Research on predicting 2D-HP protein folding using reinforcement learning with full state space

Wu, Hongjie; Yang, Ru; Fu, Qiming; Chen, Jianping; Lu, Weizhong; Li, Haiou

doi:10.1186/s12859-019-3259-6

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…Cao and Lu ( Lu et al, 2021 ) avoided loss of information due to truncation by introducing a fag vector and used a variable length dynamic two-way gated cyclic unit model to predict protein. Yang ( Wu et al, 2019 ) designed a reward function to model the protein input under full-state reinforcement learning. Wu and Huang ( Wu et al, 2019 ) et al used random forests to build their own model and used binary reordering to make their predictions more efficient.…”

Section: Introductionmentioning

confidence: 99%

Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks

Shen

et al. 2022

Front. Genet.

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Membrane proteins are an essential part of the body’s ability to maintain normal life activities. Further research into membrane proteins, which are present in all aspects of life science research, will help to advance the development of cells and drugs. The current methods for predicting proteins are usually based on machine learning, but further improvements in prediction effectiveness and accuracy are needed. In this paper, we propose a dynamic deep network architecture based on lifelong learning in order to use computers to classify membrane proteins more effectively. The model extends the application area of lifelong learning and provides new ideas for multiple classification problems in bioinformatics. To demonstrate the performance of our model, we conducted experiments on top of two datasets and compared them with other classification methods. The results show that our model achieves high accuracy (95.3 and 93.5%) on benchmark datasets and is more effective compared to other methods.

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Section: Introductionmentioning

confidence: 99%

Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks

Shen

et al. 2022

Front. Genet.

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“…Because once there is a conflict, the episode will end immediately and the agent will receive a bad reward. However, current reinforcement learning based research still suffers from low long-term prediction accuracy and cannot fold sequences well when the length is larger than 30 [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Neural‐augmented two‐stage Monte Carlo tree search with over‐sampling for protein folding in HP Model

Deng

Yuan

Tian

et al. 2022

IEEJ Transactions Elec Engng

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This paper proposes a novel Monte Carlo tree search (MCTS) algorithm to solve the protein folding problem in HP model. There are two main challenges. First, the problem is proved to be NP‐complete. The solution space is large and it is hard to find a good solution via a search algorithm without prior knowledge of the HP model. We tackle this challenge by formulating the problem as a deterministic Markov decision process (MDP) and solve it in an AlphaZero's manner. The difference is that we design a MCTS algorithm with two stages: neural exploitation stage and random exploration stage. In the first stage, the search algorithm utilizes the knowledge from previous experience by evaluating the states with a trained neural network, while in the second stage, the states are evaluated by fast and random rollouts. It effectively reduces the number of neural inferences and computational cost. The second challenge is that the evaluation of typical MCTS cannot preserve the correct preference over the actions in our task. To address this challenge, we propose an over‐sampling mechanism that encourages the agent to search more on those actions with high rollout values. The proposed method is tested and compared in a series of experiments. Experimental results have confirmed the effectiveness of the proposed method empirically. Besides, we also visualize the obtained the best conformations and verify the proposed technical designs through an ablation study. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Research on RNA secondary structure predicting via bidirectional recurrent neural network

Cao

et al. 2021

BMC Bioinformatics

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Background RNA secondary structure prediction is an important research content in the field of biological information. Predicting RNA secondary structure with pseudoknots has been proved to be an NP-hard problem. Traditional machine learning methods can not effectively apply protein sequence information with different sequence lengths to the prediction process due to the constraint of the self model when predicting the RNA secondary structure. In addition, there is a large difference between the number of paired bases and the number of unpaired bases in the RNA sequences, which means the problem of positive and negative sample imbalance is easy to make the model fall into a local optimum. To solve the above problems, this paper proposes a variable-length dynamic bidirectional Gated Recurrent Unit(VLDB GRU) model. The model can accept sequences with different lengths through the introduction of flag vector. The model can also make full use of the base information before and after the predicted base and can avoid losing part of the information due to truncation. Introducing a weight vector to predict the RNA training set by dynamically adjusting each base loss function solves the problem of balanced sample imbalance. Results The algorithm proposed in this paper is compared with the existing algorithms on five representative subsets of the data set RNA STRAND. The experimental results show that the accuracy and Matthews correlation coefficient of the method are improved by 4.7% and 11.4%, respectively. Conclusions The flag vector introduced allows the model to effectively use the information before and after the protein sequence; the introduced weight vector solves the problem of unbalanced sample balance. Compared with other algorithms, the LVDB GRU algorithm proposed in this paper has the best detection results.

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Research on predicting 2D-HP protein folding using reinforcement learning with full state space

Cited by 3 publications

References 32 publications

Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks

Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks

Neural‐augmented two‐stage Monte Carlo tree search with over‐sampling for protein folding in HP Model

Research on RNA secondary structure predicting via bidirectional recurrent neural network

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