reinforcement learning in bioinformatics

Besides, the hydrogen-bond network is also an important point that should be carefully attended to in sequence optimization procedures [104]. Despite the significant achievements [2224], these conventional approaches are mainly knowledge based, relying on physical principles and statistical rules [25]. The root-mean-square deviation score of their GAN method has 44% improvement compared to other tools, and their GAN method obtains the smallest standard deviation compared to other tools, which show the stability of their prediction. The Author(s) 2022. After each action, the state can change. Deep learning (DL) has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex The future perspectives on design goals, challenges and opportunities are also comprehensively discussed. In this work, we explore the potential of deep learning to streamline the process of identifying new potential drugs through the computational generation of molecules with manifest strikingly excellent properties compared with man-made machines, including extremely high efficiency, economy and precision in operation, self-assembly upon synthesis and so on. In addition to circumstantial improvement of protein design through advances in structure prediction, customized deep learning approaches also made considerable contributions to protein design directly nowadays. In comparison to traditional knowledge-based energy functions that are typically combinations of statistical and empirical potential terms [21, 97, 108], deep learning models could provide a more general and more accurate description of the multidimensional potential functions in the real world. , et al. An illustration of two inverse processes, i.e. Using deep reinforcement learning to speed up collective cell Reinforcement learning can be applied in collective cell migration (Hou etal., 2019), DNA fragment assembly (Bocicor etal., 2012), and characterizing cell movement (Wang etal., 2018). In: Rao R, Bhattacharya N, Thomas N, et al.. Due to the strictly limited working environment and relatively short operation life, native proteins, however, cannot meet the surging demands of human beings satisfactorily. In addition, these breakthroughs in protein design also expand our exploration and understanding of protein sequence, structure and function spaces. Attention mechanisms can potentially be used in a wide range of biosequence analysis problems, such as RNA sequence analysis and prediction (Park etal., 2017), protein structure and function prediction from amino acid sequences (Zou etal., 2018), and identification of enhancerpromoter interactions (EPIs) (Hong etal., 2020). Deep generative models, such as variational autoencoders (VAEs) (Doersch, 2016), are powerful networks for information derivation using unsupervised learning, which has achieved remarkable success in recent years. A Robust Machine Learning Framework Built Upon Molecular Representations Predicts CYP450 Inhibition: Toward Precision in Drug Repurposing. Reinforcement learning ( RL) refers to "learning by interacting with an environment". In: Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space. Concrete and representative examples of using deep learning in bioinformatics. , et al. For example, DyNA PPO [132] was such a deep reinforcement learning model based on proximal-policy optimization [133] for sequence design. , Huang C. GANs also played an important role in direct protein sequence generation. , Silver D. Statistical sampling methods, exemplified by Monte Carlo simulations, have been used to solve this dilemma and could achieve acceptable approximations in practice [99]. First, the interpretability of model is essential to biologists to understand how model helps solve the biological problem, e.g. Basically, the task of de novo protein design is to find new sequences targeted for desired functions. , et al. , Kavukcuoglu K. Similarly, information of protein sequencestructure relationships stored in billions of parameters in the powerful protein structure prediction networks could also be utilized inversely to generate new sequences and structures [91]. inter-residue distances and orientations) is designed at the first step. Subsequent in vitro synthesis showed that these protein hallucinations were monomeric and stable, possessing designed structural elements. Multiple Sequence Alignment problem simply refers to the process of arranging initial sequences of DNA, RNA or proteins in order to maximize their regions of similarity. Thus, in principle, directly mapping the spaces of protein sequence and function seems to be advantageous over design procedures that need predetermined structural topologies as intermedia. Learning For example, deep convolutional GAN (dcGAN) [85] was chosen to learn a mapping from a low-dimensional standard normal distribution z to an unknown high-dimensional probability distribution in the space of protein inter-residue distance map with a fixed size [82]. amino acid embeddings, could be optimized and the representation of a protein sequence with its fundamental features could be inferred in a latent space. Since different researches of representation learning generally use self-built datasets and have no unified evaluation process or standard, it is difficult for people to compare them and consider the accuracy and efficiency, advantages and disadvantages of each [125]. Highly accurate protein structure prediction with AlphaFold WebIn this project, we performed a survey of reinforcement learning techniques and implementedvarious methods relevant to dynamical systems. conceived the study; H.L., S.T., and Y.L. An illustration