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Structure-based analyses of gut microbiome-related proteins by neural networks and molecular dynamics simulations

CURRENT OPINION IN STRUCTURAL BIOLOGY 2022年第0期73卷 102336-102336页

作者： Taneishi, Kei Tsuchiya, Yuko RIKEN Ctr Adv Photon 2-1 Hirosawa Wako Saitama 3510198 Japan Natl Inst Adv Ind Sci & Technol Artificial Intelligence Res Ctr Koto Ku 2-4-7 Aomi Tokyo 1350064 Japan

An imbalance in the gut microbiome is linked to immune disorders, such as autoimmune, allergic, and chronic inflammatory disorders. Elucidation of disease mechanisms is a matter of urgency. It requires precise elucidation of the structure-based mechanisms of protein interactions involved in disease onset. In addition, an understanding of the protein dynamics is vital because these fluctuations affect the function and interaction of disease-associated proteins. Experimental evaluation of not only protein interactions, functions, and structures but also the dynamics are time-consuming;therefore, computational predictions are necessary to elucidate disease mechanisms. Here, we introduce recent studies on structure-based analyses of proteins using computational approaches, particularly artificial intelligence (AI) and molecular dynamics (MD) simulations.

关键词： AI artificial intelligence AE autoencoder MD molecular dynamics PDB Protein Data Bank NMR nuclear magnetic resonance PSM peptide-spectrum match CNN convolutional neural network MSA multiple sequence alignment LSTM long short-term memory RNN recurrent neural network NLP natural language processing GCN graph convolutional network

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Multiscale aggregation and illumination-aware attention network for infrared and visible image fusion

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第10期36卷 e7712-e7712页

作者： Song, Wenhao Zhai, Wenzhe Gao, Mingliang Li, Qilei Chehri, Abdellah Jeon, Gwanggil Shandong Univ Technol Sch Elect & Elect Engn Zibo Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London England Royal Mil Coll Canada Dept Math & Comp Sci Kingston ON Canada Incheon Natl Univ Dept Embedded Syst Engn Incheon South Korea

Image fusion plays a significant role in computer vision since numerous applications benefit from the fusion results. The existing image fusion methods are incapable of perceiving the most discriminative regions under varying illumination circumstances and thus fail to emphasize the salient targets and ignore the abundant texture details of the infrared and visible images. To address this problem, a multiscale aggregation and illumination-aware attention network (MAIANet) is proposed for infrared and visible image fusion. Specifically, the MAIANet consists of four modules, namely multiscale feature extraction module, lightweight channel attention module, image reconstruction module, and illumination-aware module. The multiscale feature extraction module attempts to extract multiscale features in the images. The role of the lightweight channel attention module is to assign different weights to each channel so as to focus on the essential regions in the infrared and visible images. An illumination-aware module is employed to assess the probability distribution regarding the illumination factor. Meanwhile, an illumination perception loss is formulated by the illumination probabilities to enable the proposed MAIANet to better adjust to the changes in illumination. Experimental results on three datasets, that is, MSRS, TNO, and RoadSence, verify the effectiveness of the MAIANet in both qualitative and quantitative evaluations.

关键词： attention mechanism autoencoder illumination awareness image fusion multiscale feature

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Deep Neural Network based Place and Manner of Articulation Detection and Classification for Bengali Continuous Speech

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Procedia Computer Science 2018年 125卷 895-901页

作者： Tanmay Bhowmik Amitava Chowdhury Shyamal Kumar Das Mandal CET IIT Kharagpur Kharagpur-721302 India SCS University of Petroleum and Energy Studies Dehradun-248007 India

The phonological features are the most basic unit of a speech knowledge hierarchy. This paper reports about detection and classification of phonological features from Bengali continuous speech. The phonological features are based on place and manner of articulation. All the experiments are performed by a deep neural network based framework. Two different models are designed for the detection and classification task. The deep-structured models are pre-trained by stacked autoencoder. The C-DAC speech corpus is used for continuous spoken Bengali speech data. Frame wise cepstral representation is provided in the input layer of the deep-structured model. Speech data from multiple speakers has been used to confirm speaker-independency. In detection task, the system achieved 86.19% average overall accuracy. In the classification task, accuracy for the classification of place of articulation remains low with 50.2% while in manner-based classification, the system delivered an improved performance with 98.9% accuracy.

