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.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477957 | PMC |
http://dx.doi.org/10.1007/s11538-022-01081-9 | DOI Listing |
Sci Rep
January 2025
College of Computing and Information Technology, University of Bisha, Bisha, Bisha, 61922, Saudi Arabia.
Smart devices are enabled via the Internet of Things (IoT) and are connected in an uninterrupted world. These connected devices pose a challenge to cybersecurity systems due attacks in network communications. Such attacks have continued to threaten the operation of systems and end-users.
View Article and Find Full Text PDFJ Am Med Inform Assoc
November 2024
Health Data Science Research Group, National Taiwan University Hospital, Taipei 100, Taiwan.
Objectives: To pioneer the first artificial intelligence system integrating radiological and objective clinical data, simulating the clinical reasoning process, for the early prediction of high-risk influenza patients.
Materials And Methods: Our system was developed using a cohort from National Taiwan University Hospital in Taiwan, with external validation data from ASST Grande Ospedale Metropolitano Niguarda in Italy. Convolutional neural networks pretrained on ImageNet were regressively trained using a 5-point scale to develop the influenza chest X-ray (CXR) severity scoring model, FluDeep-XR.
Comput Methods Programs Biomed
January 2025
The First Clinical Medical College of Lanzhou University, 199 Donggang West Road, Lanzhou 730000, China; Department of Cardiovascular Surgery, First Hospital of Lanzhou University, 1 Donggang West Road, Lanzhou 730000, China. Electronic address:
Hypertension is a major preventable risk factor for cardiovascular disease, affecting over 1.5 billion adults worldwide. Antihypertensive peptides (AHTPs) have gained attention as a natural therapeutic option with minimal side effects.
View Article and Find Full Text PDFCureus
September 2024
Department of Occupational Therapy, Faculty of Medical Sciences, Nagoya Women's University, Nagoya, JPN.
Introduction The progression of performance learning (PL) may have complex relationships beyond mere concurrent occurrences and may influence each other. This study aimed to classify the speed of PL using a random forest based on brain network and stress state information and to identify the factors necessary for PL. In addition, this study also aimed to clarify the complex interdependent relationships between PL, psychological state, and brain function through these factors, using covariance structure analysis.
View Article and Find Full Text PDF3 Biotech
November 2024
Computer Science & Engineering Department, Thapar Institute of Engineering & Technology, Patiala, India.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!