most citedInterpretable multimodal fusion networks reveal mechanisms of brain cognition

8 citations · 12 across the 4 of their papers we have counts for

collaborators

5 papers

stat.ME20222 cited

Statistical Inference of Cell-type Proportions Estimated from Bulk Expression Data

Biao Cai, Jingfei Zhang, Hongyu Li +2

There is a growing interest in cell-type-specific analysis from bulk samples with a mixture of different cell types. A critical first step in such analyses is the accurate estimati…

q-bio.NC20202 cited

Functional connectome fingerprinting: Identifying individuals and predicting cognitive function via deep learning

Biao Cai, Gemeng Zhang, Aiying Zhang +6

The dynamic characteristics of functional network connectivity have been widely acknowledged and studied. Both shared and unique information has been shown to be present in the con…

stat.ML2020

Causal inference of brain connectivity from fMRI with -Learning Incorporated Linear non-Gaussian Acyclic Model (-LiNGAM)

Aiying Zhang, Gemeng Zhang, Biao Cai +6

Functional connectivity (FC) has become a primary means of understanding brain functions by identifying brain network interactions and, ultimately, how those interactions produce c…

q-bio.NC20208 cited

Interpretable multimodal fusion networks reveal mechanisms of brain cognition

Wenxing Hu, Xianghe Meng, Yuntong Bai +7

Multimodal fusion benefits disease diagnosis by providing a more comprehensive perspective. Developing algorithms is challenging due to data heterogeneity and the complex within- a…

stat.ME2020

Latent Network Structure Learning from High Dimensional Multivariate Point Processes

Biao Cai, Jingfei Zhang, Yongtao Guan

Learning the latent network structure from large scale multivariate point process data is an important task in a wide range of scientific and business applications. For instance, w…