of protein representation learning, direct protein sequence design and related downstream protein analysis applications. Learning context-aware structural representations to predict antigen Although the data mining ability of RNN architecture used by UniRep might be inferior compared with current popular ones in the field of nature language processing like transformer, the basic conceptions it came up with and the impressive extensions it showed still influenced following researches a lot. Information from unimproved variants is discarded. In the bioinformatics field, symbolic reasoning is applied and evaluated on structured biological knowledge, which can be used for data integration, retrieval, and federated queries in the knowledge graph (Alshahrani etal., 2017). In: Yang J, Anishchenko I, Park H, et al., Alley EC, Khimulya G, Biswas S, et al., Biswas S, Khimulya G, Alley EC, et al.. This work has been supported by the National Natural Science Foundation of China (#32171243), the Beijing Advanced Innovation Center for Structural Biology, the Japan Society for the Promotion of Science (JSPS) KAKENHI (19H03213 and 18H0298) and the Startup Foundation for Introducing Talent of NUIST. Existing approaches usually employ a flat policy structure that treat all symptoms and diseases equally for action making. In this article, we reviewed some selected modern and principled DL methodologies, some of which have recently been applied to bioinformatics, while others have not yet been applied. Although similar in general, the learning process of deep neural network differs from conventional energy minimization in several ways. , et al. Using deep reinforcement learning to speed up - BMC Although there is a large amount of data in the bioinformatics field (Li etal., 2019), data scarcity still occurs in biology and biomedicine. reinforcement learning Then, compatible peptide fragments are picked under the evaluation by sequence-independent energy functions and several sequence-structure optimization iterations are executed. DL is a relatively new field compared to traditional ML, and the application of DL in bioinformatics is an even newer field. He focuses on protein bioinformatics and molecular dynamics simulations. As we searched, one-shot learning has been used to significantly lower the quantity of data required and achieves precise predictions in drug discovery (Altae-Tran etal., 2017). With the application of more advanced technologies, these methods can help us excavate more intrinsic principles of proteins and get more high-quality functional protein materials. Other deep-learning-based methods constructed their networks with various architectures including auto-encoders [105], 3D convolutional neural networks [106], DenseNets [107] and GANs [84] to predict sequence probability profiles from a given backbone structure. Scoring functions in the Rosetta program range from statistical potentials established using Bayesian methods [111] to complicated modern force fields [112]. Reinforcement learning is a ML has been the main contributor to the recent resurgence of artificial intelligence. (, Mnih V. Many impressive achievements have been made through protein design over the past decade, which intensively impacted and promoted synthetic biology in both academia and industry. protein structure prediction (upper) and structure-based protein design (lower). For instance, the drug discovery problem is to optimize the candidate molecule that can modulate essential pathways to achieve therapeutic activity by finding analogue molecules with increased pharmaceutical activity. Predicting potential microbe-disease associations based on multi-source features and deep learning. difference between current order ranked by the network and the supposed one. The results of applying one-shot models to a number of assay collections show strong performance compared to other methods, such as random forest and graph CNNs. Moreover, two fundamental breakthroughs have tremendously increased the applicability of ANN techniques: convolutional neural networks (CNNs) for imaging data and recurrent neural networks (RNNs) for natural language data, which will be introduced in the Supplementary material with other well-known architectures. Radford A, Jozefowicz R, Sutskever I. , Czibula G. Reinforcement Learning - an overview | ScienceDirect Topics This review covers all aspects of retrosynthesis, including datasets, models and Note that a key distinguishing feature is that users do not have to predefine all the states, and a model can be trained in an end-to-end manner, which has become an increasingly active research field with numerous algorithms being developed. Few-shot learning is suitable for many problems in bioinformatics that have limited data, such as protein function prediction (Li etal., 2017a) and drug discovery (Joslin etal., 2018). Basically, deep reinforcement learning divides the world into two parts, an environment and an agent. Protein design approaches based on deep reinforcement learning are just like in silico simulations of natural protein synthesis processes (Figure 5). Some summaries have been articulated in the last two sections since this step has a close relationship with previous steps and many researches integrate them all together. Although its superiority has been shown in the large-scale benchmarking across several methods, the report of DyNA PPO did not exhibit any verification through wet lab experiments. This reinforcement learning model shows less computational complexity and unnecessary external supervision in the learning process compared with the genetic algorithm and supervised