Biological Networks
A probabilistic model for the evaluation of module extraction algorithms in complex biological networks
... The detection of modules, highly interconnected substructures that perform specific tasks, in complex biological networks is a considerable challenge that is of importance to many areas of biological ...
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On the Complexity of Average Path length for Biological Networks and Patterns
... In biological networks, different proteins harbor different path lengths. Many PPI structures can have the same topology and yet differ in characteristics in terms of conformational features (path length). ...
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Functional Genomics Assistant (FUGA): a toolbox for the analysis of complex biological networks
... Biological networks can be inferred with FUGA from computationally or experimentally derived datasets (e.g. BLAST similarity matrices, microarray data) using any form of similarity measure (e.g. Pearson ...
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Comparison of MLP NN Approach with PCA and ICA for Extraction of Hidden Regulatory Signals in Biological Networks
... Different linear methods such as Principal Component Analysis (PCA), or Independent Component Analysis (ICA) have been previously employed to extract the regulatory components in biological systems [1- 5], but in ...
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ATria: a novel centrality algorithm applied to biological networks
... scale-free networks and synthetic networks with both positive and negative edge weights, both of which are particularly relevant in biological networks, and finally real biological ...
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Complexity Theory and Algorithms for Graph Problems Driven by Comparative Analysis of Large-Scale Biological Networks.
... of biological networks—this research thesis answers open questions on the parameterized complexity of the problem and proposes ef- ficient and biologically relevant algorithms to solve ...interaction ...
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BicNET: Flexible module discovery in large-scale biological networks using biclustering
... Despite the relevance of biclustering to model local interactions [14, 15], the focus on dense regions comes with key drawbacks. First, such regions are associated with either trivial or well-known (putative) modules. ...
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Using graph theory to analyze biological networks
... in biological networks can be found in ...regulation networks [95] or to test hub essentiality ...in biological networks and not only are Cen- tiBiN [96], Visone [97], Pajek [98], ...
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Copula Gaussian graphical modelling of biological networks and Bayesian inference of model parameters
... of biological networks, ...construct biological networks; based on the results, it has been observed that the accuracy of the model can increase in comparison to the MARS model ...dierent ...
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Graph-based Regularization in Machine Learning: Discovering Driver Modules in Biological Networks
... from biological networks are easier to ...no biological mechanism to follow up on, but network-guided expression analysis select significant changed gene subnetworks or pathways to be explored ...
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COSNet: An R package for label prediction in unbalanced biological networks
... the biological entities to be stud- ied, ...in biological networks is ...Keywords: Biological Network, Label Imbalance, Node Label Prediction, R package, Protein Function ...
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Microarray tools and analysis methods to better characterize biological networks
... regulatory networks we need to use a high-throughput, multi-locus experimental technology for studying systems (single-locus technologies are not feasible), but and missing and misleading data has a signicant ...
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Design, Analysis, And Computational Methods For Engineering Synthetic Biological Networks
... to combine orthogonal basis polynomials in the space of projection so that they ex- pressed the design features of uni-/multi-modality of distributions. This formulation would create overly elaborate problems that lose ...
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Hindmarsh–Rose Neuron Model Using Two-Coupled Biological Networks Without Digital Multipliers
... Available online: https://edupediapublications.org/journals/index.php/IJR/ P a g e | 1681 realization, its development time is considerably lower and is robust against power supply fluctuations and thermal noise. The ...
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Automated gene function prediction through gene multifunctionality in biological networks
... large-scale networks of genetic and physical interactions, where nodes are genes/gene products and connections among nodes the gene pairwise relationships, has focused the investigation also on the design of ...
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Improvement of cancer subtype prediction by incorporating transcriptome expression data and heterogeneous biological networks
... fies biological subtype patterns in multi-sources data by using non-negative matrix factorization ...similarity networks between samples, and the model is complex and hard to be inter- preted in practice ...
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Medusa: A tool for exploring and clustering biological networks
... In this section we present major changes/updates to Medusa, providing significant additional functionality. This version of Medusa comes with a friendlier interface and is offered both as an applet and a standalone ...
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Mango: combining and analyzing heterogeneous biological networks
... Four large E. coli network data sets were collected. The corr 4 M link network was com- puted using the WGCNA (weighted gene coexpression network analysis) package in R [13] on microarray data measuring the expression of ...
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Identification of functional connections in biological neural networks using dynamical Bayesian networks
... of biological networks is crucial to understanding the system-level regulatory mechanism of network ...Neural Networks (PNN), which are typical in synthetic biological ...
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Effects of Cellular Homeostatic Intrinsic Plasticity on Dynamical and Computational Properties of Biological Recurrent Neural Networks.
... learning rates, or the scaling and spatial patterning of inputs. In particular, the extreme robustness of HIP in bringing network dynamics at the edge of chaos in presence of SP is stunning (Figure 4C, 5A and 5B). ...
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