Tree-based Network Research Articles

Path partitions of phylogenetic networks

In phylogenetics, evolution is traditionally represented in a tree-like manner. However, phylogenetic networks can be more appropriate for representing evolutionary events such as hybridization, horizontal gene transfer, and others. In particular, the class of forest-based networks was recently introduced to represent introgression, in which genes are swapped between species. A network is forest-based if it can be obtained by adding arcs to a collection of trees, so that the endpoints of the new arcs are in different trees. This contrasts with so-called tree-based networks, which are formed by adding arcs within a single tree.We are interested in the computational complexity of recognizing forest-based networks, which was recently left as an open problem by Huber et al. It has been observed that forest-based networks coincide with directed acyclic graphs that can be partitioned into induced paths, each ending at a leaf of the original graph. Several types of path partitions have been studied in the graph theory literature, but to our best knowledge this type of ‘leaf induced path partition’ has not been directly considered before. The study of forest-based networks in terms of these partitions allows us to establish closer relationships between phylogenetics and algorithmic graph theory, and to provide answers to problems in both fields.More specifically, we show that deciding whether a network is forest-based is NP-complete, even on input networks that are tree-based, binary, and have only three leaves. This shows that partitioning a directed acyclic graph into a constant number of induced paths is NP-complete, answering a recent question of Fernau et al. We then show that the problem is polynomial-time solvable on binary networks with two leaves and on the recently introduced class of orchards, which we show to be always forest-based. Finally, for undirected graphs, we introduce unrooted forest-based networks and provide hardness results for this class as well.

Bridging Between Deviation Indices for Non-Tree-Based Phylogenetic Networks.

Phylogenetic networks are a useful model that can represent reticulate evolution and complex biological data. In recent years, mathematical and computational aspects of tree-based networks have been well studied. However, not all phylogenetic networks are tree-based, so it is meaningful to consider how close a given network is to being tree-based; Francis-Steel-Semple (2018) proposed several different indices to measure the degree of deviation of a phylogenetic network from being tree-based. One is the minimum number of leaves that need to be added to convert a given network to tree-based, and another is the number of vertices that are not included in the largest subtree covering its leaf-set. Both values are zero if and only if the network is tree-based. Both deviation indices can be computed efficiently, but the relationship between the above two is unknown, as each has been studied using different approaches. In this study, we derive a tight inequality for the values of the two measures and also give a characterisation of phylogenetic networks such that they coincide. This characterisation yields a new efficient algorithm for the Maximum Covering Subtree Problem based on the maximal zig-zag trail decomposition.

Age, quality of life and mental well-being in adolescent population: a network model tree analysis

This study presents the results of a network-based analysis of health related quality of life (HRQoL) among Slovenian adolescents. The study aimed to examine the relationship between HRQoL and mental well-being among adolescents of different age and gender groups. A cross-sectional study was conducted from November 2019 to January 2020 in 16 primary and 9 secondary schools in Slovenia. The KIDSCREEN-27 scale was used to collect the data on HRQoL, and the Warwick–Edinburgh Mental Well-being Scale to collect data on mental well-being. We used network model trees to demonstrate differences in psychometric network structure measuring correlations between different concepts in adolescent HRQoL. A total of 2972 students aged 10–19 years participated in the study. The significant split in the network tree (p < 0.001) indicated differences in relations between HRQoL subscale scores and mental well-being score among adolescents younger than 12 years old. In comparison to older adolescents the correlation between mental well-being and mood scores was significantly weaker in this group of the youngest participants (p < 0.001). A network model tree analysis also uncovered an interesting pattern based on gender and age (p < 0.013) where a correlation between mood and family support became weaker for female at the age of 12 and for male at the age of 16. Data mining techniques have recently been used by healthcare researchers and professionals. Network-based analysis is an innovative alternative to classical approaches in HRQoL research. In this study we demonstrate the significant differences in the perceptions of HRQoL and mental well-being among adolescents in different age and gender groups that were discovered using tree-based network analysis.