approach, respectively. In brief, meta learning outputs an ML model that can learn quickly. Bocicor etal. Compared with the classical molecular mechanics force fields with great complexity and cost, this data-driven method only needed a few hours for training, which exhibited its practical applicability and huge potentiality. Brief summary of recent researches focused on direct protein sequence design. Rau M, Renaud N, Xue LC, et al. DeepRank-GNN: a graph neural network framework to learn patterns in protein-protein interfaces. In this case, standard DL algorithms cannot work because one needs numerous data for each class to train a generalizable DL model (Li etal., 2018). Deterministic approaches could solve the fitness problem accurately for small backbones [98] but become powerless for large ones due to the exponential increase of computational complexity. By uniformly interpolating the latent space, the model successfully generated 20 thousand protein sequences exhibiting sequence properties highly correlated with the latent dimensions, which supported its ability to capture the intrinsic features of native sequences and their inter-relationships. GANs are used as an inpainting tool to repair the inter-residue distance map for a corrupted protein structure. A reinforcement-learning-based approach to enhance exhaustive protein loop sampling Amlie Barozet, Kevin Molloy, Marc Vaisset, Thierry Simon, Juan Corts , et al. Through supervised or unsupervised training, a dictionary of word vectors, i.e. Although these two frameworks have a large intersection, the VAE architecture could be trained for some models that GANs could not and vice versa. DyNA PPO balanced the tradeoff in reward estimation by using a bunch of models to learn different aspects of the sequence fitness landscape but only using the most suitable one with sufficient accuracy to update its policy. His research focuses on bioinformatics and machine learning. The earlier protein design approaches such as directed evolution [11, 12] and the following rational engineering [13, 14] mainly focus on the imitation and/or acceleration of natural evolutionary processes. GANs were used to generate protein inter-residue distance maps for the completion of corrupted structures [8284]. These successes deepened our understanding of the sequencestructure relationship for proteins, which is also the foundation of structure-based design, and provided a bunch of practical tools that could be directly used in design problems. The adoption of more advanced and lightweight network architectures as well as knowledge distillation [147] and network pruning [148] may partially handle this dilemma. Hierarchical reinforcement learning for automatic disease diagnosis | Bioinformatics | Oxford Academic AbstractMotivation. Deep reinforcement learning of cell movement in the early , Lin S.-C. Bioinformatics, 20(7):1178--1190, 2004. Here are also some problems in the bioinformatics field as follows, which need to be tackled. Unlike other works in this topic that mainly used computational metrics to validate the accuracy and quality of their designs, in vitro validations of ProteinSolver by circular dichroism experiments testified its capability to fit protein sequences. Introduction In recent years, deep learning has met with great success in machine learning tasks such as Taking sequence space as an example, since all native protein sequences originated from a few ancient accidental events and gradually evolved with haphazard mutation and oriented selective pressure, they exist in the sequence space in the form of sprinkling clusters called protein families instead of even dispersion. , Pappu A.S. , Min S. , Ding L. Different from protein fitness landscape searching for a given backbone, direct sequence design learns a meaningful distribution of sequence representation in a latent space and generates sequences in real space according to speculative representations derived from the learned distribution (Figure 4). One possible solution to overcome these obstacles is the representation learning using protein language models. Meanwhile, a distinct approach introduces deep ranking networks called RankNet and LambdaRank [113] in recommendation systems for candidate rating [61]. This lack of interpretability has limited their applications, particularly when their performance did not stand out among other more interpretable ML methods, such as linear regression, logistic regression, support vector machines, and decision trees. Therefore, in combination with downstream generative models or methods, proteins of desired functions but with unseen sequences could be generated in a high throughput manner. The second one is the lack of method generalization, since most domain-specific deep learning methods have not sufficiently exploited the fundamental features of protein sequences and thus are hard to be transferred from one problem to another through simple fine-tuning. ], Alshahrani M. Doersch C. Tutorial on variational autoencoders. Hierarchical reinforcement learning for automatic disease diagnosis In vitro experimental data, especially the high-resolution crystal structures of two designed TM-barrel proteins, validated the design capability of this network and corresponding structural agreements. This substitution would be accepted only if the KullbackLeibler divergence between distance distributions of the new sequence and corresponding background satisfied the Metropolis criterion. Introduction of Reinforcement Learning in Due to the limitation of small biological data, it is challenging to form accurate predictions for novel compounds. For full access to this pdf, sign in to an