Should Networks Supplant Tree Building?
Ontology highlight
ABSTRACT:
SUBMITTER: DeSalle R
PROVIDER: S-EPMC7466111 | biostudies-literature | 2020 Aug
REPOSITORIES: biostudies-literature
Publications
Recent studies suggested that network methods should supplant tree building as the basis of genealogical analysis. This proposition is based upon two arguments. First is the observation that bacterial and archaeal lineages experience processes oppositional to bifurcation and hence the representation of the evolutionary process in a tree like structure is illogical. Second is the argument tree building approaches are circular-you ask for a tree and you get one, which pins a verificationist label ...[more]
Similar Datasets
Project description:The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches.
| S-EPMC6778203 | biostudies-literature
Project description:Belief networks represent a powerful approach to problems involving probabilistic inference, but much of the work in this area is software based utilizing standard deterministic hardware based on the transistor which provides the gain and directionality needed to interconnect billions of them into useful networks. This paper proposes a transistor like device that could provide an analogous building block for probabilistic networks. We present two proof-of-concept examples of belief networks, one reciprocal and one non-reciprocal, implemented using the proposed device which is simulated using experimentally benchmarked models.
| S-EPMC4956763 | biostudies-literature
Project description:BackgroundScientific literature is a source of the most reliable and comprehensive knowledge about molecular interaction networks. Formalization of this knowledge is necessary for computational analysis and is achieved by automatic fact extraction using various text-mining algorithms. Most of these techniques suffer from high false positive rates and redundancy of the extracted information. The extracted facts form a large network with no pathways defined.ResultsWe describe the methodology for automatic curation of Biological Association Networks (BANs) derived by a natural language processing technology called Medscan. The curated data is used for automatic pathway reconstruction. The algorithm for the reconstruction of signaling pathways is also described and validated by comparison with manually curated pathways and tissue-specific gene expression profiles.ConclusionBiological Association Networks extracted by MedScan technology contain sufficient information for constructing thousands of mammalian signaling pathways for multiple tissues. The automatically curated MedScan data is adequate for automatic generation of good quality signaling networks. The automatically generated Regulome pathways and manually curated pathways used for their validation are available free in the ResNetCore database from Ariadne Genomics, Inc. 1. The pathways can be viewed and analyzed through the use of a free demo version of PathwayStudio software. The Medscan technology is also available for evaluation using the free demo version of PathwayStudio software.
| S-EPMC1435941 | biostudies-literature
Project description:We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis-Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.
| S-EPMC5036115 | biostudies-literature
Project description:The understanding of the nature and structure of energy use in large buildings is vital for defining novel energy and climate change strategies. The advances on metering technology and low-cost devices make it possible to form a submetering network, which measures the main supply and other intermediate points providing information of the behavior of different areas. However, an analysis by means of classical techniques can lead to wrong conclusions if the load is not balanced. This paper proposes the use of a deep convolutional autoencoder to reconstruct the whole consumption measured by the submeters using the learnt features in order to analyze the behavior of different building areas. The display of weights and information of the latent space provided by the autoencoder allows us to obtain precise details of the influence of each area in the whole building consumption and its dependence on external factors such as temperature. A submetering network is deployed in the León University Hospital building in order to test the proposed methodology. The results show different correlations between environmental variables and building areas and indicate that areas can be grouped depending on their function in the building performance. Furthermore, this approach is able to provide discernible results in the presence of large differences with respect to the consumption ranges of the different areas, unlike conventional approaches where the influence of smaller areas is usually hidden.
| S-EPMC7374320 | biostudies-literature
Project description:A major ambition of systems science is to uncover the building blocks of any biological network to decipher how cellular function emerges from their interactions. Here, we introduce a graph representation of the information flow in these networks as a set of input trees, one for each node, which contains all pathways along which information can be transmitted in the network. In this representation, we find remarkable symmetries in the input trees that deconstruct the network into functional building blocks called fibers. Nodes in a fiber have isomorphic input trees and thus process equivalent dynamics and synchronize their activity. Each fiber can then be collapsed into a single representative base node through an information-preserving transformation called "symmetry fibration," introduced by Grothendieck in the context of algebraic geometry. We exemplify the symmetry fibrations in gene regulatory networks and then show that they universally apply across species and domains from biology to social and infrastructure networks. The building blocks are classified into topological classes of input trees characterized by integer branching ratios and fractal golden ratios of Fibonacci sequences representing cycles of information. Thus, symmetry fibrations describe how complex networks are built from the bottom up to process information through the synchronization of their constitutive building blocks.