How far is my network from being edge-based? Proximity measures for edge-basedness of unrooted phylogenetic networks

Phylogenetic networks which are, as opposed to trees, suitable to describe processes like hybridization and horizontal gene transfer, play a substantial role in evolutionary research. However, while non-treelike events need to be taken into account, they are relatively rare, which implies that biologically relevant networks are often assumed to be similar to trees in the sense that they can be obtained by taking a tree and adding some additional edges. This observation led to the concept of so-called tree-based networks, which recently gained substantial interest in the literature. Unfortunately, though, identifying such networks in the unrooted case is an NP-complete problem. Therefore, classes of networks for which tree-basedness can be guaranteed are of the utmost interest. The most prominent such class is formed by so-called edge-based networks, which have a close relationship to generalized series–parallel graphs known from graph theory. They can be identified in linear time and are in some regards biologically more plausible than general tree-based networks. While concerning the latter proximity measures for general networks have already been introduced, such measures are not yet available for edge-basedness. This means that for an arbitrary unrooted network, the “distance” to the nearest edge-based network could so far not be determined. The present manuscript fills this gap by introducing two classes of proximity measures for edge-basedness, one based on the given network itself and one based on its so-called leaf shrink graph (LS graph). Both classes contain four different proximity measures, whose similarities and differences we study subsequently.

Ranking top-k trees in tree-based phylogenetic networks.

Tree-based phylogenetic networks provide a powerful model for representing complex data or non-tree-like evolution. Such networks consist of an underlying evolutionary tree called a "support tree" (also known as a "subdivision tree") together with extra arcs added between the edges of that tree. However, a tree-based network can have exponentially many support trees, and this leads to a variety of computational problems. Recently, Hayamizu established a theory called the structure theorem for rooted binary phylogenetic networks and provided linear-time and linear-delay algorithms for different problems, such as counting, optimization, and enumeration of support trees. However, in practice, it is often more useful to search for both optimal and near-optimal solutions than to calculate only an optimal solution. In the present paper, we thus consider the following problem: Given a tree-based phylogenetic network N where each arc is weighted by its probability, compute the ranking of top-k support trees of N according to their likelihood values. We provide a linear-delay (and hence optimal) algorithm for this problem.

A gene regulatory network inference model based on pseudo-siamese network

MotivationGene regulatory networks (GRNs) arise from the intricate interactions between transcription factors (TFs) and their target genes during the growth and development of organisms. The inference of GRNs can unveil the underlying gene interactions in living systems and facilitate the investigation of the relationship between gene expression patterns and phenotypic traits. Although several machine-learning models have been proposed for inferring GRNs from single-cell RNA sequencing (scRNA-seq) data, some of these models, such as Boolean and tree-based networks, suffer from sensitivity to noise and may encounter difficulties in handling the high noise and dimensionality of actual scRNA-seq data, as well as the sparse nature of gene regulation relationships. Thus, inferring large-scale information from GRNs remains a formidable challenge.ResultsThis study proposes a multilevel, multi-structure framework called a pseudo-Siamese GRN (PSGRN) for inferring large-scale GRNs from time-series expression datasets. Based on the pseudo-Siamese network, we applied a gated recurrent unit to capture the time features of each TF and target matrix and learn the spatial features of the matrices after merging by applying the DenseNet framework. Finally, we applied a sigmoid function to evaluate interactions. We constructed two maize sub-datasets, including gene expression levels and GRNs, using existing open-source maize multi-omics data and compared them to other GRN inference methods, including GENIE3, GRNBoost2, nonlinear ordinary differential equations, CNNC, and DGRNS. Our results show that PSGRN outperforms state-of-the-art methods. This study proposed a new framework: a PSGRN that allows GRNs to be inferred from scRNA-seq data, elucidating the temporal and spatial features of TFs and their target genes. The results show the model’s robustness and generalization, laying a theoretical foundation for maize genotype-phenotype associations with implications for breeding work.

