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Pathogenicity classification of missense mutations based on deep generative model

COMPUTERS IN BIOLOGY AND MEDICINE 2024年 170卷 107980-107980页

作者： Bai, Ke Yang, Lu Xue, Jian Zhao, Lin Hao, Fanchang Shandong Jianzhu Univ Jinan 250101 Peoples R China

Missense mutations affect the function of human proteins and are closely associated with multiple acute and chronic diseases. The identification of disease-associated missense mutations and their classification for pathogenicity can provide insights into the genetic basis of disease and protein function. This paper proposes MLAE (Method based on LSTM-Ladder autoencoder), a deep learning classification model for identifying disease-associated missense mutations and classifying their pathogenicity based on the variational autoencoder (VAE) framework. MLAE overcomes the limitations of the VAE framework by introducing the Ladder structure, combined with LSTM networks. This reduces the loss of original information during the transmission process, thereby making the model more effective in learning. In the experiment, MLAE classified all 27572 possible missense variants of the three input proteins with an average classification AUC of 0.941. This result provides evidence that MLAE is effective in predicting pathogenicity. Additionally, MLAE provides results for multi-label classification, with an average Hamming loss of 0.196, supporting the classification of complex variants. The proposed MLAE method provides an insightful approach to effectively capture amino acid sequence information and accurately predict the pathogenicity of mutations, thereby providing an analytical basis for the study and prevention of related diseases.

关键词： Single amino acid variation Deep generation model variational autoencoder Pathogenicity classification Multiple label classification

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Neural networks based domain name generation

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2021年 61卷

作者： Wang, Zheng Guo, Yang NIST Gaithersburg MD 20899 USA

Domain generation algorithm (DGA) is used by botnets to build a stealthy command and control (C&C) communication channel between the C&C server and the bots. A DGA can periodically produce a large number of pseudo-random algorithmically generated domains (AGDs), a few of which direct the bots to the C&C server. AGD detection algorithms provide a lightweight, promising solution in response to the existing DGA techniques. In the constantly evolving attacker-defender game, attackers may seek more advanced DGA techniques to gain a better chance of evading detection by defenders. In this paper, we propose a new DGA, namely a neural networks-based domain name generation (NDG) architecture. NDG is based on a variational autoencoder (VAE), where the encoder and decoder networks use stacked gated convolutional neural networks (GCNNs) to learn the contextual structure hierarchically. NDG is experimentally validated using a set of state-of-the-art AGD detection algorithms. The existing DGAs of different classes following a DGA taxonomy are used to benchmark NDG. NDG shows the best overall anti-detection performance among all tested DGAs. We also demonstrate that NDG is effective in benchmarking AGD detection algorithms.

关键词： Domain generation algorithm Malicious domain name Classification variational autoencoder Deep learning

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Demonstrating the consequences of learning missingness patterns in early warning systems for preventative health care: A novel simulation and solution

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JOURNAL OF BIOMEDICAL INFORMATICS 2020年 110卷 103528-103528页

作者： Gillies, Christopher E. Taylor, Daniel F. Cummings, Brandon C. Ansari, Sardar Islim, Fadi Kronick, Steven L. Medlin, Richard P., Jr. Ward, Kevin R. Dept Emergency Med Canton MI USA Sch Nursing Canton MI USA Michigan Dialysis Serv Canton MI USA Dept Biomed Engn Ann Arbor MI USA Michigan Ctr Integrat Res Crit Care MCIRCC Ann Arbor MI USA Univ Michigan Michigan Inst Data Sci MIDAS Ann Arbor MI 48109 USA

When using tree-based methods to develop predictive analytics and early warning systems for preventive healthcare, it is important to use an appropriate imputation method to prevent learning the missingness pattern. To demonstrate this, we developed a novel simulation that generated synthetic electronic health record data using a variational autoencoder with a custom loss function, which took into account the high missing rate of electronic health data. We showed that when tree-based methods learn missingness patterns (correlated with adverse events) in electronic health record data, this leads to decreased performance if the system is used in a new setting that has different missingness patterns. Performance is worst in this scenario when the missing rate between those with and without an adverse event is the greatest. We found that randomized and Bayesian regression imputation methods mitigate the issue of learning the missingness pattern for tree-based methods. We used this information to build a novel early warning system for predicting patient deterioration in general wards and telemetry units: PICTURE (Predicting Intensive Care Transfers and other UnfoReseen Events). To develop, tune, and test PICTURE, we used labs and vital signs from electronic health records of adult patients over four years (n = 133,089 encounters). We analyzed primary outcomes of unplanned intensive care unit transfer, emergency vasoactive medication administration, cardiac arrest, and death. We compared PICTURE with existing early warning systems and logistic regression at multiple levels of granularity. When analyzing PICTURE on the testing set using all observations within a hospital encounter (event rate =3.4%), PICTURE had an area under the receiver operating characteristic curve (AUROC) of 0.83 and an adjusted (event rate = 4%) area under the precision-recall curve (AUPR) of 0.27, while the next best tested method-regularized logistic regression-had an AUROC of 0.80 and an adjusted