关键词： Phonological features place of articulation manner of articulation deep neural network detection classification autoencoder

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Deep Neural Networks for Creating Reliable PmP Database With a Case Study in Southern California

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JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 2022年第4期127卷 e2021JB023830-e2021JB023830页

作者： Ding, Wen Li, Tianjue Yang, Xu Ren, Kui Tong, Ping Columbia Univ Dept Appl Phys & Appl Math New York NY USA Nanyang Technol Univ Div Math Sci Sch Phys & Math Sci Singapore Singapore Nanyang Technol Univ Earth Observ Singapore Singapore Singapore Univ Calif Santa Barbara Dept Math Santa Barbara CA 93106 USA Nanyang Technol Univ Asian Sch Environm Singapore Singapore

Recent progresses in artificial intelligence and machine learning make it possible to automatically identify seismic phases from exponentially growing seismic data. Despite some exciting successes in automatic picking of the first P- and S-wave arrivals, auto-identification of later seismic phases such as the Moho-reflected PmP waves remains a significant challenge in matching the performance of experienced analysts. The main difficulty of machine-identifying PmP waves is that the identifiable PmP waves are rare, making the problem of identifying the PmP waves from a massive seismic database inherently unbalanced. In this work, by utilizing a high-quality PmP data set (10,192 manual picks) in southern California, we develop PmPNet, a deep-neural-network-based algorithm to automatically identify PmP waves efficiently;by doing so, we accelerate the process of identifying the PmP waves. PmPNet applies similar techniques in the machine learning community to address the unbalancement of PmP datasets. The architecture of PmPNet is a residual neural network (ResNet)-autoencoder with additional predictor block, where encoder, decoder, and predictor are equipped with ResNet connection. We conduct systematic research with field data, concluding that PmPNet can efficiently achieve high precision and high recall simultaneously to automatically identify PmP waves from a massive seismic database. Applying the pre-trained PmPNet to the seismic database from January 1990 to December 1999 in southern California, we obtain nearly twice more PmP picks than the original PmP data set, providing valuable data for other studies such as mapping the topography of the Moho discontinuity and imaging the lower crust structures of southern California.

关键词： PmP database deep neural networks unbalanced data set ResNet autoencoder southern California

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Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5

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ALGORITHMS 2023年第1期16卷 52-52页

作者： Narkhede, Gaurav Hiwale, Anil Tidke, Bharat Khadse, Chetan MIT World Peace Univ Sch Elect & Commun Engn Pune 411038 India MIT World Peace Univ Sch Comp Engn & Technol Pune 411038 India MIT World Peace Univ Sch Elect Engn Pune 411038 India

Day by day pollution in cities is increasing due to urbanization. One of the biggest challenges posed by the rapid migration of inhabitants into cities is increased air pollution. Sustainable Development Goal 11 indicates that 99 percent of the world's urban population breathes polluted air. In such a trend of urbanization, predicting the concentrations of pollutants in advance is very important. Predictions of pollutants would help city administrations to take timely measures for ensuring Sustainable Development Goal 11. In data engineering, imputation and the removal of outliers are very important steps prior to forecasting the concentration of air pollutants. For pollution and meteorological data, missing values and outliers are critical problems that need to be addressed. This paper proposes a novel method called multiple iterative imputation using autoencoder-based long short-term memory (MIA-LSTM) which uses iterative imputation using an extra tree regressor as an estimator for the missing values in multivariate data followed by an LSTM autoencoder for the detection and removal of outliers present in the dataset. The preprocessed data were given to a multivariate LSTM for forecasting PM2.5 concentration. This paper also presents the effect of removing outliers and missing values from the dataset as well as the effect of imputing missing values in the process of forecasting the concentrations of air pollutants. The proposed method provides better results for forecasting with a root mean square error (RMSE) value of 9.8883. The obtained results were compared with the traditional gated recurrent unit (GRU), 1D convolutional neural network (CNN), and long short-term memory (LSTM) approaches for a dataset of the Aotizhonhxin area of Beijing in China. Similar results were observed for another two locations in China and one location in India. The results obtained show that imputation and outlier/anomaly removal improve the accuracy of air pollution forecasting.

