four important diseases associated with circRNAs. Then, we list some significant and publicly accessible databases containing comprehensive annotation resources of circRNAs and experimentally validated circRNA-disease associations. Next, we introduce some state-of-the-art computational models for predicting novel circRNA-disease associations and divide them into two categories, namely network algorithm-based and machine learning-based models. Subsequently, several evaluation methods of prediction performance of these computational models are summarized. Finally, we analyze the advantages and disadvantages of different types of computational models and provide some suggestions to promote the development of circRNA-disease association identification from the perspective of the construction of new computational models and the accumulation of circRNA-related data.
Long non-coding RNAs (lncRNAs) have recently acquired a boost of interest for their implication in several biological conditions. However, many of these elements are not yet characterized. LErNet is a method to in sil...
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ISBN:
(纸本)9781728114620
Long non-coding RNAs (lncRNAs) have recently acquired a boost of interest for their implication in several biological conditions. However, many of these elements are not yet characterized. LErNet is a method to in silico define and predict the roles of lncRNAs. The core of the approach is a network expansion algorithm which enriches the genomic context of lncRNAs. The context is built by integrating the genes encoding proteins that are found next to the non-coding elements both at genomic and system level. The pipeline is particularly useful in situations where the functions of discovered lncRNAs are not yet known. The results show both the outperformance of LErNet compared to enrichment approaches in literature and its robustness in case of partially missing context information. LErNet is provided as an R package.
Background: The analysis of tissue-specific protein interaction networks and their functional enrichment in pathological and normal tissues provides insights on the etiology of diseases. The Pan-cancer proteomic proje...
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Background: The analysis of tissue-specific protein interaction networks and their functional enrichment in pathological and normal tissues provides insights on the etiology of diseases. The Pan-cancer proteomic project, in The Cancer Genome Atlas, collects protein expressions in human cancers and it is a reference resource for the functional study of cancers. However, established protocols to infer interaction networks from protein expressions are still missing. Results: We have developed a methodology called Inference network Based on iReflndex Analysis (INBIA) to accurately correlate proteomic inferred relations to protein-protein interaction (PPI) networks. INBIA makes use of 14 network inference methods on protein expressions related to 16 cancer types. It uses as reference model the iReflndex human PPI network. Predictions are validated through non-interacting and tissue specific PPI networks resources. The first, Negatome, takes into account likely non-interacting proteins by combining both structure properties and literature mining. The latter, TissueNet and GIANT, report experimentally verified PPIs in more than 50 human tissues. The reliability of the proposed methodology is assessed by comparing INBIA with PERA, a tool which infers protein interaction networks from Pathway Commons, by both functional and topological analysis. Conclusion: Results show that INBIA is a valuable approach to predict proteomic interactions in pathological conditions starting from the current knowledge of human protein interactions.
As one of the most important paradigms of recurrent neural networks, the echo state network (ESN) has been applied to a wide range of fields, from robotics to medicine, finance, and language processing. A key feature ...
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As one of the most important paradigms of recurrent neural networks, the echo state network (ESN) has been applied to a wide range of fields, from robotics to medicine, finance, and language processing. A key feature of the ESN paradigmis its reservoir-a directed and weighted network of neurons that projects the input time series into a high-dimensional space where linear regression or classification can be applied. By analyzing the dynamics of the reservoir we show that the ensemble of eigenvalues of the network contributes to the ESN memory capacity. Moreover, we find that adding short loops to the reservoir network can tailor ESN for specific tasks and optimize learning. We validate our findings by applying ESN to forecast both synthetic and real benchmark time series. Our results provide a simple way to design task-specific ESN and offer deep insights for other recurrent neural networks.
The vast and complex wealth of information available to researchers often leads to a systematic review, which involves a detailed and comprehensive plan and search strategy with the goal of identifying, appraising, an...
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The vast and complex wealth of information available to researchers often leads to a systematic review, which involves a detailed and comprehensive plan and search strategy with the goal of identifying, appraising, and synthesizing all relevant studies on a particular topic. A metaâanalysis, conducted ideally as part of a comprehensive systematic review, statistically synthesizes evidence from multiple independent studies to produce one overall conclusion. The increasingly widespread use of metaâanalysis has led to growing interest in metaâanalytic methods for rare events and sparse data. Conventional approaches tend to perform very poorly in such settings. Recent work in this area has provided options for sparse data, but these are still often hampered when heterogeneity across the available studies differs based on treatment group. Heterogeneity arises when participants in a study are more correlated than participants across studies, often stemming from differences in the administration of the treatment, study design, or measurement of the outcome. We propose several new exact methods that accommodate this common contingency, providing more reliable statistical tests when such patterns on heterogeneity are observed. First, we develop a permutationâbased approach that can also be used as a basis for computing exact confidence intervals when estimating the effect size. Second, we extend the permutationâbased approach to the network metaâanalysis setting. Third, we develop a new exact confidence distribution approach for effect size estimation. We show these new methods perform markedly better than traditional methods when events are rare, and heterogeneity is present.