COFlood: Concurrent Opportunistic Flooding in Asynchronous Duty Cycle Networks

For energy constraint wireless IoT nodes, their radios usually operate in duty cycle mode. With low maintenance and negotiation cost, asynchronous duty cycle radio management is widely adopted. To achieve fast network flooding is challenging in asynchronous duty cycle networks. Recently, concurrent flooding, which allows a set of nodes (called concurrent senders ) to immediately broadcast the received packet without any backoff, is a promising approach to improve the flooding speed. We observe that selecting either large or small number of concurrent senders cannot achieve the optimal flooding speed in different deployments. There is a tradeoff between the degradation of concurrent broadcast efficiency and the missing of early receiving chance. In this article, we propose COFlood (Concurrent Opportunistic Flooding), a practical and efficient concurrent flooding protocol in asynchronous duty cycle networks. First, COFlood constructs a concurrent flooding tree in distributed manner. The non-leaf nodes are selected as concurrent senders and they can cover the entire network while reserving the most capacity of concurrent broadcast for later added opportunistic concurrent senders. Moreover, we find that exploiting both early wake-up nodes and long lossy links can speed up the concurrent flooding tree-based network flooding by increasing the early receiving chances. Then, COFlood develops a lightweight method to select the nodes that meet the conditions of these two opportunities as opportunistic concurrent senders. We implement COFlood in TinyOS and evaluate it on two real testbeds. In comparison with state-of-the-art concurrent flooding protocol, completion time and energy consumption can be reduced by up to 35.3% and 26.6%.

A Quantum-Inspired Ant Colony Optimization Approach for Exploring Routing Gateways in Mobile Ad Hoc Networks

Establishing internet access for mobile ad hoc networks (MANET) is a job that is both vital and complex. MANET is used to build a broad range of applications, both commercial and non-commercial, with the majority of these apps obtaining access to internet resources. Since the gateways (GWs) are the central nodes in a MANET’s ability to connect to the internet, it is common practice to deploy numerous GWs to increase the capabilities of a MANET. Current routing methods have been adapted and optimized for use with MANET through the use of both conventional routing techniques and tree-based network architectures. Exploring new or tacking-failure GWs also increases network overhead but is essential given that MANET is a dynamic and complicated network. To handle these issues, the work presented in this paper presents a modified gateway discovery approach inspired by the quantum swarm intelligence technique. The suggested approach follows the non-root tree-based GW discovery category to reduce broadcasting in the process of exploring GWs and uses quantum-inspired ant colony optimization (QACO) for constructing new paths. Due to the sequential method of execution of the algorithms, the complexity of ACO grows dramatically with the rise in the number of paths explored and the number of iterations required to obtain better performance. The exploration of a huge optimization problem’s solution space may be made much more efficient with the help of quantum parallelization and entanglement of quantum states. Compared to other broad evolutionary algorithms, QACO s have more promise for tackling large-scale issues due to their ability to prevent premature convergence with a simple implementation. The experimental results using benchmarked datasets reveal the feasibility of the suggested approach of improving the processes of exploring new GWs, testing and maintaining existing paths to GWs, exploring different paths to existing GWs, detecting any connection failure in any route, and attempting to fix that failure by discovering an alternative optimal path. Furthermore, the comparative study demonstrates that the utilized QACO is valid and outperforms the discrete binary ACO algorithm (AntHocNet Protocol) in terms of time to discover new GWs (27% improvement on average), time that the recently inserted node takes to discover all GWs (on average, 70% improvement), routing overhead (53% improvement on average), and gateway’s overhead (on average, 60% improvement).

