Correlated coupled matrix tensor factorization method for simultaneous EEG-fMRI data fusion

2020 ◽  
Vol 62 ◽  
pp. 102071
Author(s):  
Raziyeh Mosayebi ◽  
Gholam-Ali Hossein-Zadeh
Keyword(s):  
Data Fusion ◽  
Factorization Method ◽  
Fmri Data ◽  
Tensor Factorization

scholarly journals Author Correction: Avocado: a multi-scale deep tensor factorization method learns a latent representation of the human epigenome

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jacob Schreiber ◽  
Timothy Durham ◽  
Jeffrey Bilmes ◽  
William Stafford Noble
Keyword(s):  
Factorization Method ◽  
Tensor Factorization ◽  
Multi Scale

Tensor factorization method based on review text semantic similarity for rating prediction

2018 ◽  
Vol 114 ◽  
pp. 629-638 ◽  
Author(s):  
James Chambua ◽  
Zhendong Niu ◽  
Abdallah Yousif ◽  
Jimmy Mbelwa
Keyword(s):  
Semantic Similarity ◽  
Factorization Method ◽  
Tensor Factorization ◽  
Rating Prediction

scholarly journals DTF: Deep Tensor Factorization for predicting anticancer drug synergy

2020 ◽  
Vol 36 (16) ◽  
pp. 4483-4489
Author(s):  
Zexuan Sun ◽  
Shujun Huang ◽  
Peiran Jiang ◽  
Pingzhao Hu
Keyword(s):  
Logistic Regression ◽  
Performance Metrics ◽  
Factorization Method ◽  
Drug Combinations ◽  
Supplementary Information ◽  
Tensor Factorization ◽  
Drug Synergy ◽  
Synergistic Drug Combinations

Abstract Motivation Combination therapies have been widely used to treat cancers. However, it is cost and time consuming to experimentally screen synergistic drug pairs due to the enormous number of possible drug combinations. Thus, computational methods have become an important way to predict and prioritize synergistic drug pairs. Results We proposed a Deep Tensor Factorization (DTF) model, which integrated a tensor factorization method and a deep neural network (DNN), to predict drug synergy. The former extracts latent features from drug synergy information while the latter constructs a binary classifier to predict the drug synergy status. Compared to the tensor-based method, the DTF model performed better in predicting drug synergy. The area under precision-recall curve (PR AUC) was 0.58 for DTF and 0.24 for the tensor method. We also compared the DTF model with DeepSynergy and logistic regression models, and found that the DTF outperformed the logistic regression model and achieved similar performance as DeepSynergy using several performance metrics for classification task. Applying the DTF model to predict missing entries in our drug–cell-line tensor, we identified novel synergistic drug combinations for 10 cell lines from the 5 cancer types. A literature survey showed that some of these predicted drug synergies have been identified in vivo or in vitro. Thus, the DTF model could be a valuable in silico tool for prioritizing novel synergistic drug combinations. Availability and implementation Source code and data are available at https://github.com/ZexuanSun/DTF-Drug-Synergy. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Assessing Reproducibility of MEG and fMRI Data Fusion Method in Neural Dynamics of Object Vision

2019 ◽  
Vol 19 (10) ◽  
pp. 113
Author(s):  
Benjamin Lahner ◽  
Yalda Mohsenzadeh ◽  
Caitlin Mullin ◽  
Radoslaw Cichy ◽  
Aude Oliva
Keyword(s):  
Data Fusion ◽  
Neural Dynamics ◽  
Fmri Data ◽  
Fusion Method
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