关键词： Tree-based methods Patient deterioration Early warning systems Simulation variational autoencoder Machine learning

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Spatio-Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: autoencoder and Generative Adversarial Networks

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JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 2022年第3期14卷

作者： Cheng, Meiling Fang, Fangxin Navon, Ionel M. Zheng, Jie Tang, Xiao Zhu, Jiang Pain, Christopher Imperial Coll London Dept Earth Sci & Engn Appl Modelling & Computat Grp London England Florida State Univ Dept Sci Comp Tallahassee FL 32306 USA Chinese Acad Sci Ctr Excellence Reg Atmospher Environm Inst Urban Environm Xiamen Peoples R China Chinese Acad Sci Int Ctr Climate & Environm Sci Inst Atmospher Phys Beijing Peoples R China

Efficient and accurate real-time forecasting of national spatial ozone distribution is critical to the provision of effective early warning. Traditional numerical air quality models require a high computational cost associated with running large-scale numerical simulations. In this work, we introduce a hybrid model (VAE-GAN) combining a generative adversarial network (GAN) with a variational autoencoder (VAE) to learn the dynamic ozone distributions in spatial and temporal spaces. The VAE-GAN model can not only decipher the complex nonlinear relationship between the inputs (the past states/ozone and meteorological factors) and outputs (ozone), but also provide ozone forecasts for a long lead-time beyond the training period. The performance of VAE-GAN is demonstrated in hourly and daily spatio-temporal ozone forecasts over China. The training datasets from 2013 to 2017 and validation datasets from 2018 to 2019 are the collection of data from the air quality reanalysis datasets. With the use of VAE, large dataset sizes are decreased by three orders of magnitude, enabling hourly and daily forecasts to be computed in seconds. Results show that the VAE-GAN achieves a reasonable accuracy in the prediction of both the spatial and temporal evolution patterns of hourly and daily ozone fields, as compared to the Nested Air Quality Prediction Modeling System (commonly used in China), the reanalysis data and observations during the validation period. Thus, the VAE-GAN is a cost-effective tool for large data-driven predictions, which can potentially reinforce air pollution prediction efforts in providing risk assessment and management in a timely manner.

关键词： ozone forecasting spatio-temporal generative adversarial network variational autoencoder deep learning

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Learning disentangled skills for hierarchical reinforcement learning through trajectory autoencoder with weak labels

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期230卷

作者： Song, Wonil Jeon, Sangryul Choi, Hyesong Sohn, Kwanghoon Min, Dongbo Yonsei Univ Sch Elect & Elect Engn Seoul South Korea Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Ewha Womans Univ Dept Comp Sci & Engn Seoul 03760 South Korea Korea Inst Sci & Technol KIST Seoul 02792 South Korea

Typically, hierarchical reinforcement learning (RL) requires skills that are applicable to various downstream tasks. Although several recent studies have proposed the supervised and unsupervised learning of such skills, the learned skills are often entangled, which hinders their interpretation. To alleviate this, we propose a novel method to use weak labels for learning disentangled skills from the continuous latent representations of trajectories. To this end, we extended a trajectory variational autoencoder (VAE) to impose an inductive bias using weak labels, which explicitly enforces the disentangling of the trajectory representations into factors of interest intended for the model to learn. Using the latent representations as skills, a skill-based policy network is trained to generate trajectories similar to the learned decoder of the trajectory VAE. Furthermore, using the disentangled skill, we propose a skill repetition that can expand the entire trajectories generated by the policy at test time, resulting in an effective planning strategy. Experiments were performed on several challenging navigation tasks in mazes, and the results demonstrate the effectiveness of our method at solving hierarchical RL problems even with a long horizon and sparse rewards.

