scholarly journals Unravelling Nature's Networks: From Microarray and Proteomic Analysis to Systems Biology: University of Sheffield, 21–22 July 2003

2003 ◽  
Vol 25 (6) ◽  
pp. 40-41
Author(s):  
Nick Monk ◽  
Neil Lawrence
Keyword(s):  
Gene Expression ◽  
Experimental Data ◽  
Mathematical Model ◽  
Protein Interactions ◽  
Large Scale ◽  
Dynamic Behaviour ◽  
Biochemical Networks ◽  
Data Sets ◽  
Variable Quality

The robust and adaptable behaviours of cells and tissues depend on the operation of complex regulatory biochemical networks. The elucidation of the structure and functioning of such networks poses many daunting challenges. Recently developed experimental techniques, such as large-scale profiling of gene expression and protein interactions, provide unprecedented amounts of information on the molecular composition of cells. The size (and often variable quality) of the resulting data sets necessitates the use of sophisticated computational schemes for the analysis, mining and integration of the data. In all but the simplest cases, the complexity of the networks is such that it is impossible to provide an intuitive picture of the principles governing their dynamic behaviour without synthesizing the experimental data into a coherent mathematical model of the underlying system.

Thermal Laser Separation – A Novel Dicing Technology Fulfilling the Demands of Volume Manufacturing of 4H-SiC Devices

2015 ◽  
Vol 821-823 ◽  
pp. 528-532 ◽  
Author(s):  
Dirk Lewke ◽  
Karl Otto Dohnke ◽  
Hans Ulrich Zühlke ◽  
Mercedes Cerezuela Barret ◽  
Martin Schellenberger ◽  
...  
Keyword(s):  
Experimental Data ◽  
Process Control ◽  
Tool Wear ◽  
Large Scale ◽  
State Of The Art ◽  
Scale Production ◽  
High Quality ◽  
Large Scale Production ◽  
Laser Separation

One challenge for volume manufacturing of 4H-SiC devices is the state-of-the-art wafer dicing technology – the mechanical blade dicing which suffers from high tool wear and low feed rates. In this paper we discuss Thermal Laser Separation (TLS) as a novel dicing technology for large scale production of SiC devices. We compare the latest TLS experimental data resulting from fully processed 4H-SiC wafers with results obtained by mechanical dicing technology. Especially typical product relevant features like process control monitoring (PCM) structures and backside metallization, quality of diced SiC-devices as well as productivity are considered. It could be shown that with feed rates up to two orders of magnitude higher than state-of-the-art, no tool wear and high quality of diced chips, TLS has a very promising potential to fulfill the demands of volume manufacturing of 4H-SiC devices.

scholarly journals BSO-MV: An Optimized Multiview Clustering Approach for Items Recommendation in Social Networks

2021 ◽  
Vol 27 (7) ◽  
pp. 667-692
Author(s):  
Lamia Berkani ◽  
Lylia Betit ◽  
Louiza Belarif
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Data Sets ◽  
Large Scale Data ◽  
Recommendation Algorithms ◽  
Clustering Approach ◽  
Real World Datasets ◽  
Multiview Clustering ◽  
Improving Accuracy

Clustering-based approaches have been demonstrated to be efficient and scalable to large-scale data sets. However, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we propose in this article an optimized multiview clustering approach for the recommendation of items in social networks. First, the selection of the initial medoids is optimized using the Bees Swarm optimization algorithm (BSO) in order to generate better partitions (i.e. refining the quality of medoids according to the objective function). Then, the multiview clustering (MV) is applied, where users are iteratively clustered from the views of both rating patterns and social information (i.e. friendships and trust). Finally, a framework is proposed for testing the different alternatives, namely: (1) the standard recommendation algorithms; (2) the clustering-based and the optimized clustering-based recommendation algorithms using BSO; and (3) the MV and the optimized MV (BSO-MV) algorithms. Experimental results conducted on two real-world datasets demonstrate the effectiveness of the proposed BSO-MV algorithm in terms of improving accuracy, as it outperforms the existing related approaches and baselines.

scholarly journals Distributed Bayesian Networks Reconstruction on the Whole Genome Scale

2015 ◽  
Author(s):  
Alina Frolova ◽  
Bartek Wilczynski
Keyword(s):  
Experimental Data ◽  
Bayesian Networks ◽  
Graphical Models ◽  
Polynomial Time ◽  
Protein Interactions ◽  
Regulatory Networks ◽  
Large Scale ◽  
External Information ◽  
Whole Genome ◽  
Wide Audience