Forest-Based Networks

In evolutionary studies, it is common to use phylogenetic trees to represent the evolutionary history of a set of species. However, in case the transfer of genes or other genetic information between the species or their ancestors has occurred in the past, a tree may not provide a complete picture of their history. In such cases, tree-based phylogenetic networks can provide a useful, more refined representation of the species’ evolution. Such a network is essentially a phylogenetic tree with some arcs added between the tree’s edges so as to represent reticulate events such as gene transfer, hybridization and recombination. Even so, this model does not permit the direct representation of evolutionary scenarios where reticulate events have taken place between different subfamilies or lineages of species. To represent such scenarios, in this paper we introduce the notion of a forest-based network, that is, a collection of leaf-disjoint phylogenetic trees on a set of species with arcs added between the edges of distinct trees within the collection. Forest-based networks include the recently introduced class of overlaid species forests which can be used to model introgression. As we shall see, even though the definition of forest-based networks is closely related to that of tree-based networks, they lead to new mathematical theory which complements that of tree-based networks. As well as studying the relationship of forest-based networks with other classes of phylogenetic networks, such as tree-child networks and universal tree-based networks, we present some characterizations of some special classes of forest-based networks. We expect that our results will be useful for developing new models and algorithms to understand reticulate evolution, such as introgression and gene transfer between species.

MTREE: A Customized Multicast-Enabled Tree-Based Network on Chip for AI Chips

The network on chip (NoC) used in the AI chip is required to be able to deliver data to multiple destination endpoints simultaneously. The existing solutions, such as hierarchial mesh (HMESH) and FATTREE all modify the connection between routers to achieve multicasting. Although this method can achieve delivering data to multiple destination endpoints at the same time, it significantly increases the number of routers used and, therefore, brings a huge additional area and power consumption overhead. In order to solve this problem, this work designed a customized multicast-enabled tree-based NoC, named mTREE, by modifying the microarchitecture of the router, including the routing calculation unit, credit counter, and switch allocator. When implemented on Arria10 FPGA, mTREE only brings a 1% increase in resource utilization compared with the traditional tree-topology NoC. Compared with two other multicast-enabled NoCs: 1) FATTREE and 2) HMESH, mTREE can reach a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.19\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.50\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.67\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.02\times $ </tex-math></inline-formula> reduction in resource utilization, respectively, and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.81\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8.01\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.85\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.03\times $ </tex-math></inline-formula> reduction in energy consumption, respectively.

Orchard Networks are Trees with Additional Horizontal Arcs

Phylogenetic networks are used in biology to represent evolutionary histories. The class of orchard phylogenetic networks was recently introduced for their computational benefits, without any biological justification. Here, we show that orchard networks can be interpreted as trees with additional horizontal arcs. Therefore, they are closely related to tree-based networks, where the difference is that in tree-based networks the additional arcs do not need to be horizontal. Then, we use this new characterization to show that the space of orchard networks on n leaves with k reticulations is connected under the rNNI rearrangement move with diameter O(kn+nlog (n)).

A Hybrid Intelligent System for Company Financial Risk Detection Based on Tree-Based Model and Deep Neural Network

At present, the process of security issuance in China has changed from examination and approval system to approval system. With the increasing stock that enters the capital market through the IPO, establishing an effective financial risk monitoring and control system of listed companies has great practical significance and application value. In the perspective of big data, this paper constructs a new practical financial early warning integration model. A total of 32,283 financial statements of 3,025 listed companies are collected from 2000 to 2017. First, this paper collected and built a financial crisis prediction database: a total of 32,283 financial statements of 3,025 listed companies occurred from year 2000 to 2017 and corresponding financial indicators including 6 primary indicators and 25 secondary indicators are collected. Next, the financial crisis model based on financial indicators is proposed and trained by the classic machine learning method such as random forest and gradient boosting decision tree. And then, through natural language processing and deep learning technology, this paper also builds a financial crisis prediction model based on financial statement text. Finally, an ensemble model is proposed and its performances are evaluated; the results indicate that the proposed ensemble model performed the best and significantly outperforms other benchmark methods for financial crisis prediction and identification, indicating that it can be employed as an intelligent identification system to enhance identification accuracy for early warning and identification of financial crisis of listed companies in China stock marketing.