We present a method for automatically discovering signaling pathways from time-resolved phosphoproteomic data. The Temporal Pathway Synthesizer (TPS) algorithm uses constraint-solving techniques first developed in the...
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We present a method for automatically discovering signaling pathways from time-resolved phosphoproteomic data. The Temporal Pathway Synthesizer (TPS) algorithm uses constraint-solving techniques first developed in the context of formal verification to explore paths in an interaction network. It systematically eliminates all candidate structures for a signaling pathway where a protein is activated or inactivated before its upstream regulators. The algorithm can model more than one hundred thousand dynamic phosphosites and can discover pathway members that are not differentially phosphorylated. By analyzing temporal data, TPS defines signaling cascades without needing to experimentally perturb individual proteins. It recovers known pathways and proposes pathway connections when applied to the human epidermal growth factor and yeast osmotic stress responses. Independent kinase mutant studies validate predicted substrates in the TPS osmotic stress pathway.
Background: ToxCast data have been used to develop models for predicting in vivo toxicity. To predict the in vivo toxicity of a new chemical using a ToxCast data based model, its ToxCast bioactivity data are needed bu...
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Background: ToxCast data have been used to develop models for predicting in vivo toxicity. To predict the in vivo toxicity of a new chemical using a ToxCast data based model, its ToxCast bioactivity data are needed but not normally available. The capability of predicting ToxCast bioactivity data is necessary to fully utilize ToxCast data in the risk assessment of chemicals. Objectives: We aimed to understand and elucidate the relationships between the chemicals and bioactivity data of the assays in ToxCast and to develop a network analysis based method for predicting ToxCast bioactivity data. Methods: We conducted modularity analysis on a quantitative network constructed from ToxCast data to explore the relationships between the assays and chemicals. We further developed Nebula (neighbor-edges based and unbiased leverage algorithm) for predicting ToxCast bioactivity data. Results: Modularity analysis on the network constructed from ToxCast data yielded seven modules. Assays and chemicals in the seven modules were distinct Leave-one-out cross-validation yielded a Q(2) of 0.5416, indicating ToxCast bioactivity data can be predicted by Nebula. Prediction domain analysis showed some types of ToxCast assay data could be more reliably predicted by Nebula than others. Conclusions: network analysis is a promising approach to understand ToxCast data. Nebula is an effective algorithm for predicting ToxCast bioactivity data, helping fully utilize ToxCast data in the risk assessment of chemicals. Published by Elsevier Ltd.
Hash table is one of the most fundamental and critical data structures for membership query and maintenance. However, the performance of a standard hash table degrades greatly when the hash collision is large due to h...
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ISBN:
(纸本)9781509063864
Hash table is one of the most fundamental and critical data structures for membership query and maintenance. However, the performance of a standard hash table degrades greatly when the hash collision is large due to high load factor or unpredictable dynamic membership updates, especially per-packet updates in network processing. In this paper, we shape a hash table from the conventional slim-and-tall style to a wide-and-short style by facilitating an extension of logical cache block. Then, a cache aware hash table (CaHash) is given and explored in detail. Based on an observation that the operation sequences may be in a potential and probabilistic successive order, especially for network applications, a rewritable hash table (RwHash) is finally proposed, which provides two rewritable policies to dynamically move elements within a bucket when updating. Theoretical analysis shows that, no matter what load factor and collision are, RwHash can achieve near-optimal performance the same as the performance when a standard hash table in the case of no collision. Real experiments show that RwHash can achieve 4.10 times speedup in some parameter and even more with different configurations than a standard hash table in the case of heavy collisions. Our approaches are elegantly practical in implementation for both software and hardware.
In this paper we present a novel on-line NFV (network Function Virtualization) orchestration algorithm for edge computing infrastructure providers that operate in a heterogeneous cloud environment. The goal of our alg...
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In this paper we present a novel on-line NFV (network Function Virtualization) orchestration algorithm for edge computing infrastructure providers that operate in a heterogeneous cloud environment. The goal of our algorithm is to minimize the usage of computing resources which are offered by a public cloud provider (e.g., Amazon Web Services), while fulfilling the required networking related constraints (latency, bandwidth) of the services to be deployed. We propose a reference network architecture which acts as a test environment for the evaluation of our algorithm. During the measurements, we compare our results to the optimal solution provided by an ILP-based solver.
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