AbstractBackgroundBayesian networks are directed acyclic graphical models widely used to represent the probabilistic relationships between random variables. They have been applied in various biological contexts, including gene regulatory networks and protein-protein interactions inference. Generally, learning Bayesian networks from experimental data is NP-hard, leading to widespread use of heuristic search methods giving suboptimal results. However, in cases when the acyclicity of the graph can be externally ensured, it is possible to find the optimal network in polynomial time. While our previously developed tool BNFinder implements polynomial time algorithm, reconstructing networks with the large amount of experimental data still leads to computations on single CPU growing exceedingly.ResultsIn the present paper we propose parallelized algorithm designed for multi-core and distributed systems and its implementation in the improved version of BNFinder - tool for learning optimal Bayesian networks. The new algorithm has been tested on different simulated and experimental datasets showing that it has much better efficiency of parallelization than the previous version. BNFinder gives comparable results in terms of accuracy with respect to current state-of-the-art inference methods, giving significant advantage in cases when external information such as regulators list or prior edge probability can be introduced.ConclusionsWe show that the new method can be used to reconstruct networks in the size range of thousands of genes making it practically applicable to whole genome datasets of prokaryotic systems and large components of eukaryotic genomes. Our benchmarking results on realistic datasets indicate that the tool should be useful to wide audience of researchers interested in discovering dependencies in their large-scale transcriptomic datasets.

Protein Interaction Networks

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Large Scale ◽  
Scientific Literature ◽  
Genetic Interaction ◽  
Genetic Interactions ◽  
Protein Protein Interactions ◽  
Expression Array ◽  
Biological Responses ◽  
Altered Gene

Genes and proteins interact with each other in many complicated ways. For example, proteins can interact directly with each other to form complexes or to modify each other so that their function is altered. Gene expression can be repressed or induced by transcription factor proteins. In addition there are countless other types of interactions. They constitute the key physiological steps in regulating or initiating biological responses. For example the binding of transcription factors to DNA triggers the assembly of the RNA assembly machinery that transcribes the mRNA that then is used as the template for protein production. Interactions such as these have been carefully elucidated and have been described in great detail in the scientific literature. Modern assays such as yeast-2-hybrid screens offer rapid means to ascertain many of the potential protein–protein interactions in an organism in a large-scale approach. In addition, other experimental modalities such as gene-expression array assays offer indirect clues about possible genetic interactions. One area that has been greatly explored in the bioinformatics literature is the possibility of learning genetic or protein networks, both from the scientific literature and from large-scale experimental data. Indeed, as we get to know more and more genes, it will become increasingly important to appreciate their interactions with each other. An understanding of the interactions between genes and proteins in a network allows for a meaningful global view of the organism and its physiology and is necessary to better understand biology. In this chapter we will explore methods to either (1) mine the scientific literature to identify documented genetic interactions and build networks of genes or (2) to confirm protein interactions that have been proposed experimentally. Our focus here is on direct physical protein–protein interactions, though the techniques described could be extended to any type of biological interaction between genes or proteins. There are multiple steps that must be addressed in identifying genetic interaction information contained within the text. After compiling the necessary documents and text, the first step is to identify gene and protein names in the text.

scholarly journals Clustering analysis of tumor metabolic networks

2020 ◽  
Vol 21 (S10) ◽  
Author(s):  
Ichcha Manipur ◽  
Ilaria Granata ◽  
Lucia Maddalena ◽  
Mario R. Guarracino
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Protein Interactions ◽  
Biological Networks ◽  
Clustering Analysis ◽  
Large Scale ◽  
Metabolic Networks ◽  
Computational Time ◽  
Expression Data ◽  
Metabolic Models

Abstract Background Biological networks are representative of the diverse molecular interactions that occur within cells. Some of the commonly studied biological networks are modeled through protein-protein interactions, gene regulatory, and metabolic pathways. Among these, metabolic networks are probably the most studied, as they directly influence all physiological processes. Exploration of biochemical pathways using multigraph representation is important in understanding complex regulatory mechanisms. Feature extraction and clustering of these networks enable grouping of samples obtained from different biological specimens. Clustering techniques separate networks depending on their mutual similarity. Results We present a clustering analysis on tissue-specific metabolic networks for single samples from three primary tumor sites: breast, lung, and kidney cancer. The metabolic networks were obtained by integrating genome scale metabolic models with gene expression data. We performed network simplification to reduce the computational time needed for the computation of network distances. We empirically proved that networks clustering can characterize groups of patients in multiple conditions. Conclusions We provide a computational methodology to explore and characterize the metabolic landscape of tumors, thus providing a general methodology to integrate analytic metabolic models with gene expression data. This method represents a first attempt in clustering large scale metabolic networks. Moreover, this approach gives the possibility to get valuable information on what are the effects of different conditions on the overall metabolism.

scholarly journals Can Bibliographic Pointers for Known Biological Data Be Found Automatically? Protein Interactions as a Case Study

2001 ◽  
Vol 2 (4) ◽  
pp. 196-206 ◽  
Author(s):  
Christian Blaschke ◽  
Alfonso Valencia
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Biological Data ◽  
Positive Finding ◽  
Data Set ◽  
Large Scale Assessment ◽  
Low Coverage ◽  
Standard Protein

