23 citations · 80 across the 12 of their papers we have counts for
12 papers
MMA-RNN: A Multi-level Multi-task Attention-based Recurrent Neural Network for Discrimination and Localization of Atrial Fibrillation
Yifan Sun, Jingyan Shen, Yunfan Jiang +3
The automatic detection of atrial fibrillation based on electrocardiograph (ECG) signals has received wide attention both clinically and practically. It is challenging to process E…
Biomarker-guided heterogeneity analysis of genetic regulations via multivariate sparse fusion
Sanguo Zhang, Xiaonan Hu, Ziye Luo +3
Heterogeneity is a hallmark of many complex diseases. There are multiple ways of defining heterogeneity, among which the heterogeneity in genetic regulations, for example GEs (gene…
An integrative sparse boosting analysis of cancer genomic commonality and difference
Yifan Sun, Zhengyang Sun, Yu Jiang +2
In cancer research, high-throughput profiling has been extensively conducted. In recent studies, the integrative analysis of data on multiple cancer patient groups/subgroups has be…
Identification of cancer omics commonality and difference via community fusion
Yifan Sun, Yu Jiang, Yang Li +1
The analysis of cancer omics data is a "classic" problem, however, still remains challenging. Advancing from early studies that are mostly focused on a single type of cancer, some…
Robust structured heterogeneity analysis approach for high-dimensional data
Yifan Sun, Ziye Luo, Xinyan Fan
Revealing relationships between genes and disease phenotypes is a critical problem in biomedical studies. This problem has been challenged by the heterogeneity of diseases. Patient…
Robust nonparametric integrative analysis to decipher heterogeneity and commonality across subgroups using sparse boosting
Zihan Li, Ziye Luo, Yifan Sun
In many biomedical problems, data are often heterogeneous, with samples spanning multiple patient subgroups, where different subgroups may have different disease subtypes, stages,…
Semiparametric integrative interaction analysis for non-small-cell lung cancer
Yang Li, Fan Wang, Rong Li +1
In the genomic analysis, it is significant while challenging to identify markers associated with cancer outcomes or phenotypes. Based on the biological mechanisms of cancers and th…
Regression-based heterogeneity analysis to identify overlapping subgroup structure in high-dimensional data
Ziye Luo, Xinyue Yao, Yifan Sun +1
Heterogeneity is a hallmark of complex diseases. Regression-based heterogeneity analysis, which is directly concerned with outcome-feature relationships, has led to a deeper unders…
An adaptive shortest-solution guided decimation approach to sparse high-dimensional linear regression
Xue Yu, Yifan Sun, Haijun Zhou
High-dimensional linear regression model is the most popular statistical model for high-dimensional data, but it is quite a challenging task to achieve a sparse set of regression c…
Subgroup analysis for the functional linear model
Yifan Sun, Ziyi Liu, Wu Wang
Classical functional linear regression models the relationship between a scalar response and a functional covariate, where the coefficient function is assumed to be identical for a…
NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data
Yifan Sun, Linan Zhang, Hayden Schaeffer
We propose a neural network based approach for extracting models from dynamic data using ordinary and partial differential equations. In particular, given a time-series or spatio-t…
Learning low-dimensional state embeddings and metastable clusters from time series data
Yifan Sun, Yaqi Duan, Hao Gong +1
This paper studies how to find compact state embeddings from high-dimensional Markov state trajectories, where the transition kernel has a small intrinsic rank. In the spirit of di…