关键词： Deep reinforcement learning Hierarchical reinforcement learning Skill learning variational autoencoder Disentangled representation Weak label Planning

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VAE-GAN3D: Leveraging image-based semantics for 3D zero-shot recognition

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IMAGE AND VISION COMPUTING 2024年 147卷

作者： Abdullah, Md Tahmeed Rahman, Sejuti Rahman, Shafin Islam, Md Fokhrul Univ Dhaka Dept Robot & Mechatron Engn Dhaka 1000 Bangladesh North South Univ Dept Elect & Comp Engn Dhaka 1229 Bangladesh

The current state of 3D zero-shot recognition falls short in performance when compared to its counterpart in 2D images. A major challenge is the absence of a robust feature extractor for 3D point cloud data. To overcome this challenge, a large dataset needs to be collected, processed, and fed into a deep-learning model capable of generating distinguishable features after training. To this end, we propose VAE-GAN3D, a model that uses a combination of a variational autoencoder (VAE) and a generative adversarial network (GAN) to supplement the small amount of data available for training with synthetic examples of classes. This allows for the underlying data distribution of large and complex data to be learned by the deep VAE network. When combined with a GAN, this network generates synthetic features that exhibit consistency in both seen and unseen classes. Furthermore, we notice that for tasks involving a small domain of classes, the existing text features do not contribute significantly to zero-shot learning. Therefore, we introduce image representation-based semantic features of classes, which improve the performance of zero-shot recognition for 3D objects. We assess the proposed model using three different datasets and present a technique for partitioning the RGB-D object dataset, which contains real -world objects, into seen and unseen classes. Our approach shows promising results in addressing the challenges of 3D zero-shot recognition and presents a novel solution for improving the accuracy of 3D point cloud recognition.

关键词： Zero-shot learning 3D object recognition Deep learning Generative adversarial network variational autoencoder

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IF-TONIR: Iteration-free Topology Optimization based on Implicit Neural Representations

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COMPUTER-AIDED DESIGN 2024年 167卷

作者： Hu, Jiangbei He, Ying Xu, Baixin Wang, Shengfa Lei, Na Luo, Zhongxuan Jiangbei Ying Nanyang Technol Univ Singapore Singapore Dalian Univ Technol Dalian Peoples R China

Topology optimization holds great significance as a research topic in the field of mechanical engineering, aiming to design and optimize structures to achieve desired performance while adhering to specific constraints. However, its high computational complexity and iterative optimization process severely impact the efficiency, which presents substantial obstacles to its practical applications. To tackle this challenge, recent research is dedicated to the advancement of iteration-free topology optimization methods that leverage neural networks and deep learning, aiming to directly predict optimal structures through optimization problem configurations. In this paper, we propose IF-TONIR, a novel data-driven topology optimization method that utilizes implicit neural representations. Our approach employs signed distance fields to represent structures, offering compact and smooth representations that effectively eliminate the checkerboard phenomenon commonly observed in density-based methods. IF-TONIR leverages Conditional variational autoencoders, which use a CNN-based encoder and a MLP-based decoder to learn and reconstruct optimal structures. We employ the features extracted from physical information as conditions to guide the decoder in generating optimal structures that adhere to specific design domain shapes and boundary conditions. Furthermore, we propose the integration of a topological loss based on persistent homology to train the model. This loss function effectively penalizes the existence of structural disconnections in the reconstructed output, thereby enhancing the overall physical reliability of the generated structures. Various experiments have demonstrated that our iteration-free topology optimization method based on implicit representations can accurately identify regions of high strain energy and generate continuous structures with low compliance. The methods also holds the theoretical capability of outputting optimal structures at any desired resolut

关键词： Topology optimization Implicit neural representations variational autoencoder Persistent homology Topological loss

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Robust errant beam prognostics with conditional modeling for particle accelerators

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2024年第1期5卷 015044页

作者： Rajput, Kishansingh Schram, Malachi Blokland, Willem Alanazi, Yasir Ramuhalli, Pradeep Zhukov, Alexander Peters, Charles Vilalta, Ricardo Thomas Jefferson Natl Accelerator Facil Newport News VA 23606 USA Oak Ridge Natl Lab Oak Ridge 37830 TN USA Univ Houston Dept Comp Sci Houston TX 77204 USA

Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault and abort operations for numerous reasons, lowering efficiency and science output. To avoid these faults, we apply anomaly detection techniques to predict unusual behavior and perform preemptive actions to improve the total availability. Supervised machine learning (ML) techniques such as siamese neural network models can outperform the often-used unsupervised or semi-supervised approaches for anomaly detection by leveraging the label information. One of the challenges specific to anomaly detection for particle accelerators is the data's variability due to accelerator configuration changes within a production run of several months. ML models fail at providing accurate predictions when data changes due to changes in the configuration. To address this challenge, we include the configuration settings into our models and training to improve the results. Beam configurations are used as a conditional input for the model to learn any cross-correlation between the data from different conditions and retain its performance. We employ conditional siamese neural network (CSNN) models and conditional variational auto encoder (CVAE) models to predict errant beam pulses at the spallation neutron source under different system configurations and compare their performance. We demonstrate that CSNNs outperform CVAEs in our application.