关键词： MIA-LSTM data preprocessing iterative imputation autoencoder LSTM

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More evidence for prediction model of radiosensitivity

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BIOSCIENCE REPORTS 2021年第4期41卷 BSR20210034页

作者： Du, Zixuan Zhang, Xinyan Tang, Zaixiang Soochow Univ Sch Publ Hlth Dept Biostat Med Coll Suzhou 215123 Peoples R China Soochow Univ Jiangsu Key Lab Prevent & Translat Med Geriatr Di Med Coll Suzhou 215123 Peoples R China Kennesaw State Univ Sch Data Sci & Analyt Kennesaw GA 30144 USA

With the development of precision medicine, searching for potential biomarkers plays a major role in personalized medicine. Therefore, how to predict radiosensitivity to improve radiotherapy is a burning question. The definition of radiosensitivity is complex. Radiosensitive gene/biomarker can be useful for predicting which patients would benefit from radiotherapy. The discovery of radiosensitivity biomarkers require multiple pieces of evidence. A prediction model of breast cancer radiosensitivity based on six genes was established. We had put forward some supplements on the basis of the present study. We found that there were no differences between high-and low-risk scores in the non-radiotherapy group. Patients who received radiotherapy had a significantly better overall survival than non-radiotherapy patients in the predicted low-risk score patients. Furthermore, there was no difference between radiotherapy group and non-radiotherapy group in the high-risk score group. Those results firmly supported the prediction model of radiosensitivity. In addition, building a radiosensitivity prediction model was systematically discussed. Genes of model could be screened by different methods, such as Cox regression analysis, Lasso Cox regression method, random forest algorithm and other methods. In the future, precision radiotherapy might depend on the combination of multi-omics data and high dimensional image data.

关键词： Breast cancer LASSO Cox regression autoencoder radiotherapy sensitivity

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Multi-Features Fusion for Fault Diagnosis of Pedal Robot Using Time-Speed Signals

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SENSORS 2019年第1期19卷 163-163页

作者： Zhu, Yuhao Fu, Zeyu Fu, Zhuang Chen, Xi Wu, Qi Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Shanghai Aircraft Customer Serv Co Ltd Shanghai Engn Res Ctr Civil Aircraft Hlth Monitor Shanghai 200241 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

In order to realize automation of the pollutant emission tests of vehicles, a pedal robot is designed instead of a human-driven vehicle. Sometimes, the actual time-speed curve of the vehicle will deviate from the upper or lower limit of the worldwide light-duty test cycle (WLTC) target curve, which will cause a fault. In this paper, a new fault diagnosis method is proposed and applied to the pedal robot. Since principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and autoencoder cannot extract feature information adequately when they are used alone, three types of feature components extracted by PCA, t-SNE, and autoencoder are fused to form a nine-dimensional feature set. Then, the feature set is reduced into three-dimensional space via Treelet Transform. Finally, the fault samples are classified by Gaussian process classifier. Compared with the methods using only one algorithm to extract features, the proposed method has the minimum standard deviation, 0.0078, and almost the maximum accuracy, 98.17%. The accuracy of the proposed method is only 0.24% lower than that without Treelet Transform, but the processing time is 6.73% less than that without Treelet Transform. These indicate that the multi-features fusion model and Treelet Transform method is quite effective. Therefore, the proposed method is quite helpful for fault diagnosis of the pedal robot.

关键词： pedal robot fault diagnosis autoencoder Treelet Transform multi-features fusion

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Time Series Classification for Portable Medical Devices

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2023年第4期10卷

作者： Zhong, Zhaoyi Sun, Le Subramani, Sudha Peng, Dandan Wang, Yilin Nanjing Univ Informat Sci & Technol Engn Res Ctr Digital Forens Minist Educ Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Dept Jiangsu Collaborat Innovat Ctr Atmospher Env Nanjing Peoples R China Victoria Univ Footscray Vic Australia Guangzhou Univ Sch Comp Sci & Network Engn Guangzhou Guangdong Peoples R China