The Dictionary of Interacting Proteins(DIP) (Xenarioset al., 2000) is a large repository of protein interactions: its March 2000 release included 2379 protein pairs whose interactions have been detected by experimental methods. Even if many of these correspond to poorly characterized proteins, the result of massive yeast two-hybrid screenings, as many as 851 correspond to interactions detected using direct biochemical methods.We used information retrieval technology to search automatically for sentences in Medline abstracts that support these 851 DIP interactions. Surprisingly, we found correspondence between DIP protein pairs and Medline sentences describing their interactions in only 30% of the cases. This low coverage has interesting consequences regarding the quality of annotations (references) introduced in the database and the limitations of the application of information extraction (IE) technology to Molecular Biology. It is clear that the limitation of analyzing abstracts rather than full papers and the lack of standard protein names are difficulties of considerably more importance than the limitations of the IE methodology employed. A positive finding is the capacity of the IE system to identify new relations between proteins, even in a set of proteins previously characterized by human experts. These identifications are made with a considerable degree of precision.This is, to our knowledge, the first large scale assessment of IE capacity to detect previously known interactions: we thus propose the use of the DIP data set as a biological reference to benchmark IE systems.

scholarly journals De novo prediction of RNA-protein interactions with Graph Neural Networks

2021 ◽  
Author(s):  
Viplove Arora ◽  
Guido Sanguinetti
Keyword(s):  
Neural Networks ◽  
Protein Interactions ◽  
Large Scale ◽  
Rna Binding ◽  
De Novo ◽  
Rna Binding Proteins ◽  
Large Data ◽  
Data Sets ◽  
Graph Neural Networks ◽  
Post Transcriptional Regulation

RNA-binding proteins (RBPs) are key co- and post-transcriptional regulators of gene expression, playing a crucial role in many biological processes. Experimental methods like CLIP-seq have enabled the identification of transcriptome-wide RNA-protein interactions for select proteins, however the time and resource intensive nature of these technologies call for the development of computational methods to complement their predictions. Here we leverage recent, large-scale CLIP-seq experiments to construct a de novo predictor of RNA-protein interactions based on graph neural networks (GNN). We show that the GNN method allows not only to predict missing links in a RNA-protein network, but to predict the entire complement of targets of previously unassayed proteins, and even to reconstruct the entire network of RNA-protein interactions in different conditions based on minimal information. Our results demonstrate the potential of machine learning methods to extract useful information on post-transcriptional regulation from large data sets.

scholarly journals Analysis of Routing Protocols for Large Scale Multihop Multirate MANETs

2013 ◽  
Vol 10 (3) ◽  
pp. 1440-1444
Author(s):  
Vincy Goyal ◽  
Sunil Kumar Jangir ◽  
Naveen Hemrajani
Keyword(s):  
Routing Protocol ◽  
Routing Protocols ◽  
Large Scale ◽  
Data Communication ◽  
Data Sets ◽  
Large Set ◽  
Data Traffic ◽  
Network Mobility ◽  
Manet Routing

In this paper, we perform rigorous analysis of MANET routing protocols selected from different categories over various scenarios using a large set of performance evaluation metrics. The traffic that we model on source-destination pairs is the video streams that consist of varying sized data frames and the inter-packet time is very low. In this way, we can check the MANET routing protocols over varying data sets and can provide the analysis that among the existing MANET routing protocols which routing protocol is best suited for data transmission over MANETs. To analyze the behavior of various routing protocols during the data communication in MANETs, we generate simulation results over various MANET scenarios consists of varying number of nodes and source destination pairs. The simulation process is done by using the open source simulator NS-3. We generate and analyze the scenarios where the effects of data communication is evaluated and analyzed over the increase in network mobility and network data traffic. The work is helpful for the students working on the various issues on MANETs as attacks, Quality-of-Service etc to identify which protocol they should use for their work as a base routing protocol.  

High Cycle Fatigue Cracks at Radial Fan Impellers Caused by Aeroelastic Self-Excited Impeller Vibrations: Part 2 — Mechanism and Mathematical Model

1999 ◽  
Author(s):  
Alexander Ni
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
High Cycle Fatigue ◽  
Dynamic Behaviour ◽  
Fatigue Cracks ◽  
Model Fit ◽  
Acoustic Properties ◽  
Cycle Fatigue ◽  
Pressure Oscillations ◽  
The Mathematical Model

Abstract The radial impeller of the main fan of hydrogen cooled generator has been noted to sustain fatigue failure. Experimental data pointed out to the impeller selfexcitation as the cause of the failure. According to the mechanism and the mathematical model suggested in the paper the impeller selfexcitation is due to the feedback between the natural impeller vibrations and the acoustic field in the side room adjacent to the impeller. The impeller vibrations induce the pressure oscillations in the side room that in turn influence the impeller. Under special conditions of the fan dynamic behaviour and the acoustic properties of the fan side room this feedback leads to the selfexcitation. The suggested mechanism and the model fit all the experimental data. Their validity has been also later confirmed by the maintaince experience of other similar machines.

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