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Jiangning Song

Showing results (81-90 of 328) 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.
Biomed Research International|October 6, 2016
Integrated Analysis of Multiscale Large-Scale Biological Data for Investigating Human Disease 2016Tao Huang, Lei Chen, Jiangning Song, et al.
Bioinformatics (Oxford, England)|May 20, 2020
PASSION: an ensemble neural network approach for identifying the binding sites of RBPs on circRNAsCangzhi Jia, Yue Bi, Jinxiang Chen, et al.
Environmental Health Perspectives|March 7, 2022
Deep Ensemble Machine Learning Framework for the Estimation of <math></math> ConcentrationsWenhua Yu, Shanshan Li, Tingting Ye, et al.
Journal of Chemical Information and Modeling|August 21, 2025
BiVAE-CPI: An Interpretable Generative Model Using a Bilateral Variational Autoencoder for Compound-Protein Interaction PredictionYongxin Zhu, Jianxin Wang, Shiyue He, et al.
Genome Biology|May 2, 2026
EvoRMD: integrating biological context and evolutionary RNA language models for interpretable prediction of RNA modificationsBo Wang, Hao Zhang, Taoyong Cui, et al.
Plos One|November 21, 2012
An integrative computational framework based on a two-step random forest algorithm improves prediction of zinc-binding sites in proteinsCheng Zheng, Mingjun Wang, Kazuhiro Takemoto, et al.
Briefings in Bioinformatics|November 23, 2020
Computational identification of eukaryotic promoters based on cascaded deep capsule neural networksYan Zhu, Fuyi Li, Dongxu Xiang, et al.
Briefings in Bioinformatics|November 2, 2018
Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithmsLeyi Wei, Jie Hu, Fuyi Li, et al.
Briefings in Bioinformatics|March 14, 2014
Towards more accurate prediction of protein folding rates: a review of the existing Web-based bioinformatics approachesCatherine Ching Han Chang, Beng Ti Tey, Jiangning Song, et al.
Pageof 33

Showing results (81-90 of 328) with videos related to

Sort By:
Pageof 33
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.
Biomed Research International|October 6, 2016
Integrated Analysis of Multiscale Large-Scale Biological Data for Investigating Human Disease 2016Tao Huang, Lei Chen, Jiangning Song, et al.
Bioinformatics (Oxford, England)|May 20, 2020
PASSION: an ensemble neural network approach for identifying the binding sites of RBPs on circRNAsCangzhi Jia, Yue Bi, Jinxiang Chen, et al.
Environmental Health Perspectives|March 7, 2022
Deep Ensemble Machine Learning Framework for the Estimation of <math></math> ConcentrationsWenhua Yu, Shanshan Li, Tingting Ye, et al.
Journal of Chemical Information and Modeling|August 21, 2025
BiVAE-CPI: An Interpretable Generative Model Using a Bilateral Variational Autoencoder for Compound-Protein Interaction PredictionYongxin Zhu, Jianxin Wang, Shiyue He, et al.
Genome Biology|May 2, 2026
EvoRMD: integrating biological context and evolutionary RNA language models for interpretable prediction of RNA modificationsBo Wang, Hao Zhang, Taoyong Cui, et al.
Plos One|November 21, 2012
An integrative computational framework based on a two-step random forest algorithm improves prediction of zinc-binding sites in proteinsCheng Zheng, Mingjun Wang, Kazuhiro Takemoto, et al.
Briefings in Bioinformatics|November 23, 2020
Computational identification of eukaryotic promoters based on cascaded deep capsule neural networksYan Zhu, Fuyi Li, Dongxu Xiang, et al.
Briefings in Bioinformatics|November 2, 2018
Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithmsLeyi Wei, Jie Hu, Fuyi Li, et al.
Briefings in Bioinformatics|March 14, 2014
Towards more accurate prediction of protein folding rates: a review of the existing Web-based bioinformatics approachesCatherine Ching Han Chang, Beng Ti Tey, Jiangning Song, et al.
Pageof 33