Article
Computer Science, Information Systems
Xiaofeng Lu, Cheng Jiang
Summary: With the arrival of the 5G era, the Internet of Things (IoT) has entered a new stage, and the amount of IoT data is growing rapidly. The traditional blockchain cannot handle massive amounts of data, which presents scalability challenges for blockchain technology. In this paper, we propose a high-throughput distributed ledger based on Directed Acyclic Graph (DAG) named TEEDAG, which demonstrates significantly higher throughput and improved security and efficiency compared to existing solutions.
Article
Biochemical Research Methods
Mikko Rautiainen, Tobias Marschall
Summary: With the emergence of HiFi reads, De Bruijn graphs can now be efficiently constructed with a combination of long read length and low error rate. The MBG tool outperforms existing tools in building sparse De Bruijn graphs, and can quickly construct whole human genome and E.coli genome with high accuracy.
Article
Computer Science, Artificial Intelligence
Zhuangyan Fang, Shengyu Zhu, Jiji Zhang, Yue Liu, Zhitang Chen, Yangbo He
Summary: Learning causal structures represented by directed acyclic graphs (DAGs) in high-dimensional settings remains challenging, especially for non-sparse graphs. In this article, we propose using a low-rank assumption for the adjacency matrix of a DAG causal model to address this problem. By adapting existing low-rank techniques, we establish useful results connecting interpretable graphical conditions to the low-rank assumption. Our experiments demonstrate the utility of these low-rank adaptations, particularly for large and dense graphs, with comparable performance even when graphs are not restricted to be low rank.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zeinab Zare Hosseini, Shekoufeh Kolahdouz Rahimi, Esmaeil Forouzan, Ahmad Baraani
Summary: Genome assembly is the computational process of merging short parts of DNA into larger sequences. The shift to distributed memory systems has become necessary due to the rapid growth of high-throughput genome sequencing technologies. The DRMI-DBG model proposed in this paper is a scalable iterative de Bruijn Graph framework that utilizes the power of Spark and Giraph to parallelize the construction and processing of de Bruijn graphs on distributed memory systems. Experimental results show that DRMI-DBG accelerates the performance of existing algorithms while maintaining or improving the quality of the assembly.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Biology
Thais Cristina Vilela Rodrigues, Arun Kumar Jaiswal, Marcela Rezende Lemes, Marcos Vinicius da Silva, Helioswilton Sales-Campos, Luiz Carlos Junior Alcantara, Sthephane Fraga de Oliveira Tosta, Rodrigo Bentes Kato, Khalid J. Alzahrani, Debmalya Barh, Vasco Ariston de Carvalho Azevedo, Sandeep Tiwari, Siomar de Castro Soares
Summary: A multi-epitope vaccine against Mycoplasma pneumoniae was designed using immunoinformatics approach, selecting epitopes based on immunogenicity and other criteria from core proteins. Docking and simulation showed potential efficacy, and in vitro cloning in expression vectors yielded positive results, suggesting the vaccine's safety and effectiveness, pending further experimental and clinical validations.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Xiaohan Liu, Xiaoguang Gao, Zidong Wang, Xinxin Ru, Qingfu Zhang
Summary: This paper proposes a generic metaheuristic method for causal discovery by directly finding causal relationships in a directed acyclic graph. Several novel heuristic factors are introduced to expand the search space and maintain acyclicity. A metaheuristic algorithm is then used to search for an optimal solution closer to real causality. The proposed method is theoretically proven and extensively validated through experiments comparing it with other state-of-the-art causal solvers on real-world and simulated structures.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jiaxuan Liang, Jun Wang, Guoxian Yu, Wei Guo, Carlotta Domeniconi, Maozu Guo
Summary: This paper proposes a method called HetDAG to learn causal relationships between nodes in heterogeneous networks. By embedding node attributes and using prior network structure to update node representations, and then using attention mechanism for DAG learning, HetDAG is able to learn DAG efficiently and performs well in experiments.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Public, Environmental & Occupational Health
Michael Webster-Clark, Alexander Breskin
Summary: The study provides two rules regarding effect measure modification, indicating whether a variable has an effect on the outcome at different treatment levels, and how to identify sufficient adjustments to generalize study results to a broader population.
AMERICAN JOURNAL OF EPIDEMIOLOGY
(2021)
Article
Computer Science, Interdisciplinary Applications
Ken Aho, Cathy Kriloff, Sarah E. Godsey, Rob Ramos, Chris Wheeler, Yaqi You, Sara Warix, DeWayne Derryberry, Sam Zipper, Rebecca L. Hale, Charles T. Bond, Kevin A. Kuehn
Summary: To address the incompatibility of conventional stream network metrics with non-perennial streams, the researchers treat non-perennial stream networks as directed acyclic graphs (DAGs). DAG metrics enable the summarization of important characteristics of non-perennial streams and tracking of these features as networks change. They introduce a new R package, streamDAG, which includes procedures and functions for handling water presence data and analyzing both unweighted and weighted stream DAGs. The package is demonstrated using two North American non-perennial streams.
ENVIRONMENTAL MODELLING & SOFTWARE
(2023)
Article
Computer Science, Artificial Intelligence
Shih-Gu Huang, Jing Xia, Liyuan Xu, Anqi Qiu
Summary: We developed a deep learning framework for predicting cognition and disease using fMRI. The framework consists of two neural networks for learning spatial and temporal information of functional time series and functional connectivity features. It also includes an attention component for generating a spatial attention map. Experimental results demonstrate that the framework is generalizable and outperforms other machine learning techniques in cognition and age prediction.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Mathematics, Applied
Yuichi Asahiro, Tetsuya Furukawa, Keiichi Ikegami, Eiji Miyano, Tsuyoshi Yagita
Summary: This paper discusses the minimum block transfer problem for directed acyclic graphs (DAGs), introducing the problem definition and algorithm designs, proving the hardness and inapproximability of the problem, as well as exploring different heights and values of B.
DISCRETE APPLIED MATHEMATICS
(2021)
Editorial Material
Medicine, General & Internal
Ari M. Lipsky, Sander Greenland
Summary: This article discusses the basics of causal directed acyclic graphs, which are useful tools for researchers to communicate their understanding of potential interplay among variables and are commonly used for mediation analysis.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
(2022)
Article
Biochemical Research Methods
Lisa Fiedler, Matthias Bernt, Martin Middendorf, Peter F. Stadler
Summary: This study presents a novel method for detecting gene breakpoints in the nucleotide sequences of complete mitochondrial genomes, considering high substitution rates. The method uses a parallel program design and has been extensively tested for accuracy.
BMC BIOINFORMATICS
(2023)
Article
Automation & Control Systems
Chunlin Li, Xiaotong Shen, Wei Pan
Summary: This article introduces a method for the statistical inference of directed relations with unspecified interventions. By establishing a topological order of primary variables and considering the uncertainty, accurate inference results are obtained. Numerical examples demonstrate the utility and effectiveness of the proposed method.
JOURNAL OF MACHINE LEARNING RESEARCH
(2023)
Article
Immunology
Josue Odales, Rodolfo Servin-Blanco, Fernando Martinez-Cortes, Jesus Guzman Valle, Allan Noe Dominguez-Romero, Goar Gevorkian, Karen Manoutcharian
Summary: This study utilized an innovative Variable Epitope Library vaccine platform to develop MUC1 signal peptide- and VNTR-derived immunogens, demonstrating their immunogenic and antitumor properties.
