Xinming Tu

发表

Identifying complex motifs in massive omics data with a variable-convolutional layer in deep neural network.

Ge Gao, Yang Ding, Jing-Yi Li, 2021, Briefings in bioinformatics.

Identifying complex sequence patterns in massive omics data with a variable-convolutional layer in deep neural network

Ge Gao, Yang Ding, Jing-Yi Li, 2021 .

Identifying complex sequence patterns with a variable-convolutional layer effectively and efficiently

Ge Gao, Yang Ding, Jing-Yi Li, 2021 .

Identifying complex motifs in massive omics data with a variable-convolutional layer in deep neural network

Ge Gao, Yang Ding, Jing-Yi Li, 2018, bioRxiv.

Effective identification of sequence patterns via a new convolutional model with adaptively learned kernels

Yang Ding, Shen Jin, Ge Gao, 2018 .

Expectation pooling: an effective and interpretable pooling method for predicting DNA–protein binding

Minghua Deng, Xiao Luo, Xinming Tu, 2019, bioRxiv.

Spatial-linked alignment tool (SLAT) for aligning heterogenous slices

Ge Gao, Zhi-Jie Cao, Xinming Tu, 2023, bioRxiv.

Cross-Linked Unified Embedding for cross-modality representation learning

S. Mostafavi, Zhi-Jie Cao, Ge Gao, 2022, NeurIPS.

Identifying complex motifs in massive omics data with a variable-convolutional layer in deep neural network

Yang Ding, Ge Gao, Shen Jin, 2018, bioRxiv.

Identifying complex sequence patterns in massive omics data with a variable-convolutional layer in deep neural network

Ge Gao, Yang Ding, Jing-Yi Li, 2021 .

Effective identification of sequence patterns via 1 a new convolutional model with adaptively 2 learned kernels 3

Yang Ding, Jing-Yi Li, Shen Jin, 2020 .

An exact transformation for CNN kernel ena- bles accurate sequence motif identification and leads to a potentially full probabilistic in- terpretation of CNN

M. Wang, Ge Gao, Yang Ding, 2019 .