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Dong-Jun Yu

Showing results (31-40 of 126) with videos related to

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Briefings in Bioinformatics|February 4, 2021
Why can deep convolutional neural networks improve protein fold recognition? A visual explanation by interpretationYan Liu, Yi-Heng Zhu, Xiaoning Song, et al.
IEEE Transactions on Nanobioscience|October 7, 2015
A Cascade Random Forests Algorithm for Predicting Protein-Protein Interaction SitesZhi-Sen Wei, Jing-Yu Yang, Hong-Bin Shen, et al.
Analytical Biochemistry|September 19, 2020
DeepPPSite: A deep learning-based model for analysis and prediction of phosphorylation sites using efficient sequence informationSaeed Ahmed, Muhammad Kabir, Muhammad Arif, et al.
Combinatorial Chemistry & High Throughput Screening|December 7, 2020
TargetMM: Accurate Missense Mutation Prediction by Utilizing Local and Global Sequence Information with Classifier EnsembleFang Ge, Jun Hu, Yi-Heng Zhu, et al.
Methods in Molecular Biology (Clifton, N.J.)|July 29, 2025
Machine Learning for Protein Function PredictionYi-Heng Zhu, Zi Liu, Yu Ding, et al.
Briefings in Bioinformatics|February 13, 2024
ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein-DNA binding site predictionYi-Heng Zhu, Zi Liu, Yan Liu, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics|June 11, 2017
A Self-Training Subspace Clustering Algorithm under Low-Rank Representation for Cancer Classification on Gene Expression DataChun-Qiu Xia, Ke Han, Yong Qi, et al.
Proteins|August 14, 2019
Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13Yang Li, Chengxin Zhang, Eric W Bell, et al.
Bioinformatics (Oxford, England)|September 21, 2021
ATPdock: a template-based method for ATP-specific protein-ligand dockingLiang Rao, Ning-Xin Jia, Jun Hu, et al.
Journal of Chemical Information and Modeling|August 26, 2022
Learning Protein Embedding to Improve Protein Fold Recognition Using Deep Metric LearningGuan-Yu Zhu, Yan Liu, Peng-Hao Wang, et al.
Pageof 13

Showing results (31-40 of 126) with videos related to

Sort By:
Pageof 13
Briefings in Bioinformatics|February 4, 2021
Why can deep convolutional neural networks improve protein fold recognition? A visual explanation by interpretationYan Liu, Yi-Heng Zhu, Xiaoning Song, et al.
IEEE Transactions on Nanobioscience|October 7, 2015
A Cascade Random Forests Algorithm for Predicting Protein-Protein Interaction SitesZhi-Sen Wei, Jing-Yu Yang, Hong-Bin Shen, et al.
Analytical Biochemistry|September 19, 2020
DeepPPSite: A deep learning-based model for analysis and prediction of phosphorylation sites using efficient sequence informationSaeed Ahmed, Muhammad Kabir, Muhammad Arif, et al.
Combinatorial Chemistry & High Throughput Screening|December 7, 2020
TargetMM: Accurate Missense Mutation Prediction by Utilizing Local and Global Sequence Information with Classifier EnsembleFang Ge, Jun Hu, Yi-Heng Zhu, et al.
Methods in Molecular Biology (Clifton, N.J.)|July 29, 2025
Machine Learning for Protein Function PredictionYi-Heng Zhu, Zi Liu, Yu Ding, et al.
Briefings in Bioinformatics|February 13, 2024
ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein-DNA binding site predictionYi-Heng Zhu, Zi Liu, Yan Liu, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics|June 11, 2017
A Self-Training Subspace Clustering Algorithm under Low-Rank Representation for Cancer Classification on Gene Expression DataChun-Qiu Xia, Ke Han, Yong Qi, et al.
Proteins|August 14, 2019
Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13Yang Li, Chengxin Zhang, Eric W Bell, et al.
Bioinformatics (Oxford, England)|September 21, 2021
ATPdock: a template-based method for ATP-specific protein-ligand dockingLiang Rao, Ning-Xin Jia, Jun Hu, et al.
Journal of Chemical Information and Modeling|August 26, 2022
Learning Protein Embedding to Improve Protein Fold Recognition Using Deep Metric LearningGuan-Yu Zhu, Yan Liu, Peng-Hao Wang, et al.
Pageof 13