Search Our Scientific Publications & Authors

Appl Intell (Dordr) 2022 May 21:1-14. Epub 2022 May 21.

College of Computer and Information Technology, China Three Gorges University, Yichang, 443002 China.

In the era of the Internet and big data, online social media platforms have been developing rapidly, which accelerate rumors circulation. Rumor detection on social media is a worldwide challenging task due to rumor's feature of high speed, fragmental information and extensive range. Most existing approaches identify rumors based on single-layered hybrid features like word features, sentiment features and user characteristics, or multimodal features like the combination of text features and image features. Read More

IEEE Trans Neural Netw Learn Syst 2022 May 25;PP. Epub 2022 May 25.

In recent years, many recommender systems using network embedding (NE) such as graph neural networks (GNNs) have been extensively studied in the sense of improving recommendation accuracy. However, such attempts have focused mostly on utilizing only the information of positive user-item interactions with high ratings. Thus, there is a challenge on how to make use of low rating scores for representing users' preferences since low ratings can be still informative in designing NE-based recommender systems. Read More

Bioinformatics 2022 May 24. Epub 2022 May 24.

Department of Anesthesia, Pain and Perioperative Medicine, Stanford University School of Medicine, Stanford, CA, 94305.

Motivation: Our ability to identify causative genetic factors for mouse genetic models of human diseases and biomedical traits has been limited by the difficulties associated with identifying true causative factors, which are often obscured by the many false positive genetic associations produced by a GWAS.

Results: To accelerate the pace of genetic discovery, we developed a graph neural network (GNN)-based automated pipeline (GNNHap) that could rapidly analyze mouse genetic model data and identify high probability causal genetic factors for analyzed traits. After assessing the strength of allelic associations with the strain response pattern; this pipeline analyzes 29M published papers to assess candidate gene-phenotype relationships; and incorporates the information obtained from a protein-protein interaction network and protein sequence features into the analysis. Read More

Patterns (N Y) 2022 May 27;3(5):100491. Epub 2022 Apr 27.

Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA.

Machine-learning-based materials property prediction models have emerged as a promising approach for new materials discovery, among which the graph neural networks (GNNs) have shown the best performance due to their capability to learn high-level features from crystal structures. However, existing GNN models suffer from their lack of scalability, high hyperparameter tuning complexity, and constrained performance due to over-smoothing. We propose a scalable global graph attention neural network model DeeperGATGNN with differentiable group normalization (DGN) and skip connections for high-performance materials property prediction. Read More

Sci Rep 2022 May 19;12(1):8360. Epub 2022 May 19.

Department of Electrical Engineering, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan, 342030, India.

Proteins are the essential biological macromolecules required to perform nearly all biological processes, and cellular functions. Proteins rarely carry out their tasks in isolation but interact with other proteins (known as protein-protein interaction) present in their surroundings to complete biological activities. The knowledge of protein-protein interactions (PPIs) unravels the cellular behavior and its functionality. Read More