关键词： anomaly prediction siamese model variational autoencoder conditional SNS spallational neutron source particle accelerators

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GenSMILES: An enhanced validity conscious representation for inverse design of molecules

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KNOWLEDGE-BASED SYSTEMS 2023年 268卷

作者： Bhadwal, Arun Singh Kumar, Kamal Kumar, Neeraj Natl Inst Technol Srinagar 246174 Uttaranchal India Thapar Inst Engn & Technol Patiala 147004 Punjab India Lebanese Amer Univ Dept Elect & Comp Engn Beirut 11022801 Lebanon King Abdulaziz Univ Fac Comp & IT Jeddah 22233 Saudi Arabia Univ Petr & Energy Studies Sch Comp Sci Dehra Dun 248007 Uttaranchal India Chandigarh Univ Dept Comp Sci & Engn Gharuan Mohali 140413 Punjab India

Deep neural networks have become increasingly important in recent years for creating molecules with desirable properties. In general, SMILES strings are used to train deep neural network based models. The trained model is then used to generate the desired molecules. Unfortunately, due to syntactical and semantic flaws in the representation, the SMILES string generates a substantial number of invalid molecules. SMILES representation fails to efficiently handle rings, branch and bonds between atoms. Lack of robustness in dealing with the cited aspects results in abundance of invalid strings. To overcome this limitation, this paper proposes a SMILES like representation, called GenSMILES. GenSMILES tackles syntactical and semantic issues by relying upon derivative rules to apply constraints. This causes a generative model produces more valid SMILES strings. By substituting a single notation for the pair representation of branches and rings in SMILES with a ) and boolean AND, respectively, GenSMILES corrects the syntactical issues. The mismatching of atom's bonds is the main cause of semantic errors. GenSMILES addresses such issues by employing derivation rules during string conversion from GenSMILES to SMILES. Every SMILES string can be represented with an equivalent GenSMILES. When used for designing drug molecule, GenSMILES increases molecule's validity when compared with SMILES and DeepSMILES on two popular architectures i.e., Recurrent Neural Network and variational autoencoder. The main benefit of GenSMILES is that it can be applied directly to generative algorithms without adapting the model environment. GenSMILES is beneficial not only to generative approaches of DL but also to the approaches that use SMILES string-like representation. On most of the datasets, GenSMILES is effective in improving validity above 90% and diversity score 15. GenSMILES results in more diversity in the properties of generated molecules and allows exploration of larger portion of undisc

关键词： SMILES GenSMILES DeepSMILES Deep learning Recurrent neural network variational autoencoder

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Domain Adaptation for Synthesis of Hazy Images

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Journal of Computer and Communications 2021年第10期9卷 142-151页

作者： Haoying Sun Yutong Zheng Qing Lang School of Information Engineering Minzu University of China Beijing China

Most existing image dehazing methods based learning are less able to perform well to real hazy images. An important reason is that they are trained on synthetic hazy images whose distribution is different from real hazy images. To relieve this issue, this paper proposes a new hazy scene generation model based on domain adaptation, which uses a variational autoencoder to encode the synthetic hazy image pairs and the real hazy images into the latent space to adapt. The synthetic hazy image pairs guide the model to learn the mapping of clear images to hazy images, the real hazy images are used to adapt the synthetic hazy images’ latent space to real hazy images through generative adversarial loss, so as to make the generative hazy images’ distribution as close to the real hazy images’ distribution as possible. By comparing the results of the model with traditional physical scattering models and Adobe Lightroom CC software, the hazy images generated in this paper is more realistic. Our end-to-end domain adaptation model is also very convenient to synthesize hazy images without depth map. Using traditional method to dehaze the synthetic hazy images generated by this paper, both SSIM and PSNR have been improved, proved that the effectiveness of our method. The non-reference haze density evaluation algorithm and other quantitative evaluation also illustrate the advantages of our method in synthetic hazy images.

关键词： Haze Scene Domain Adaptation Generative Adversarial Net variational autoencoder Deep Learning

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