7 citations · 7 across the 3 of their papers we have counts for
6 papers · 1 filter
Spatio-Temporal Tensor Sketching via Adaptive Sampling
Jing Ma, Qiuchen Zhang, Joyce C. Ho +1
Mining massive spatio-temporal data can help a variety of real-world applications such as city capacity planning, event management, and social network analysis. The tensor represen…
TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records
Ardavan Afshar, Ioakeim Perros, Haesun Park +5
Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of p…
Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction
Bonggun Shin, Sungsoo Park, Keunsoo Kang +1
Predicting drug-target interactions (DTI) is an essential part of the drug discovery process, which is an expensive process in terms of time and cost. Therefore, reducing DTI cost…
Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis
Jing Ma, Qiuchen Zhang, Jian Lou +3
Tensor factorization has been demonstrated as an efficient approach for computational phenotyping, where massive electronic health records (EHRs) are converted to concise and meani…
PIVETed-Granite: Computational Phenotypes through Constrained Tensor Factorization
Jette Henderson, Bradley A. Malin, Joyce C. Ho +1
It has been recently shown that sparse, nonnegative tensor factorization of multi-modal electronic health record data is a promising approach to high-throughput computational pheno…
COPA: Constrained PARAFAC2 for Sparse & Large Datasets
Ardavan Afshar, Ioakeim Perros, Evangelos E. Papalexakis +3
PARAFAC2 has demonstrated success in modeling irregular tensors, where the tensor dimensions vary across one of the modes. An example scenario is modeling treatments across a set o…