existing account, or purchase an annual subscription. It is noteworthy that ProteinSolver was only trained and tested with the constraints derived from existing proteins, and thus, its ability to sample reasonable sequences of novel proteins still needs further validation. It utilized another self-supervised strategy, masking language modeling objective, for its training. Deep reinforcement learning combines , Xu D. Although deep learning has shown huge success in many sub-fields of protein bioinformatics, there are still two major obstacles impeding its further development. backbone generation, sequence fitness and candidate scoring, exemplified by Top 7 [20], the first globular protein that was designed without natural homologs, as well as other famous related works. We surveyed the literature and tabulated the number of publications in log-scale for 14 commonly studied biological topics appearing together with RNN, CNN, or deep learning according to PubMed, which are detailed in Figure1. (, Hamilton W. Besides, deep learning also sheds a light on the direct protein sequence design for specific functions or properties without the medium of structures. However, the last decade has witnessed the rapid development of DL with thrillingly promising power to mine complex relationships hidden in large-scale biological and biomedical data. Deep-reinforcement-learning-based protein design is analogous to natural protein synthesis process. In recent years, reinforcement learning (RL) has acquired a prominent position in the space of health-related sequential decision-making, becoming an increasingly With gradients backpropagated from the predefined structures to input protein sequences through the trRosetta network [32], sequences and structures could be optimized simultaneously [110]. Iterative refinement LSTMs can generalize to new experimental assays related but not identical to assays in the training collection, and graph convolutional networks are useful for transforming small molecules into continuous vectorial representations. Thus, meta learning can be used in B-cell conformational epitope prediction in continuously evolving viruses, which is useful for vaccine design. Furthermore, with the rapid accumulation of protein sequence data and the usage of network architectures with higher complexity and capability, the future versions of ESM-1b were expected to have additional improvements in protein sequence representation. Furthermore, attention networks with the most advanced end-to-end training procedure developed by Google DeepMind shocked the public in the 14th Critical Assessment of protein Structure Prediction (CASP) experiments by providing a wonderful solution for the structure prediction of single-domain proteins [6365]. Wenze Ding is currently a research scientist in School of Artificial Intelligence and School of Future Technology, NUIST, interested in structural bioinformatics and protein design. Deep neural networks originally trained for image recognition could be used to generate hallucinations with a transformed style [90]. Another important and imminent assignment of deep-learning-based protein design is promoting its application scope. Published by Oxford University Press. (, Li Y. , et al. WebThis study aims to perform generation of targeted molecules by training the recurrent neural network to learn the building rules of production of valid molecules in the form of SMILES strings and optimize it to produce molecules with Using few-shot learning algorithms, a model can be trained with reasonable performance on some difficult problems by utilizing only the existing limited data. PMID: 32321157 For example, under the enzyme commission (EC) classification (Li etal., 2017a), only one enzyme belongs to the class of phosphonate dehydrogenase (EC 1.20.1.1). Reinforcement Learning Method for BioAgents - IEEE Xplore Recent introduction of deep learning into design methods exhibits a transformative influence and is expected to represent a promising and exciting future direction. Reinforcement learning Although satisfactory outcomes have been achieved by these works, some limitations still exist. reinforcement-learning-based approach to enhance exhaustive In: Ashish V, Noam S, Niki P, et al. Attention is all you need. Generally, a specific folding topology with predefined secondary structural elements and/or geometric constraints (e.g. In: Ingraham J, Garg VK, Barzilay R, et al., Strokach A, Becerra D, Corbi-Verge C, et al.. However, inferring possible sequences for a predefined structure with desired function from the vast multidimensional sequence space termed as the protein fitness landscape [94] is extremely struggling and impossible to be handled with brute force, considering the countless permutations formed by the 20 usual proteinogenic amino acids [95]. Costello Z, Martin HG. , Choi H.-S. (, Park S. one-hot encoding for RNA, DNA, or protein sequences) into another representation of the sequence. His research interests include sequence analysis in molecular biology and bioinformatics. predicting DNAprotein binding (Luo etal., 2020). Learning to generate reviews and discovering sentiment. Arrows represent corresponding dataflow. This method has been tested on six cell lines, and the area under the receiver operating characteristic (AUROC) and area under the precision-recall curve (AUPR) values of EPIVAN are higher than those without the attention mechanism, which indicates that the attention mechanism is more concerned with cell line-specific features and can better capture the hidden information from the perspective of sequences.
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