| S-EPMC7165483 | biostudies-literature
Project description:Controllable platforms to engineer robust cytoskeletal scaffolds have the potential to create novel on-chip nanotechnologies. Inspired by axons, we combined the branching microtubule (MT) nucleation pathway with microfabrication to develop "cytoskeletal circuits." This active matter platform allows control over the adaptive self-organization of uniformly polarized MT arrays via geometric features of microstructures designed within a microfluidic confinement. We build and characterize basic elements, including turns and divisions, as well as complex regulatory elements, such as biased division and MT diodes, to construct various MT architectures on a chip. Our platform could be used in diverse applications, ranging from efficient on-chip molecular transport to mechanical nano-actuators. Further, cytoskeletal circuits can serve as a tool to study how the physical environment contributes to MT architecture in living cells.
| S-EPMC10823238 | biostudies-literature
Project description:Efforts in phylogenomics have greatly improved our understanding of the backbone tree of life. However, due to the systematic error in sequence data, a sequence-based phylogenomic approach leads to well-resolved but statistically significant incongruence. Thus, independent test of current phylogenetic knowledge is required. Here, we have devised a distance-based strategy to reconstruct a highly resolved backbone tree of life, on the basis of the genome context networks of 195 fully sequenced representative species. Along with strongly supporting the monophylies of three superkingdoms and most taxonomic sub-divisions, the derived tree also suggests some intriguing results, such as high G+C gram positive origin of Bacteria, classification of Symbiobacterium thermophilum and Alcanivorax borkumensis in Firmicutes. Furthermore, simulation analyses indicate that addition of more gene relationships with high accuracy can greatly improve the resolution of the phylogenetic tree. Our results demonstrate the feasibility of the reconstruction of highly resolved phylogenetic tree with extensible gene networks across all three domains of life. This strategy also implies that the relationships between the genes (gene network) can define what kind of species it is.
| S-EPMC2566592 | biostudies-literature
Project description:A genomic catalogue of protein-protein interactions is a rich source of information, particularly for exploring the relationships between proteins. Numerous systems-wide and small-scale experiments have been conducted to identify interactions; however, our knowledge of all interactions for any one species is incomplete, and alternative means to expand these network maps is needed. We therefore took a comparative biology approach to predict protein-protein interactions across five species (human, mouse, fly, worm, and yeast) and developed InterologFinder for research biologists to easily navigate this data. We also developed a confidence score for interactions based on available experimental evidence and conservation across species.The connectivity of the resultant networks was determined to have scale-free distribution, small-world properties, and increased local modularity, indicating that the added interactions do not disrupt our current understanding of protein network structures. We show examples of how these improved interactomes can be used to analyze a genome-scale dataset (RNAi screen) and to assign new function to proteins. Predicted interactions within this dataset were tested by co-immunoprecipitation, resulting in a high rate of validation, suggesting the high quality of networks produced.Protein-protein interactions were predicted in five species, based on orthology. An InteroScore, a score accounting for homology, number of orthologues with evidence of interactions, and number of unique observations of interactions, is given to each known and predicted interaction. Our website http://www.interologfinder.org provides research biologists intuitive access to this data.
| S-EPMC2859380 | biostudies-literature
Project description:Despite its high ecological importance, the commensal interactions at community level are poorly studied. In tropical dry forests (TDF) there is a great diversity of species adapted to the high seasonality that characterizes them; however, little is known regarding how the spatial and temporal availability of resources generates changes in the pattern of commensal interactions. We experimentally studied changes in the diversity, composition, and pattern of interactions in spatio-temporal associations between the saproxylophagous beetles and their host trees in a TDF in Morelos, Mexico. A total of 65 host tree species were selected, from which 16 wood sections were obtained per species. These sections were exposed in the field to allow oviposition by the cerambycids under four different (spatio-temporal) treatments. We analyzed the network structure and generated indices at species level (i.e., specialization, species strength, and effective partners) and those related to physical characteristics of the wood (hardness and degradation rate) and the cerambycids (body size). In total, 1,323 individuals of 57 species of cerambycids emerged. Our results showed that, independently of the space and time, the network presented a nested and modular structure, with a high specialization degree and a high turnover of cerambycid species and their interactions. In general, we found that the cerambycids are mostly associated with softwood species with a lower decomposition rate of wood, as well as with the most abundant host species. The commensalistic interactions between the cerambycids and their host trees are highly specialized but are not spatio-temporally static. The high turnover in the interactions is caused by the emergence patterns of cerambycids, which seem to restrict their use to certain species. The knowledge of the spatio-temporal variation in Cerambycidae-host tree interactions allows us to predict how environmental and structural changes in the habitat can modify the species ensemble, and therefore its interactions.
| S-EPMC7010308 | biostudies-literature