Article
Computer Science, Hardware & Architecture
Sanjukta Bhowmick, Patrick Bell, Michela Taufer
Summary: The increasing nondeterminism in High-Performance Computing (HPC) applications, resulting from hardware concurrency and the overlap of computation and communication in asynchronous executions, poses challenges in terms of reproducibility, debugging, testing, and fault-tolerance. This survey article explores the connection between graph comparison algorithms and understanding nondeterminism in HPC, presents existing methods' limitations, and highlights open challenges for leveraging graph comparisons in studying HPC nondeterminism.
INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS
(2023)
Article
Mathematics
Sile Wang, Xiaorui Su, Bowei Zhao, Pengwei Hu, Tao Bai, Lun Hu
Summary: DDI prediction is crucial for drug development to ensure patient safety. The proposed algorithm DDIGIN incorporates a graph isomorphism network (GIN) to improve drug representation and achieves better performance in DDI prediction.
Article
Biochemical Research Methods
Hyun-Myung Woo, Byung-Jun Yoon
Summary: MONACO is a novel and versatile network alignment algorithm that achieves highly accurate pairwise and multiple network alignments through iterative optimal matching of 'local' neighborhoods around focal nodes. Extensive performance assessments on real and synthetic networks, where the ground truth is known, show that MONACO consistently outperforms all other state-of-the-art network alignment algorithms in terms of accuracy, coherence, and topological quality of the aligned network regions. Despite the significantly enhanced alignment accuracy, MONACO remains computationally efficient and scales well with increasing size and number of networks.
Article
Engineering, Multidisciplinary
Jacob D. Moorman, Thomas K. Tu, Qinyi Chen, Xie He, Andrea L. Bertozzi
Summary: This research focuses on designing algorithms for solving the subgraph matching problem in computational science, aiming to find copies of a given template graph in a larger world graph. The study presents a suite of filtering methods for multiplex networks and aims to understand the entire solution space.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Theory & Methods
Sarra Bouhenni, Said Yahiaoui, Nadia Nouali-Taboudjemat, Hamamache Kheddouci
Summary: This article discusses the challenges of graph pattern matching in big data, introduces relaxed GPM models, and emphasizes the necessity of distributed storage and processing of data.
ACM COMPUTING SURVEYS
(2021)
Article
Biochemical Research Methods
Kerr Ding, Sheng Wang, Yunan Luo
Summary: This article introduces a new approach to biological network alignment called GraNA, which utilizes deep learning and functional correspondence between proteins across species to predict functional relatedness. GraNA performs well on multiple tasks and successfully discovers functionally replaceable human-yeast protein pairs documented in previous studies.
Article
Computer Science, Interdisciplinary Applications
Nan Li, Zhihao Yang, Yumeng Yang, Jian Wang, Hongfei Lin
Summary: This paper proposes HEM, a hyperbolic hierarchical knowledge graph embedding model, which can accurately capture latent hierarchical information and improve the accuracy of biological entity representation by encoding entities and relationships in the hyperbolic space. Experimental results demonstrate the superior performance of HEM in protein-protein interaction prediction and gene-disease association prediction tasks.
JOURNAL OF BIOMEDICAL INFORMATICS
(2023)
Article
Biochemical Research Methods
Peiqiang Liu, Chang Liu, Yanyan Mao, Junhong Guo, Fanshu Liu, Wangmin Cai, Feng Zhao
Summary: This paper proposes a method called CTF to identify essential proteins based on edge features and fusion of multiple-source information. The CTF method outperforms state-of-the-art methods in identifying essential proteins, and the fusion of other biological information improves the accuracy of identification.
BMC BIOINFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, Michael M. Bronstein
Summary: Although Graph Neural Networks (GNNs) have achieved remarkable results, recent studies have shown important shortcomings in their ability to capture the structure of the underlying graph. We propose Graph Substructure Networks (GSN), a topologically-aware message passing scheme based on substructure encoding, to address these limitations and obtain state-of-the-art results in various real-world settings.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Xuefeng Zhang, Richong Zhang, Junfan Chen, Jaein Kim, Yongyi Mao
Summary: This study proposes a model called GALA to address the limitation of existing entity alignment models in exploring the topology information of unaligned entities. By constructing global features and aggregating local information, GALA aligns entities from different knowledge graphs and achieves promising results.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Biochemical Research Methods
Khalique Newaz, Tijana Milenkovic
Summary: Gene expression data and biological network data have the potential for inferring condition-specific gene networks, but current methods fail to capture dynamic processes. This study utilizes network propagation to infer a dynamic aging-related gene subnetwork, and predicts new aging-related protein candidates by studying the evolution of network structure with age.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Ziwei Zhang, Peng Cui, Wenwu Zhu
Summary: This survey comprehensively reviews the application of deep learning methods on graph data. The existing methods are categorized into five types, and their development history, differences, and compositions are covered in a systematic manner. Potential future research directions are also discussed.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Biochemical Research Methods
Xi Yang, Wei Wang, Jing-Lun Ma, Yan-Long Qiu, Kai Lu, Dong-Sheng Cao, Cheng-Kun Wu
Summary: This paper introduces a deep biological network model BioNet with a graph encoder-decoder architecture for predicting chemical-gene interactions. BioNet utilizes graph convolution to learn latent information from complex interactions among chemicals, genes, diseases and biological pathways. Through parallel training algorithm and multiple GPUs, BioNet achieves outstanding prediction performance in CGI prediction, surpassing current state-of-the-art methods.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Computer Science, Information Systems
Judith Hermanns, Konstantinos Skitsas, Anton Tsitsulin, Marina Munkhoeva, Alexander Kyster, Simon Nielsen, Alexander M. Bronstein, Davide Mottin, Panagiotis Karras
Summary: This article introduces a method called GRASP for matching nodes of two graphs. The method establishes a correspondence between functions derived from Laplacian matrix eigenvectors to align nodes. Experimental results show that GRASP outperforms scalable state-of-the-art methods for graph alignment across noise levels and performs competitively with the best nonscalable methods.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2023)
Review
Genetics & Heredity
Vladimir N. Uversky, Alessandro Giuliani
Summary: The text discusses the multi-level organization of nature, highlighting interactions and organization from the protein level to ecological systems. It challenges the traditional causative model based on genotype-phenotype distinction, proposing alternative top-down, bottom-up, and middle-out perturbation/control trajectories. The recent complex network studies reveal non-linear and non-bottom-up processes, shedding light on the deep nature of multi-level organization in biology.
FRONTIERS IN GENETICS
(2021)
