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Found 10 papers, 6 papers with code
FGBERT: Function-Driven Pre-trained Gene Language Model for Metagenomics
Metagenomic data, comprising mixed multi-species genomes, are prevalent in diverse environments like oceans and soils, significantly impacting human health and ecological functions.
Must: Maximizing Latent Capacity of Spatial Transcriptomics Data
MuST integrates the multi-modality information contained in the ST data effectively into a uniform latent space to provide a foundation for all the downstream tasks.
Protein Language Models and Structure Prediction: Connection and Progression
The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding.
EVNet: An Explainable Deep Network for Dimension Reduction
The proposed techniques are integrated with a visual interface to help the user to adjust EVNet to achieve better DR performance and explainability.
UDRN: Unified Dimensional Reduction Neural Network for Feature Selection and Feature Projection
We propose that the ideal DR approach combines both FS and FP into a unified end-to-end manifold learning framework, simultaneously performing fundamental feature discovery while maintaining the intrinsic relationships between data samples in the latent space.
Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning
Spatiotemporal predictive learning aims to generate future frames by learning from historical frames.
Ranked #12 on
Video Prediction
on Moving MNIST
Unsupervised Deep Manifold Attributed Graph Embedding
Unsupervised attributed graph representation learning is challenging since both structural and feature information are required to be represented in the latent space.
Ranked #2 on
Node Clustering
on Pubmed
Conditional Local Convolution for Spatio-temporal Meteorological Forecasting
We further propose the distance and orientation scaling terms to reduce the impacts of irregular spatial distribution.
Towards Robust Graph Neural Networks against Label Noise
A pseudo label is computed from the neighboring labels for each node in the training set using LP; meta learning is utilized to learn a proper aggregation of the original and pseudo label as the final label.
