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Graph neural network
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In this study, we explore the potential of graph neural networks (GNNs), in combination with transfer learning, for the prediction of molecular solubility, a crucial property in drug discovery and materials science. Our approach begins with the development of a GNN-based model to predict the dipole moment of molecules.
8p
viwalton
02-07-2024
3
1
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In this paper, we present VFFinder, a novel graph-based approach for automated silent vulnerability fix identification. To precisely capture the meaning of code changes, the changed code is represented in connection with the related unchanged code.
13p
viambani
18-06-2024
3
1
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This paper introduces a novel framework, July, which serves the dual purpose of detecting vulnerable commits and localizing the root causes of the vulnerabilities. The fundamental concept of July is that the determinant of the vulnerability of a commit is the inherent meaning embedded in its changed code. For just-in-time vulnerability detection (JIT-VD), July represents each commit by a Code Transformation Graph and employs a Graph Neural Network model to capture their meanings and distinguish between vulnerable and non-vulnerable commits.
23p
dianmotminh02
03-05-2024
3
1
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Contemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models.
16p
vibransone
28-03-2024
4
2
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The emergence of image-based systems to improve diagnostic pathology precision, involving the intent to label sets or bags of instances, greatly hinges on Multiple Instance Learning for Whole Slide Images(WSIs). Contemporary works have shown excellent performance for a neural network in MIL settings.
11p
vialfrednobel
23-12-2023
4
3
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The survey starts by reviewing basic concepts on graph theory and graph signal processing. Next, we provide systematic categorization of graph learning methods to address two aspects above respectively. Finally, we conclude our paper with discussions and open issues in research and practice.
21p
visystrom
22-11-2023
8
5
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This paper focuses on an exact algorithm for solving NP-hard combinatorial optimization problems which frequently requires significant specialized knowledge and trial and error, especially, Mixed Integer Linear Programs (MILP). This challenging, tedious process can be automated by learning the algorithms instead.
6p
visystrom
22-11-2023
5
4
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In this paper, we propose to use the combination of Imaging Graph Neural Network With Defined Pattern to detect vulnerabilities in smart contracts. We construct a contract graph that shows the relationship between the main components in a smart contract.
10p
visystrom
22-11-2023
6
5
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Ebook "Natural language processing with PyTorch: Build intelligent language applications using deep learning" aims to bring newcomers to natural language processing (NLP) and deep learning to a tasting table covering important topics in both areas. Both of these subject areas are growing exponentially. As it introduces both deep learning and NLP with an emphasis on implementation, this book occupies an important middle ground.
210p
dangsovu
20-10-2023
8
4
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In recent times, we have witnessed dramatic progresses and emergence of advanced deep neural architectures in natural language processing (NLP) domain. The advanced sequence-to-sequence (seq2seq)/transformer based architectures have demonstrated remarkable improvements in multiple NLP’s tasks, including text categorization.
10p
viannee
02-08-2023
6
5
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In this study, the relations between 2 medical concepts are classified by simultaneously learning representations of text segments in the context of sentence syntactic dependency: preceding, concept1, middle, concept2, and succeeding segments. Seg-GCRN was systematically evaluated on the i2b2/VA relation classification challenge datasets.
7p
visteverogers
24-06-2023
7
2
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Most methods for inferring gene-gene interactions from expression data focus on intracellular interactions. The availability of high-throughput spatial expression data opens the door to methods that can infer such interactions both within and between cells. To achieve this, we developed Graph Convolutional Neural networks for Genes (GCNG).
16p
viarchimedes
26-01-2022
10
0
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Graph neural networks (GNNs) have achieved superior performance and gained significant interest in various domains. However, most of the existing GNNs are considered for homogeneous graphs, whereas real-world systems are usually modeled as heterogeneous graphs or heterogeneous information networks (HINs).
26p
guernsey
28-12-2021
4
0
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Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms.
15p
viflorida2711
30-10-2020
9
1
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Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes.
11p
viconnecticut2711
28-10-2020
16
1
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Alkaloids, a class of organic compounds that contain nitrogen bases, are mainly synthesized as secondary metabolites in plants and fungi, and they have a wide range of bioactivities. Although there are thousands of compounds in this class, few of their biosynthesis pathways are fully identified.
13p
vijisoo2711
27-10-2020
13
1
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The treatment of complex diseases by taking multiple drugs becomes increasingly popular. However, drug-drug interactions (DDIs) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. DDI detection in the wet lab is expensive and time-consuming.
15p
vicolorado2711
22-10-2020
10
0
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Neural network induction graph for pattern recognition
18p
nhan4321
29-10-2009
109
30
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