Graph-Based 3-Dimensional Spatial Gene Neighborhood Networks of Single Cells in Gels and Tissues by Zhou Fang, Kelsey Krusen, Hannah Priest, Mingshuang Wang, Sungwoong Kim, Anirudh Sriram, Ashritha Yellanki, Ankur Singh, Edwin Horwitz, Ahmet F. Coskun

“…Then, a graph autoencoder projects the gene proximity relationships into a latent space. …”
Get full text

Bigpicc: a graph-based approach to identifying carcinogenic gene combinations from mutation data by Vladyslav Oles, Sajal Dash, Ramu Anandakrishnan

Get full text

SheepDoctor: A knowledge graph enhanced large language model for sheep disease diagnosis by Jiayi Xiong, Yong Zhou, Fang Tian, Fuchuan Ni, Liang Zhao

“…A comprehensive question-and-answer (Q&A) dataset was constructed using prompt techniques, resulting in 5987 samples covering 207 sheep diseases. This dataset included detailed symptom descriptions, treatments, and related information, which were also structured into a knowledge graph. …”
Get full text

Analysis of Student Errors in Solving Numeracy Literacy Problems of Graph Representation Model in Elementary School by Intan Sari Rufiana, Slamet Arifin, Mohammad Yusuf Randy, Fierda Nursitasari Amaliya

“…This study aims to describe the types of student errors in solving numeracy literacy problems of graph representation models. This study used a mixed methods research design with a sequential exploratory type. …”
Get full text

BiFormer for Scene Graph Generation Based on VisionNet With Taylor Hiking Optimization Algorithm by S. Monesh, N. C. Senthilkumar

“…In this study, a deep learning-based optimization model, VisionNet_Taylor Hiking Optimization Algorithm (VisionNet_THOA), was introduced to generate high-quality scene graphs from noisy samples. Here, objects were detected by performing semantic segmentation using dynamic routing. …”
Get full text

Graph-Enhanced Deep Learning With Character Similarity Mining for Automated NOTAM Correction in Aviation Systems by Bing Dong, Haoran Yao, Chuang Luo, Ruichao Yang, Ziyue Wang

“…During the masking stage of the CKBERT model, both positive and negative samples of knowledge graph triples were generated and integrated into multi-hop comparative learning. …”
Get full text

Few-shot English text classification method based on graph convolutional network and prompt learning by Yunfei Jin

“…Therefore, this paper proposes a novel few-shot English text classification method based on graph neural network and prompt learning. The text level graph convolutional network is used to construct a graph for each input text and share global parameters, and the result of the text graph neural network is used as the input of the prototype network. …”
Get full text

New Psychometric Evidence of the Life Satisfaction Scale in Older Adults: An Exploratory Graph Analysis Approach by Julio Dominguez-Vergara, Brigitte Aguilar-Salcedo, Rita Orihuela-Anaya, José Villanueva-Alvarado

“…A non-probabilistic convenience sampling was used. The Satisfaction with Life Scale (SWLS) was analyzed using EGA with the Gaussian GLASSO model to assess its dimensionality and structural consistency. …”
Get full text

ARGContextProfiler: extracting and scoring the genomic contexts of antibiotic resistance genes using assembly graphs by Nazifa Ahmed Moumi, Shafayat Ahmed, Connor Brown, Amy Pruden, Liqing Zhang

“…By leveraging the assembly graph for genomic neighborhood extraction and validating contexts through read mapping, ARGContextProfiler minimizes chimeric errors that are a common artifact of assembly outputs. …”
Get full text

Topological graphs: a review of some of our achievements and perspectives in physical chemistry and homogeneous catalysis by Bougueroua, Sana, Aboulfath, Ylène, Cimas, Alvaro, Hashemi, Ali,  Pidko, Evgeny A., Barth, Dominique, Gaigeot, Marie-Pierre

“…We show hereby that the use of algorithmic graph theory provides a direct and fast approach to identify the actual conformations sampled over time in a trajectory. …”
Get full text

Classification of Pulmonary Nodules Using Multimodal Feature‐Driven Graph Convolutional Networks with Specificity Proficiency by Renjie Xu, Zhanlue Liang, Dan Wang, Rui Zhang, Jiayi Li, Lingfeng Bi, Kai Zhang, Weimin Li

“…Graph neural networks could compare the difference among all samples (nodes in graph) and transmit the interrelationship among them to obtain a global landscape. …”
Get full text

A Transductive Zero-Shot Learning Framework for Ransomware Detection Using Malware Knowledge Graphs by Ping Wang, Hao-Cyuan Li, Hsiao-Chung Lin, Wen-Hui Lin, Nian-Zu Xie

“…This study proposes a Transductive Zero-Shot Learning (TZSL) model based on the Vector Quantized Variational Autoencoder (VQ-VAE) architecture, integrated with a malware knowledge graph constructed from sandbox behavioral analysis of ransomware families. …”
Get full text

WirelessNet: An Efficient Radio Access Network Model Based on Heterogeneous Graph Neural Networks by Jose Perdomo, M. A. Gutierrez-Estevez, Chan Zhou, Jose F. Monserrat

“…Heterogeneous graphs are fed as samples into the HMPGNN model to simulate the wireless phenomena within WirelessNet’s model architecture. …”
Get full text

Bayesian Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG by Polina Suter, Jack Kuipers, Giusi Moffa, Niko Beerenwinkel

“… The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for structure learning and sampling of Bayesian networks. The package includes tools to search for a maximum a posteriori (MAP) graph and to sample graphs from the posterior distribution given the data. …”
Get full text

ON SOME FEATURES OF ULTRASOUND REFLECTION WATER-SAMPLE IN AN INCLINED FALL (PHYSICAL MODELING) by Fedin Konstantin, Lukyanowa Alina, Kolesnikov Yuriy

Get full text

The effect of a vegetation period on protein percentage in seed of the pea collection samples by A. R. Ashiev, K. N. Khabibullin, M. V. Skulova

“…The calculation of the correlation coefficient showed the absence of a correlation dependence of protein percentage on length of vegetation, both on average for the collection (r = 0.03+0.10) and for the groups of leafless (r = 0.08+0.14) and foliate (r = 0.05+0.15) leaf morphotypes. The construction of graphs with errors for groups of leafy morphotypes revealed samples with a protein percentage of more than 25.0 %. …”
Get full text

On the Impact of Labeled Sample Selection in Semisupervised Learning for Complex Visual Recognition Tasks by Eftychios Protopapadakis, Athanasios Voulodimos, Anastasios Doulamis

“…In this paper, we scrutinize the effectiveness of different labeled sample selection approaches for training set creation, to be used in semisupervised learning approaches for complex visual pattern recognition problems. …”
Get full text

Application of artificial intelligence for quantifying Plasmodium berghei in blood samples of infected mice by Noha Talal Zelai

Get full text

ASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction. by Yunyun Dong, Yuanrong Zhang, Yuhua Qian, Yiming Zhao, Ziting Yang, Xiufang Feng

“…By contrasting the augmented graph with the original input, the model delineates distinct positive and negative sample sets at both node and graph levels. …”
Get full text

The relationship between economic growth and carbon emissions based on the combination of graph neural network and wavelet transform by Sen Wang

Get full text