Automatic Classification of Equivalent Mutants in Mutation Testing of Android Applications

Software and symmetric testing methodologies are primarily used in detecting software defects, but these testing methodologies need to be optimized to mitigate the wasting of resources. As mobile applications are becoming more prevalent in recent times, the need to have mobile applications that satisfy software quality through testing cannot be overemphasized. Testing suites and software quality assurance techniques have also become prevalent, which underscores the need to evaluate the efficacy of these tools in the testing of the applications. Mutation testing is one such technique, which is the process of injecting small changes into the software under test (SUT), thereby creating mutants. These mutants are then tested using mutation testing techniques alongside the SUT to determine the effectiveness of test suites through mutation scoring. Although mutation testing is effective, the cost of implementing it, due to the problem of equivalent mutants, is very high. Many research works gave varying solutions to this problem, but none used a standardized dataset. In this research work, we employed a standard mutant dataset tool called MutantBench to generate our data. Subsequently, an Abstract Syntax Tree (AST) was used in conjunction with a tree-based convolutional neural network (TBCNN) as our deep learning model to automate the classification of the equivalent mutants to reduce the cost of mutation testing in software testing of android applications. The result shows that the proposed model produces a good accuracy rate of 94%, as well as other performance metrics such as recall (96%), precision (89%), F1-score (92%), and Matthew’s correlation coefficients (88%) with fewer False Negatives and False Positives during testing, which is significant as it implies that there is a decrease in the risk of misclassification.

Non-binary universal tree-based networks

A tree-based network $N$ on $X$ is called universal if every phylogenetic tree on $X$ is a base tree for $N$. Recently, binary universal tree-based networks have attracted great attention in the literature and their existence has been analyzed in various studies. In this note, we extend the analysis to non-binary networks and show that there exist both a rooted and an unrooted non-binary universal tree-based network with $n$ leaves for all positive integers $n$. Keywords: phylogenetic tree, phylogenetic network, tree-based network, universal tree-based network

Design and Analysis of Cooperative Broadcast Scheme Based on Reliability in Mesh Network

In order to ensure reliable data transmission of sender, the tree-based network is extended to Mesh network by collaborative technology, and the SW-HBH-CARQ broadcast scheme is introduced for the first time. Then, in order to solve the problems of broadcast storm and energy saving, SW-HBH-oiCARQ, SW-HBH-ieCARQ, and SW-HBH-oieCARQ are further proposed. Then, the four broadcasting schemes are analyzed quantitatively, and the performance indicators such as system energy consumption and time delay are obtained. Finally, the relationships between the performance indexes and the parameters are discussed by numerical simulation. At the same time, the effectiveness of the proposed schemes in energy saving is verified by comparing with the corresponding noncollaborative broadcasting scheme. However, the latency of proposes scheme is greater than uncooperative scheme, which is the cost of saving energy and resolving broadcast storms.

Load Balancing Selection Method and Simulation in Network Communication Based on AHP-DS Heterogeneous Network Selection Algorithm

This article proposes an Analytic Hierarchy Process Dempster-Shafer (AHP-DS) and similarity-based network selection algorithm for the scenario of dynamic changes in user requirements and network environment; combines machine learning with network selection and proposes a decision tree-based network selection algorithm; combines multiattribute decision-making and genetic algorithm to propose a weighted Gray Relation Analysis (GRA) and genetic algorithm-based network access decision algorithm. Firstly, the training data is obtained from the collaborative algorithm, and it is used as the training set, and the network attributes are used as the attribute set, and the continuous attributes are discretized by dichotomization, and the attribute that can make the greatest information gain is selected as the division feature, and a decision tree with strong generalization ability is finally obtained, which is used as the decision basis for network access selection. The simulation results show that the algorithm proposed in this thesis can effectively improve user service quality under three services, and the algorithm is simple and effective with low complexity. It first uses AHP-DS hierarchical analysis to establish a recursive hierarchy for the network selection problem and obtains the subjective weights of network attributes through the judgment matrix. Then, it uses a genetic algorithm to adjust the subjective weight, defines the fitness function in the genetic algorithm-based on gray correlation analysis, adjusts the weights of the selection operator, crossover operator, and variation operator in the genetic algorithm, and gets the network with the largest fitness as the target network, which can effectively improve the user service quality.

