Multivariate time-series classification with hierarchical variational graph pooling
In recent years, multivariate time-series classification (MTSC) has attracted considerable
attention owing to the advancement of sensing technology. Existing deep-learning-based …
attention owing to the advancement of sensing technology. Existing deep-learning-based …
scENCORE: leveraging single-cell epigenetic data to predict chromatin conformation using graph embedding
Dynamic compartmentalization of eukaryotic DNA into active and repressed states enables
diverse transcriptional programs to arise from a single genetic blueprint, whereas its …
diverse transcriptional programs to arise from a single genetic blueprint, whereas its …
MTHetGNN: A heterogeneous graph embedding framework for multivariate time series forecasting
Multivariate time series forecasting, which analyzes historical time series to predict future
trends, can effectively help decision-making. Complex relations among variables in MTS …
trends, can effectively help decision-making. Complex relations among variables in MTS …
Multivariate time series forecasting with transfer entropy graph
Multivariate Time Series (MTS) forecasting is an essential problem in many fields. Accurate
forecasting results can effectively help in making decisions. To date, many MTS forecasting …
forecasting results can effectively help in making decisions. To date, many MTS forecasting …
Impeller: a path-based heterogeneous graph learning method for spatial transcriptomic data imputation
Results To address these issues, we introduce Impeller, a path-based heterogeneous graph
learning method for spatial transcriptomic data imputation. Impeller has two unique …
learning method for spatial transcriptomic data imputation. Impeller has two unique …