23 papers · ranked by Valyu relevance
Yi-Jiao Zhang, Kai‐Cheng Yang, Filippo Radicchi
Network embedding techniques aim at representing structural properties of graphs in geometric space. Those representations are considered useful in downstream tasks such as link prediction and clustering. However, the number of graph embedding methods available on the market is large, and practitioners face the…
Haixia Wu, Chunyao Song, Yao Ge, Tingjian Ge
Complex networks have been used widely to model a large number of relationships. The outbreak of COVID-19 has had a huge impact on various complex networks in the real world, for example global trade networks, air transport networks, and even social networks, known as racial equality issues caused by the spread of the…
Anthony Baptista, Rubén J. Sánchez-García, Anaı̈s Baudot, Ginestra Bianconi
'Ginestra Bianconi'] 1School of Mathematical Sciences, Queen Mary University of London, London, E1 4NS, United Kingdom; 2The Alan Turing Institute, The British Library, London, NW1 2DB, United Kingdom; 3School of Mathematical Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom; 4 Institute for…
Sabrina Benbatata, Bilal Saoud, Ibraheem Shayea, Naif Alsharabi + 5 more
'Abdulraqeb Alhammadi' 'Ali Alferaidi' 'Amr Jadi' 'Yousef Ibrahim Daradkeh' 'José Alberto Benítez-Andrades'] In this paper, the graph segmentation (GSeg) method has been proposed. This solution is a novel graph neural network framework for network embedding that leverages the inherent characteristics of nodes and the…
Poorya Parvizi, Francisco Azuaje, Evropi Theodoratou, Saturnino Luz
A network embedding approach reduces the analysis complexity of large biological networks by converting them to lowdimensional vector representations (features/embeddings). These lower-dimensional vectors can then be used in machine learning prediction tasks with a wide range of applications in computational biology…
Jinlong Ma, Tian Qin, Ju Xiang
Many diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD), are caused by abnormalities or mutations of related genes. Many computational methods based on the network relationship between diseases and genes have been proposed to predict potential pathogenic genes. However, how to effectively mine the…
Vedanta Thapar, Renaud Lambiotte, George T. Cantwell
We propose the first return time distribution (FRTD) of a random walk as an interpretable and mathematically grounded node embedding. The FRTD assigns a probability mass function to each node, allowing us to define a distance between any pair of nodes using standard metrics for discrete distributions. We present…
Daniel Tello Velasco, Sam F. L. Windels, Mikhail Rotkevich, Noël Malod-Dognin + 1 more
Spatial Analysis of Functional Enrichment (SAFE) is a popular tool for biologists to investigate the functional organisation of biological networks via highly intuitive 2D functional maps. To create these maps, SAFE uses Spring embedding to project a given network into a 2D space in which nodes connected in the network…
Jianshu Zhao, Jean Pierre Both, Rob Knight
Graph/network representation learning (or graph/network embedding) is a widely used machine learning technique in industry recommending systems and has recently been applied in computational biology. Popular network representation learning algorithms include random walk and matrix factorization methods, but they do not…
Bianka Kovács, Gergely Palla
Dept. of Biological Physics, Eotv ¨ os Lor ¨ and University, H-1117 Budapest, P ´ azm´ any P. stny. 1/A, Hungary ´ MTA-ELTE Statistical and Biological Physics Research Group, H-1117 Budapest, Pazm´ any P. stny. 1/A, Hungary ´ Health Services Management Training Centre, Semmelweis University, H-1125 Budapest, Kutv ´…
Renming Liu, Hao Yuan, Kayla A Johnson, Arjun Krishnan
Human gene interaction networks, commonly known as interactomes, encode genes’ functional relationships, which are invaluable knowledge for translational medical research and the mechanistic understanding of complex human diseases. Meanwhile, the advancement of network embedding techniques has inspired recent efforts…
Paola Lecca, Michela Lecca
Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually…
Edwin Alvarez-Mamani, Reinhard Dechant, César A. Beltran-Castañón, Alfredo J. Ibáñez
'Alfredo J. Ibáñez'] Graph embedding techniques are using deep learning algorithms in data analysis to solve problems of such as node classification, link prediction, community detection, and visualization. Although typically used in the context of guessing friendships in social media, several applications for graph…
Masanao Ochi, Masanori Shiro, Jun’ichiro Mori, Ichiro Sakata + 1 more
'Diego Raphael Amancio'] Identifying promising research as early as possible is vital to determine which research deserves investment. Additionally, developing a technology for automatically predicting future research trends is necessary because of increasing digital publications and research fragmentation. In previous…
Sarmad N. Mohammed, Semra Gündüç
In this work, Transition Probability Matrix (TPM) is proposed as a new method for extracting the features of nodes in the graph. The proposed method uses random walks to capture the connectivity structure of a node's close neighborhood. The information obtained from random walks is converted to anonymous walks to…
Renming Liu, Matthew Hirn, Arjun Krishnan
Accurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased random walks. However, while many networks, including functional gene interaction…
Yue Hu, Svitlana Oleshko, Samuele Firmani, Zhaocheng Zhu + 7 more
Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine. Traditional link prediction (LP) methods, using similarity metrics like Personalized PageRank, are limited in capturing the complexity of biological networks. Recently, representation-based learning techniques have…
Zachary Humphreys, Xenophon Evangelopoulos, Stavros Gerolymatos, Edward O. Pyzer-Knapp + 1 more
Graph neural networks have recently met huge success in various inference tasks including materials property prediction amongst many others. Nevertheless, having an inherently locally-based representation capacity as they do, global representation of materials' structures can only only be achieved by expanding the…
Haotong Sun, Yinghui Jiang, Minhao Wang, Xianglu Xiao + 5 more
Rapid and accurate prediction of molecular properties is a fundamental task in drug discovery. In recent years, deep learning-based molecular property prediction methods have received much attention and recent successes have shown that learning the representations of molecular structures by applying graph neural…
Benjamin Ries, Richard J Gowers, James RB Eastwood, Irfan Alibay + 4 more
Alchemical free energy campaigns can be planned using graph theory by building up networks that contain nodes representing molecules that are connected by possible transformations as edges. We introduce Konnektor, an open-source Python package, for systematically planning, modifying, and analyzing free energy…
Jenke Scheen, Mark Mackey, Julien Michel
Relative binding free energy (RBFE) calculations are increasingly used to support the ligand optimisation problem in early-stage drug discovery. Because RBFE calculations frequently rely on alchemical perturbations between ligands in a congeneric series, practitioners are required to estimate an optimal combination of…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi + 3 more
Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of…