A unifying characterization of tree-based networks and orchard networks using cherry covers

Phylogenetic networks are used to represent evolutionary relationships between species in biology. Such networks are often categorized into classes by their topological features, which stem from both biological and computational motivations. We study two network classes in this paper: tree-based networks and orchard networks. Tree-based networks are those that can be obtained by inserting edges between the edges of an underlying tree. Orchard networks are a recently introduced generalization of the class of tree-child networks. Structural characterizations have already been discovered for tree-based networks; this is not the case for orchard networks. In this paper, we introduce cherry covers—a unifying characterization of both network classes—in which we decompose the edges of the networks into so-called cherry shapes and reticulated cherry shapes. We show that cherry covers can be used to characterize the class of tree-based networks as well as the class of orchard networks. Moreover, we also generalize these results to non-binary networks.

Maximizing Common Data Rate of collection Tree in Energy-Harvesting Wireless Sensor Networks

Energy-harvesting wireless sensor network (EH-WSN) has the capability of capturing energy from environment and providing perpetual data services. In many applications of EH-WSN such as environmental protection, building monitoring, industrial internet of things, nodes are required to report sensed data to the sink with high common data rate to improve monitoring accuracy. In order to improve the common data rate of collection tree-based network, this paper studies collection tree construction for maximizing common data rate. We propose a collection tree construction scheme named hybridMCDRT which consists of a heuristic construction algorithm, an iterative optimization algorithm and “detour problem” elimination algorithm. The simulation results show that the proposed scheme can obtain higher common data rate than the existing methods.

Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks

The study of cellular complexity in the nervous system based on anatomy has shown more practical and objective advantages in morphology than other perspectives on molecular, physiological, and evolutionary aspects. However, morphology-based neuron type classification in the whole rat brain is challenging, given the significant number of neuron types, limited reconstructed neuron samples, and diverse data formats. Here, we report that different types of deep neural network modules may well process different kinds of features and that the integration of these submodules will show power on the representation and classification of neuron types. For SWC-format data, which are compressed but unstructured, we construct a tree-based recurrent neural network (Tree-RNN) module. For 2D or 3D slice-format data, which are structured but with large volumes of pixels, we construct a convolutional neural network (CNN) module. We also generate a virtually simulated dataset with two classes, reconstruct a CASIA rat-neuron dataset with 2.6 million neurons without labels, and select the NeuroMorpho-rat dataset with 35,000 neurons containing hierarchical labels. In the twelve-class classification task, the proposed model achieves state-of-the-art performance compared with other models, e.g., the CNN, RNN, and support vector machine based on hand-designed features.

Unrooted non-binary tree-based phylogenetic networks

Phylogenetic networks are a generalization of phylogenetic trees allowing for the representation of non-treelike evolutionary events such as hybridization. Typically, such networks have been analyzed based on their ‘level’, i.e. based on the complexity of their 2-edge-connected components. However, recently the question of how ‘treelike’ a phylogenetic network is has become the center of attention in various studies. This led to the introduction of tree-based networks, i.e. networks that can be constructed from a phylogenetic tree, called the base tree, by adding additional edges. While the concept of tree-basedness was originally introduced for rooted phylogenetic networks, it has recently also been considered for unrooted networks. In the present study, we compare and contrast findings obtained for unrooted binary tree-based networks to unrooted non-binary networks. In particular, while it is known that up to level 4 all unrooted binary networks are tree-based, we show that in the case of non-binary networks, this result only holds up to level 3.