Dataset Information

Backbone: An R package for extracting the backbone of bipartite projections.

ABSTRACT: Bipartite projections are used in a wide range of network contexts including politics (bill co-sponsorship), genetics (gene co-expression), economics (executive board co-membership), and innovation (patent co-authorship). However, because bipartite projections are always weighted graphs, which are inherently challenging to analyze and visualize, it is often useful to examine the 'backbone,' an unweighted subgraph containing only the most significant edges. In this paper, we introduce the R package backbone for extracting the backbone of weighted bipartite projections, and use bill sponsorship data from the 114th session of the United States Senate to demonstrate its functionality.

SUBMITTER: Domagalski R

PROVIDER: S-EPMC7787471 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Backbone: An R package for extracting the backbone of bipartite projections.

Domagalski Rachel R Neal Zachary P ZP Sagan Bruce B

PloS one 20210106 1

Bipartite projections are used in a wide range of network contexts including politics (bill co-sponsorship), genetics (gene co-expression), economics (executive board co-membership), and innovation (patent co-authorship). However, because bipartite projections are always weighted graphs, which are inherently challenging to analyze and visualize, it is often useful to examine the 'backbone,' an unweighted subgraph containing only the most significant edges. In this paper, we introduce the R packa ...[more]

PMID: 33406145

Similar Datasets

Project description:Nestedness is a statistical measure used to interpret bipartite interaction data in several ecological and evolutionary contexts, e.g. biogeography (species-site relationships) and species interactions (plant-pollinator and host-parasite networks). Multiple methods have been used to evaluate nestedness, which differ in how the metrics for nestedness are determined. Furthermore, several different null models have been used to calculate statistical significance of nestedness scores. The profusion of measures and null models, many of which give conflicting results, is problematic for comparison of nestedness across different studies. We developed the FALCON software package to allow easy and efficient comparison of nestedness scores and statistical significances for a given input network, using a selection of the more popular measures and null models from the current literature. FALCON currently includes six measures and five null models for nestedness in binary networks, and two measures and four null models for nestedness in weighted networks. The FALCON software is designed to be efficient and easy to use. FALCON code is offered in three languages (R, MATLAB, Octave) and is designed to be modular and extensible, enabling users to easily expand its functionality by adding further measures and null models. FALCON provides a robust methodology for comparing the strength and significance of nestedness in a given bipartite network using multiple measures and null models. It includes an "adaptive ensemble" method to reduce undersampling of the null distribution when calculating statistical significance. It can work with binary or weighted input networks. FALCON is a response to the proliferation of different nestedness measures and associated null models in the literature. It allows easy and efficient calculation of nestedness scores and statistical significances using different methods, enabling comparison of results from different studies and thereby supporting theoretical study of the causes and implications of nestedness in different biological contexts.

Project description:Deep neural network models (DNNs) are essential to modern AI and provide powerful models of information processing in biological neural networks. Researchers in both neuroscience and engineering are pursuing a better understanding of the internal representations and operations that undergird the successes and failures of DNNs. Neuroscientists additionally evaluate DNNs as models of brain computation by comparing their internal representations to those found in brains. It is therefore essential to have a method to easily and exhaustively extract and characterize the results of the internal operations of any DNN. Many models are implemented in PyTorch, the leading framework for building DNN models. Here we introduce TorchLens , a new open-source Python package for extracting and characterizing hidden-layer activations in PyTorch models. Uniquely among existing approaches to this problem, TorchLens has the following features: (1) it exhaustively extracts the results of all intermediate operations, not just those associated with PyTorch module objects, yielding a full record of every step in the model's computational graph, (2) it provides an intuitive visualization of the model's complete computational graph along with metadata about each computational step in a model's forward pass for further analysis, (3) it contains a built-in validation procedure to algorithmically verify the accuracy of all saved hidden-layer activations, and (4) the approach it uses can be automatically applied to any PyTorch model with no modifications, including models with conditional (if-then) logic in their forward pass, recurrent models, branching models where layer outputs are fed into multiple subsequent layers in parallel, and models with internally generated tensors (e.g., injections of noise). Furthermore, using TorchLens requires minimal additional code, making it easy to incorporate into existing pipelines for model development and analysis, and useful as a pedagogical aid when teaching deep learning concepts. We hope this contribution will help researchers in AI and neuroscience understand the internal representations of DNNs.

Project description:The rhamnose-glucose polysaccharide (Rgp) of Streptococcus thermophilus represents a major cell wall component, and the gene cluster responsible for its biosynthesis (termed rgp) has recently been identified. Significant genetic diversity among these loci has previously been reported, with five distinct rgp genotypes identified (designated rgp1 through -5). In the present study, two additional genotypes were identified (designated rgp6 and rgp7) through comparative analysis of the rgp loci of 78 Streptococcus thermophilus genomes. The rgp locus of a given S. thermophilus strain encoded the biosynthetic machinery for a rhamnan-rich backbone and a variable side chain component, the latter being associated with the highly specific interactions with many bacteriophages that infect this species. The chemical structure of the Rgp from three S. thermophilus strains, representing the rgp2, -3, and -4 genotypes, was elucidated, and based on bioinformatic and biochemical analyses we propose a model for Rgp biosynthesis in dairy streptococci. Furthermore, we exploited the genetic diversity within the S. thermophilus bipartite rgp locus to develop a two-step multiplex PCR system to classify strains based on gene content associated with the biosynthesis of the variable side chain structure as well as the rhamnan backbone. IMPORTANCE Streptococcus thermophilus is present and applied in industrial and artisanal dairy fermentations for the production of various cheeses and yogurt. During these fermentations, S. thermophilus is vulnerable to phage predation, and recent studies have identified the rhamnose-glucose polymer (Rgp) as the definitive receptor for at least one problematic phage species. Detailed analysis of S. thermophilus rgp loci has revealed an unprecedented level of genetic diversity, particularly within the glycosyltransferase-encoding gene content of a given locus. Our study shows that this genetic diversity reflects the biochemical structure(s) of S. thermophilus Rgp. As such, we harnessed the genetic diversity of S. thermophilus rgp loci to develop a two-step multiplex PCR method for the classification of strain collections and, ultimately, the formation of phage-robust rational starter sets.

Dataset Information

Backbone: An R package for extracting the backbone of bipartite projections.

Publications

Backbone: An R package for extracting the backbone of bipartite projections.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets