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CAE-CNN: Predicting transcription factor binding site with convolutional autoencoder and convolutional neural network

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 183卷 115404-115404页

作者： Zhang, Yongqing Qiao, Shaojie Zeng, Yuanqi Gao, Dongrui Han, Nan Zhou, Jiliu Chengdu Univ Informat Technol Sch Comp Sci Chengdu 610225 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Chengdu Univ Informat Technol Sch Software Engn Chengdu 610225 Peoples R China Chengdu Univ Informat Technol Sch Management Chengdu 610103 Peoples R China

Transcription factor binding site (TFBS) is a DNA sequence that binds to transcription factor and regulates the transcription process of the gene. Although deep learning algorithms are superior to traditional methods in predicting transcription factor binding site, they often rely too much on negative sample data, which cannot be verified by experiment. In particular, a training model with such negative samples can generate a lot of noisy data and affect the classification performance. In order to cope with the aforementioned drawbacks, we propose a new architecture by combining a convolutional autoencoder with convolutional neural network, which is called CAE-CNN (Convolutional autoencoder and Convolutional Neural Network). Specifically, motivated by the image reconstruction, we use a convolutional autoencoder to extract useful features from the positive samples in DNA nucleotides. Consequently, the learned features will be used by the convolutional neural network in the phase of training. Furthermore, we employ a highway connection layer to better capture the features of DNA nucleotides through a gated unit. Extensive experiments based on human and mouse TFBS datasets evaluate the effectiveness of the proposed method for the motif discovery task, outperforming the state-of-the-art methods in accuracy, precision, recall, and AUC value. To the best of our knowledge, the original contribution of this work lies in integrating unsupervised and supervised learning methods to study the TFBS, thereby being able to build a more robust and generative TFBS prediction model.

关键词： Transcription factor binding sites Convolutional neural networks Motif discovery Bioinformatics autoencoder

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An autoencoder-Based Deep Learning Approach for Load Identification in Structural Dynamics

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SENSORS 2021年第12期21卷 4207页

作者： Rosafalco, Luca Manzoni, Andrea Mariani, Stefano Corigliano, Alberto Politecn Milan Dipartimento Ingn Civile & Ambientale Piazza L da Vinci 32 I-20133 Milan Italy Politecn Milan Dipartimento Matemat MOX Piazza L da Vinci 32 I-20133 Milan Italy

In civil engineering, different machine learning algorithms have been adopted to process the huge amount of data continuously acquired through sensor networks and solve inverse problems. Challenging issues linked to structural health monitoring or load identification are currently related to big data, consisting of structural vibration recordings shaped as a multivariate time series. Any algorithm should therefore allow an effective dimensionality reduction, retaining the informative content of data and inferring correlations within and across the time series. Within this framework, we propose a time series autoencoder (AE) employing inception modules and residual learning for the encoding and the decoding parts, and an extremely reduced latent representation specifically tailored to tackle load identification tasks. We discuss the choice of the dimensionality of this latent representation, considering the sources of variability in the recordings and the inverse-forward nature of the AE. To help setting the aforementioned dimensionality, the false nearest neighbor heuristics is also exploited. The reported numerical results, related to shear buildings excited by dynamic loadings, highlight the signal reconstruction capacity of the proposed AE, and the capability to accomplish the load identification task.

关键词： load system identification deep learning structural dynamics autoencoder false nearest neighbor

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Deep convolutional autoencoder for the simultaneous removal of baseline noise and baseline drift in chromatograms

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JOURNAL OF CHROMATOGRAPHY A 2021年 1646卷 462093-462093页

作者： Kensert, Alexander Collaerts, Gilles Efthymiadis, Kyriakos Van Broeck, Peter Desmet, Gert Cabooter, Deirdre Katholieke Univ Leuven Univ Leuven Dept Pharmaceut & Pharmacol Sci Pharmaceut Anal Herestr 49 B-3000 Leuven Belgium Vrije Univ Brussel Dept Comp Sci Artificial Intelligence Lab Pleinlaan 9 B-1050 Brussels Belgium Janssen Pharmaceut Dept Pharmaceut Dev & Mfg Sci Turnhoutseweg 30 Beerse Belgium Vrije Univ Brussel Dept Chem Engn Pleinlaan 2 B-1050 Brussels Belgium

Enhancement of chromatograms, such as the reduction of baseline noise and baseline drift, is often essential to accurately detect and quantify analytes in a mixture. Current methods have been well studied and adopted for decades and have assisted researchers in obtaining reliable results. However, these methods rely on relatively simple statistics of the data (chromatograms) which in some cases result in significant information loss and inaccuracies. In this study, a deep one-dimensional convolutional autoencoder was developed that simultaneously removes baseline noise and baseline drift with minimal information loss, for a large number and great variety of chromatograms. To enable the autoencoder to denoise a chromatogram to be almost, or completely, noise-free, it was trained on data obtained from an implemented chromatogram simulator that generated 190.0 0 0 representative simulated chromatograms. The trained autoencoder was then tested and compared to some of the most widely used and well established denoising methods on testing datasets of tens of thousands of simulated chromatograms;and then further tested and verified on real chromatograms. The results show that the developed autoencoder can successfully remove baseline noise and baseline drift simultaneously with minimal information loss;outperforming methods like Savitzky-Golay smoothing, Gaussian smoothing and wavelet smoothing for baseline noise reduction (root mean squared error of 1.094 mAU compared to 2.074 mAU, 2.394 mAU and 2.199 mAU) and Savitkzy-Golay smoothing combined with asymmetric least-squares or polynomial fitting for baseline noise and baseline drift reduction (root mean absolute error of 1.171 mAU compared to 3.397 mAU and 4.923 mAU). Evidence is presented that autoencoders can be utilized to enhance and correct chromatograms and consequently improve and alleviate downstream data analysis, with the drawback of needing a carefully implemented simulator, that generates realistic chromatogram

关键词： Deep learning Machine learning autoencoder Baseline noise Baseline drift Noise reduction Denoising

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Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model

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BMC BIOINFORMATICS 2021年第1期22卷 33-33页

作者： Emdadi, Akram Eslahchi, Changiz Shahid Beheshti Univ Dept Comp & Data Sci Fac Math Sci Tehran Iran Inst Res Fundamental Sci IPM Sch Biol Sci Tehran 193955746 Iran

Background: Predicting the response of cancer cell lines to specific drugs is an essential problem in personalized medicine. Since drug response is closely associated with genomic information in cancer cells, some large panels of several hundred human cancer cell lines are organized with genomic and pharmacogenomic data. Although several methods have been developed to predict the drug response, there are many challenges in achieving accurate predictions. This study proposes a novel feature selection-based method, named Auto-HMM-LMF, to predict cell line-drug associations accurately. Because of the vast dimensions of the feature space for predicting the drug response, Auto-HMM-LMF focuses on the feature selection issue for exploiting a subset of inputs with a significant contribution. Results: This research introduces a novel method for feature selection of mutation data based on signature assignments and hidden Markov models. Also, we use the autoencoder models for feature selection of gene expression and copy number variation data. After selecting features, the logistic matrix factorization model is applied to predict drug response values. Besides, by comparing to one of the most powerful feature selection methods, the ensemble feature selection method (EFS), we showed that the performance of the predictive model based on selected features introduced in this paper is much better for drug response prediction. Two datasets, the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) are used to indicate the efficiency of the proposed method across unseen patient cell-line. Evaluation of the proposed model showed that Auto-HMM-LMF could improve the accuracy of the results of the state-of-the-art algorithms, and it can find useful features for the logistic matrix factorization method. Conclusions: We depicted an application of Auto-HMM-LMF in exploring the new candidate drugs for head and neck cancer that showed the proposed method is use

关键词： Cancer Drug response autoencoder Hidden Markov model Matrix factorization Personalized treatment

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A steerable pyramid autoencoder based framework for anomaly frame detection of water pipeline CCTV inspection

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MEASUREMENT 2021年 174卷 109020-109020页

作者： Jiao, Yutong Rayhana, Rakiba Bin, Junchi Liu, Zheng Wu, Angie Kong, Xiangjie Univ British Columbia Sch Engn Kelowna BC Canada Pure Technol Mississauga ON Canada

Closed-circuit television (CCTV) is being widely adopted in water pipeline inspection. The inspector needs to spend a long time to watch the recorded video during the office-based survey and can get fatigue easily. An automated process can release the inspector?s work load and ensure the consistent quality of the survey. However, a fully automated survey of varied structural discontinuities still remains as a challenge. This study aims to first identify the anomaly frames of the CCTV video, which contain the major anomalies captured from the internal surface of the pipe. Thus, the inspector can focus more on these anomaly frames. In this paper, an anomaly frame detection framework based on steerable pyramid autoencoder (SPAE) is proposed. The SPAE can generate discriminative representations to be used in the prediction. Both the parameter optimization and comparative studies for the proposed SPAE were carried out in this research. The experimental results demonstrate that this novel SPAE algorithm can achieve 0.984 accuracy and 0.984 F1-score, which outperforms other state-of-the-art methods selected for comparison. Thus, the proposed framework can significantly improve the accuracy and efficiency for anomaly frame detection, which will highly facilitate the pipeline condition assessment through the CCTV inspection.

关键词： Anomaly frame detection Water pipeline CCTV inspection autoencoder Steerable pyramid Deep learning Classification

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Temporal convolutional autoencoder for unsupervised anomaly detection in time series

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APPLIED SOFT COMPUTING 2021年 112卷 107751-107751页

作者： Thill, Markus Konen, Wolfgang Wang, Hao Back, Thomas TH Koln Univ Appl Sci D-51643 Gummersbach Germany Leiden Univ LIACS NL-2333 CA Leiden Netherlands

Learning temporal patterns in time series remains a challenging task up until today. Particularly for anomaly detection in time series, it is essential to learn the underlying structure of a system's normal behavior. Periodic or quasiperiodic signals with complex temporal patterns make the problem even more challenging: Anomalies may be a hard-to-detect deviation from the normal recurring pattern. In this paper, we present TCN-AE, a temporal convolutional network autoencoder based on dilated convolutions. Contrary to many other anomaly detection algorithms, TCN-AE is trained in an unsupervised manner. The algorithm demonstrates its efficacy on a comprehensive real-world anomaly benchmark comprising electrocardiogram (ECG) recordings of patients with cardiac arrhythmia. TCNAE significantly outperforms several other unsupervised state-of-the-art anomaly detection algorithms. Moreover, we investigate the contribution of the individual enhancements and show that each new ingredient improves the overall performance on the investigated benchmark. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Anomaly detection Deep learning TCN autoencoder Mahalanobis distance

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Explainable prediction of electric energy demand using a deep autoencoder with interpretable latent space

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 186卷 115842-115842页

作者： Kim, Jin-Young Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul 03722 South Korea

Recently, many studies have exploited the potential of deep learning to forecast energy demand, but they cannot explain the results. They only analyze the simple correlations between the input and output to discover the most important input features, or they depend on the manual investigation of the latent space embedded with power demand patterns. In this paper, to overcome these shortcomings, we propose a deep autoencoder that can explain the prediction results by manipulating the latent space. It consists of 1) a power encoder that embeds power information, 2) an auxiliary encoder that embeds auxiliary information for an interpretable latent space in two dimensions, 3) a predictor that predicts power demand by using concatenated values of the latent variables extracted from the two encoders, and 4) an explainer that provides the most important input features in predicting the future demand by utilizing the interpretable latent variables. Several experiments on a dataset of household electric energy demand show that the proposed model not only performs better than conventional models, with a mean squared error of 0.376 in predicting electricity demand for 60 min, but also provides the capacity to explain the results by analyzing the correlation of inputs, latent variables, and energy demand predicted.

关键词： Deep learning autoencoder Explainability Latent space Energy demand prediction Time-series modeling

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Performance Evaluation of a Combined Convolutional autoencoder and Image Recognition Model for Large Scale Images 11th

Performance Evaluation of a Combined Convolutional AutoEncod...

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11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024

作者： Myojin, Takuho Hoshino, Yukinobu Tosayamada Kami City 185 Miyanokuchi Kochi782-8502 Japan

ISBN: (纸本)9789819647521

There is a need to introduce remote disaster monitoring camera systems that utilize AI technology. This is especially the point where disasters have become larger and more frequent in recent years. The challenge to realize this system is to acquire images with cameras and to transmit this information stably and quickly. I then turned my attention to the CAE(Convolutional autoencoder). The reason for using CAE was to reduce the amount of data compared to images captured by cameras and to improve security. In this paper, we use this CAE to design an AI model that integrates an image recognition model for image restoration verification and disaster detection, and verify its performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： AI autoencoder CNN Deconvolution

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Imputation of single-cell gene expression with an autoencoder neural network

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Quantitative Biology 2020年第1期8卷 78-94页

作者： Md.Bahadur Badsha Rui Li Boxiang Liu Yang ILi Min Xian Nicholas EBanovich Audrey Qiuyan Fu Department of Statistical Science Institute for Bioinformatics and Evolutionary StudiesInstitute for Modeling Collaboration&InnovationUniversity of IdahoMoscowID 83844USA Department of Biology Stanford UniversityStanfordCA 94305USA Section of Genetic Medicine University of ChicagoChicagoIL 60637USA Department of Computer Science University of IdahoIdaho FallsID 83401USA The Translational Genomics Research Institute PhoenixAZ 85004USA

Background:Single-cell RNA-sequencing(scRNA-seq)is a rapidly evolving technology that enables measurement of gene expression levels at an unprecedented *** the explosive growth in the number of cells that can be assayed by a single experiment,scRNA-seq still has several limitations,including high rates of dropouts,which result in a large number of genes having zero read count in the scRNA-seq data,and complicate downstream ***:To overcome this problem,we treat zeros as missing values and develop nonparametric deep learning methods for ***,our LATE(Learning with autoencoder)method trains an autoencoder with random initial values of the parameters,whereas our TRANSLATE(TRANSfer learning with LATE)method further allows for the use of a reference gene expression data set to provide LATE with an initial set of parameter ***:On both simulated and real data,LATE and TRANSLATE outperform existing scRNA-seq imputation methods,achieving lower mean squared error in most cases,recovering nonlinear gene-gene relationships,and better separating cell *** are also highly scalable and can efficiently process over 1 million cells in just a few hours on a ***:We demonstrate that our nonparametric approach to imputation based on autoencoders is powerful and highly efficient.

关键词： single-cell gene expression deep learning autoencoder

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Bearing Fault Classification Using Wavelet Energy and autoencoder 16th

Bearing Fault Classification Using Wavelet Energy and Autoen...

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16th International Conference on Distributed Computing and Internet Technology (ICDCIT)

作者： Udmale, Sandeep S. Singh, Sanjay Kumar Veermata Jijabai Technol Inst VJTI Dept Comp Engn & IT Mumbai 400019 Maharashtra India Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi 221005 Uttar Pradesh India

ISBN: (纸本)9783030369873;9783030369866

Today's modern industry has widely accepted the intelligent condition monitoring system to improve the industrial organization. As an effect, the data-driven-based fault diagnosis methods are designed by integrating signal processing techniques along with artificial intelligence methods. Various signal processing approaches have been proposed for feature extraction from vibration signals to construct the fault feature space, and thus, over the years, the feature space has increased rapidly. Also, the challenge is to identify the promising features from the space for improving diagnosis performance. Therefore, in this paper, wavelet energy is presented as an input feature set to the fault diagnosis system. In this paper, wavelet energy is utilized to represent the multiple faults for reducing the requirement of number features, and therefore, the complex task of feature extraction becomes simple. Further, the convolutional autoencoder has assisted in finding more distinguishing fault feature from wavelet energy to improve the diagnosis task using extreme learning machine. The proposed method testified using two vibration datasets, and decent results are achieved. The effect of autoencoder on fault diagnosis performance has been observed in comparison to principal component analysis (PCA). Also, the consequence has seen in the size of the extreme learning machine (ELM) architecture.

关键词： autoencoder Bearing Fault diagnosis Wavelet energy

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