INTRODUCTION: With the continuous progress of the medical Internet of Things, intelligent medical wearable devices are also gradually mature. Among them, medical wearable devices for arrhythmia detection have broad application prospects. Arrhythmia is a common cardiovascular disease. Arrhythmia causes millions of deaths every year and is one of the most noteworthy diseases. Medical mobile information systems (MMIS) provide many ECG signals, which can be used to train deep models to detect arrhythmia automatically. OBJECTIVES: Using deep models to detect arrhythmia is a research hot spot. However, the current algorithms for arrhythmia detection lack of attention to the unsupervised depth model. And they usually build a large comprehensive model for all users for arrhythmia detection, which has low flexibility and cannot extract personalized features from users. Therefore, this paper proposes a personalized arrhythmia detection system based on attention mechanism called personAD. METHODS: The personAD contains four modules: (1) Preprocessing module;(2) Training module;(3) Arrhythmia detection module and (4) User registration module. The personAD trains a separate autoencoder for each user to detect personalized arrhythmia. Using autoencoder to detect arrhythmia can avoid the imbalance of training data. The autoencoder combines a convolutional network and two attention mechanisms. RESULTS: Based on the results on MIT-BIH Arrhythmia Database, we can find that our arrhythmia detection system achieve 98.03% ACC and 99.32% AUC respectively. CONCLUSION: The personAD can effectively detect arrhythmia in ECG signals. The personAD has higher flexibility, and can easily modify the autoencoders for detecting arrhythmia for users.

关键词： time series classification autoencoder attention mechanism Medical mobile information systems

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Establishing a novel procedure to detect deviations from standard milk processing by using online Raman spectroscopy

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FOOD CONTROL 2022年 131卷 108442-108442页

作者： Vasafi, Pegah Sadeghi Hinrichs, Joerg Hitzmann, Bernd Univ Hohenheim Inst Food Sci & Biotechnol Garbenstr 23 D-70599 Stuttgart Germany

Controlling milk processing steps is a crucial task as it affects the quality and safety of the final product. Using Raman spectrometer in combination with various evaluation techniques such as principal component analysis and regression, Gaussian process regression, and the autoencoder were checked to define an accurate method for detection of deviations from standard procedures. For this purpose, milk with 5% fat measured at 10 degrees C was considered as the reference milk. A temperature-controlled flow cell was used in a by-pass for online measurements. While the principal component regression was not able to predict the deviations, results demonstrate the capability of Gaussian process regression and the autoencoder to detect 5% added water and cleaning solution, 0.1% difference in fat content and variation of 5 degrees C in measurement temperature. It can be concluded that both procedures display promising results, however, the autoencoder can be trained once and used immediately for online supervision. Therefore, changes can be detected promptly, enabling companies to react instantly.

关键词： autoencoder Milk processing Deviations detection Gaussian process regression Raman spectroscopy

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DECIMER: towards deep learning for chemical image recognition

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JOURNAL OF CHEMINFORMATICS 2020年第1期12卷 65-65页

作者： Rajan, Kohulan Zielesny, Achim Steinbeck, Christoph Friedrich Schiller Univ Jena Inst Inorgan & Analyt Chem Lessingstr 8 D-07743 Jena Germany Westphalian Univ Appl Sci Inst Bioinformat & Chemoinformat August Schmidt Ring 10 D-45665 Recklinghausen Germany

The automatic recognition of chemical structure diagrams from the literature is an indispensable component of workflows to re-discover information about chemicals and to make it available in open-access databases. Here we report preliminary findings in our development of Deep lEarning for Chemical ImagE Recognition (DECIMER), a deep learning method based on existing show-and-tell deep neural networks, which makes very few assumptions about the structure of the underlying problem. It translates a bitmap image of a molecule, as found in publications, into a SMILES. The training state reported here does not yet rival the performance of existing traditional approaches, but we present evidence that our method will reach a comparable detection power with sufficient training time. Training success of DECIMER depends on the input data representation: DeepSMILES are superior over SMILES and we have a preliminary indication that the recently reported SELFIES outperform DeepSMILES. An extrapolation of our results towards larger training data sizes suggests that we might be able to achieve near-accurate prediction with 50 to 100 million training structures. This work is entirely based on open-source software and open data and is available to the general public for any purpose.

关键词： Optical chemical entity recognition Chemical structure Deep learning Deep neural networks autoencoder decoder

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