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Found 16 papers, 1 papers with code
Adaptive control of dynamic networks
However, the efficacy of the incumbent MDS may diminish as the network topologies evolve.
NetMoST: A network-based machine learning approach for subtyping schizophrenia using polygenic SNP allele biomarkers
Subtyping neuropsychiatric disorders like schizophrenia is essential for improving the diagnosis and treatment of complex diseases.
Pixel-global Self-supervised Learning with Uncertainty-aware Context Stabilizer
Moreover, we used Monte Carlo dropout in the stabilizer to measure uncertainty and adaptively balance the discrepancy between the representations of the same pixels in different views.
RAW-GNN: RAndom Walk Aggregation based Graph Neural Network
Due to the homophily assumption of Graph Convolutional Networks (GCNs) that these methods use, they are not suitable for heterophily graphs where nodes with different labels or dissimilar attributes tend to be adjacent.
Identification of cancer-keeping genes as therapeutic targets by finding network control hubs
One of the recent approaches is based on network structural controllability that focuses on finding a control scheme and driver genes that can steer the cell from an arbitrary state to a designated state.
Response to: Significance and stability of deep learning-based identification of subtypes within major psychiatric disorders. Molecular Psychiatry (2022)
They questioned the generalizability of our methods and the statistical significance, stability, and overfitting of the results, and proposed a pipeline for disease subtyping.
Total controllability analysis discovers explainable drugs for Covid-19 treatment
One such approach adopts structural controllability, a theory for controlling a network (the cell).
Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning
However, the existing contrastive learning methods are inadequate for heterogeneous graphs because they construct contrastive views only based on data perturbation or pre-defined structural properties (e. g., meta-path) in graph data while ignore the noises that may exist in both node attributes and graph topologies.
Weaving Attention U-net: A Novel Hybrid CNN and Attention-based Method for Organs-at-risk Segmentation in Head and Neck CT Images
Our results of the new Weaving Attention U-net demonstrate superior or similar performance on the segmentation of head and neck CT images.
Pyramid Medical Transformer for Medical Image Segmentation
Deep neural networks have been a prevailing technique in the field of medical image processing.
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning
We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
Semi-supervised Semantic Segmentation of Prostate and Organs-at-Risk on 3D Pelvic CT Images
We developed a novel semi-supervised adversarial deep learning approach for 3D pelvic CT image semantic segmentation.
ARPM-net: A novel CNN-based adversarial method with Markov Random Field enhancement for prostate and organs at risk segmentation in pelvic CT images
A segmentation network and a discriminator network were trained jointly, and only the segmentation network was used for prediction.
An efficient algorithm for finding all possible input nodes for controlling complex networks
The main idea is to modify a maximum matching algorithm to make it efficient for finding all possible input nodes by computing only one MIS.
Discrete Mathematics Social and Information Networks Physics and Society
SAS+ Planning as Satisfiability
We prove the correctness of the new encoding by establishing an isomorphism between the solution plans of SASE and that of STRIPS based encodings.
An Effective Algorithm for and Phase Transitions of the Directed Hamiltonian Cycle Problem
By combining effective algorithms for the AP and SAT, our algorithm significantly outperforms previous exact DHCP algorithms, including an algorithm based on the award-winning Concorde TSP algorithm.
