G. Yao

University of Electronic Science and Technology of China

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Jinyan Li

Shenyang Agricultural University

Yahui Wang

PLA Information Engineering University

Bin Huang

Shenzhen University

Bo Yang

Jingdezhen University

Jian Zhang

Nanjing University

Wanruo Zhang

Peking University

Wenfeng Zheng

University of Electronic Science and Technology of China

Sijie Zhang

Dalian Maritime University

Shuhan Zhang

China University of Mining and Technology

Chao Liu

Université de Montpellier

Cooperative Institutions

University of Electronic Science and Technology of China

Nanjing University

Capital Medical University

Shenyang Agricultural University

Beijing Institute of Technology

Peking University

Beihang University

Collaborative Innovation Center of Advanced Microstructures

Jingdezhen University

Shanghai Jiao Tong University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Функция скоринга РНК для прогнозирования третичной структуры РНК на основе многослойных нейронных сетей

Молекулярная биология (2019)

Yahui Wang Jinyan Li Shuhan Zhang Bin Huang G. Yao

A good scoring function is necessary for ab inito prediction of RNA tertiary structures. In this study, we explored the power of a machine learning based approach as a scoring function. Compared with the traditional scoring functions, the present approach is more flexible in incorporating different kinds of features; it is also free of the difficult problem of choosing the reference state. Two multi-layer neural networks were constructed and trained. They took RNA a structural candidate as input and then output its likeness score that evaluates the likeness of the candidate to the native structure. The first network was working at the coarse-grained level of RNA structures, while the second at the all-atom level. We also built an RNA database and split it into the training, validation, and testing sets, containing 322, 70, and 70 RNAs, respectively. Each RNA was accompanied with 300 decoys generated by high-temperature molecular dynamics simulations. The networks were trained on the training set and then optimized with an early-stop strategy, based on the loss of the validation set. We then tested the performance of the networks on the testing set. The results were found to be consistently better than a recent knowledge-based all-atom potential.

Training set

10.1134/s0026898419010178

Cite

Citations (2)

Motion Prediction of Beating Heart Using Spatio-Temporal LSTM

IEEE Signal Processing Letters (2022)

Wanruo Zhang G. Yao Bo Yang Wenfeng Zheng Chao Liu

In robot-assisted cardiac surgery, predicting heart motion can help improve the operation accuracy and safety of surgical robots. Different from the conventional prediction schemes which model the point of interest (POI) with only temporal correlation of past observations, this paper proposes an LSTM-based method by exploiting the spatio-temporal correlation of the 3D movements of POI and auxiliary points (APs) on the same surface of the heart. Three different LSTM models are investigated. The first two models define the POI prediction as a pure time-series forecasting problem based on past POI trajectory, and the third model combines the past observations of POI and new observations of APs to take into consideration the extra spatial correlations for prediction. Experimental comparison studies based on 3D coordinates obtained from real stereo-endoscopic videos demonstrate the superior performance of the proposed spatio-temporal LSTM model.

Point of interest

10.1109/lsp.2022.3154317

Cite

Citations (17)

An RNA Scoring Function for Tertiary Structure Prediction Based on Multi-Layer Neural Networks

Molecular Biology (2019)

Yahui Wang Jinyan Li Sijie Zhang Bin Huang G. Yao

Training set

10.1134/s0026893319010175

Cite

Citations (4)