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A deep learning method for lincRNA detection using auto-encoder algorithm

BMC BIOINFORMATICS 2017年第15-Sup期18卷 511-511页

作者： Yu, Ning Yu, Zeng Pan, Yi SUNY Coll Brockport Dept Comp Sci 350 New Campus Dr Brockport NY 14420 USA Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Sichuan Peoples R China Georgia State Univ Dept Comp Sci 25 Pk Pl Atlanta GA 30303 USA

Background: RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences;(2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition. Results: The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction. Conclusions: The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence da

关键词： Deep learning Long intergenic non-coding RNA (lincRNA) auto-encoder Transcription sites RNA-seq Knowledge-based discovery

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Towards reliable hepatocytic anatomy segmentation in laparoscopic cholecystectomy using U-Net with auto-encoder

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SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES 2023年第9期37卷 7358-7369页

作者： Alkhamaiseh, Koloud N. Grantner, Janos L. Shebrain, Saad Abdel-Qader, Ikhlas Western Michigan Univ Dept Elect & Comp Engn Kalamazoo MI 49008 USA Western Michigan Univ Homer Stryker MD Sch Med Kalamazoo MI USA

BackgroundMost bile duct (BDI) injuries during laparoscopic cholecystectomy (LC) occur due to visual misperception leading to the misinterpretation of anatomy. Deep learning (DL) models for surgical video analysis could, therefore, support visual tasks such as identifying critical view of safety (CVS). This study aims to develop a prediction model of CVS during LC. This aim is accomplished using a deep neural network integrated with a segmentation model that is capable of highlighting hepatocytic *** images from LC videos were annotated with four hepatocystic landmarks of anatomy segmentation. A deep autoencoder neural network with U-Net to investigate accurate medical image segmentation was trained and tested using fivefold cross-validation. Accuracy, Loss, Intersection over Union (IoU), Precision, Recall, and Hausdorff Distance were computed to evaluate the model performance versus the annotated ground *** total of 1550 images from 200 LC videos were annotated. Mean IoU for segmentation was 74.65%. The proposed approach performed well for automatic hepatocytic landmarks identification with 92% accuracy and 93.9% precision and can segment challenging ***, can potentially provide an intraoperative model for surgical video analysis and can be trained to guide surgeons toward reliable hepatocytic anatomy segmentation and produce selective video documentation of this safety step of LC.

关键词： Critical view of safety Laparoscopic cholecystectomy Deep learning Anatomy segmentation auto-encoder U-Net Challenging cases Surgical safety

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A Specific and Selective Neural Response Representation With Decorrelating auto-encoder

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IEEE ACCESS 2019年 7卷 70011-70020页

作者： Zhou, Jie Chen, Qian Jiang, Hao Cai, Shiqing Shao, Genfu Kikuchi, Hisakazu Nanjing Univ Informat Sci & Technol Coll Elect & Informat Engn Nanjing 210044 Jiangsu Peoples R China Hangzhou Dianzi Univ Coll Commun Engn Hangzhou 310000 Zhejiang Peoples R China Niigata Univ Coll Elect & Elect Engn Niigata 9502181 Japan

Since the pioneering report with an unsupervised pre-training principle was published, deep architectures, as a simulation of primary cortexes, have been intensively studied and successfully utilized in solving some recognition tasks. Motivated by that, herein, we propose a decorrelating regularity on autoencoders, named decorrelating auto-encoder (DcA), which can be stacked to deep architectures, called the SDcA model. The learning algorithm is designed based on the principles of redundancy-reduction and the infomax, and a fine-tuning algorithm based on correlation detecting criteria. The property of our model is evaluated by auditory and handwriting recognition tasks with the TIMIT acoustic-phonetic continuous speech corpus and MNIST database. The results show that our model has a general advantage as compared with four existing models, especially in low levels, and when training samples are scarce our model put up stronger learning capacity and generalization.

关键词： Machine learning pattern recognition deep architecture auto-encoder de-correlation

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Gappy AE: A nonlinear approach for Gappy data reconstruction using auto-encoder

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2024年 426卷

作者： Kim, Youngkyu Choi, Youngsoo Yoo, Byounghyun Korea Inst Sci & Technol Ctr Artificial Intelligence Seoul 02792 South Korea Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94550 USA Korea Natl Univ Sci & Technol KIST Sch AI Robot Seoul 02792 South Korea

We introduce a novel data reconstruction algorithm known as Gappy auto -encoder (Gappy AE) to address the limitations associated with Gappy proper orthogonal decomposition (Gappy POD), a widely used method for data reconstruction when dealing with sparse measurements or missing data. Gappy POD has inherent constraints in accurately representing solutions characterized by slowly decaying Kolmogorov N -widths, primarily due to its reliance on linear subspaces for data prediction. In contrast, Gappy AE leverages the power of nonlinear manifold representations to address data reconstruction challenges of conventional Gappy POD. It excels at real-time state prediction in scenarios where only sparsely measured data is available, filling in the gaps effectively. This capability makes Gappy AE particularly valuable, such as for digital twin and image correction applications. To demonstrate the superior data reconstruction performance of Gappy AE with sparse measurements, we provide several numerical examples, including scenarios like 2D diffusion, 2D radial advection, and 2D wave equation problems. Additionally, we assess the impact of four distinct sampling algorithms - discrete empirical interpolation method, the S -OPT algorithm, Latin hypercube sampling, and uniformly distributed sampling - on data reconstruction accuracy. Our findings conclusively show that Gappy AE outperforms Gappy POD in data reconstruction when sparse measurements are given.

关键词： auto-encoder Nonlinear manifold solution representation Hyper-reduction Sparse measurements Data reconstruction Digital twin

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Discriminative Regularized auto-encoder for Early Detection of Knee OsteoArthritis: Data from the Osteoarthritis Initiative

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2020年第9期39卷 2976-2984页

作者： Nasser, Yassine Jennane, Rachid Chetouani, Aladine Lespessailles, Eric El Hassouni, Mohammed Mohammed V Univ Rabat Fac Sci LRIT Rabat 10000 Morocco Univ Orleans I3MTO EA 4708 Lab F-45100 Orleans France Univ Orleans PRISME EA 4229 Lab F-45100 Orleans France I3MTO EA 4708 Lab F-45067 Orleans France Hosp Orleans F-45100 Orleans France Mohammed V Univ Rabat FLSH Rabat 10000 Morocco Mohammed V Univ Rabat LRIT Rabat 10000 Morocco

OsteoArthritis (OA) is the most common disorder of the musculoskeletal system and the major cause of reduced mobility among seniors. The visual evaluation of OA still suffers from subjectivity. Recently, Computer-Aided Diagnosis (CAD) systems based on learning methods showed potential for improving knee OA diagnostic accuracy. However, learning discriminative properties can be a challenging task, particularly when dealing with complex data such as X-ray images, typically used for knee OA diagnosis. In this paper, we introduce a Discriminative Regularized auto encoder (DRAE) that allows to learn both relevant and discriminative properties that improve the classification performance. More specifically, a penalty term, called discriminative loss is combined with the standard auto-encoder training criterion. This additional term aims to force the learned representation to contain discriminative information. Our experimental results on data from the public multicenter OsteoArthritis Initiative (OAI) show that the developed method presents potential results for early knee OA detection.

关键词： Feature extraction Bones Decoding Osteoarthritis Image reconstruction X-ray imaging Laboratories Feature representation learning auto-encoder discriminative regularization knee osteoarthritis detection plain radiography X-ray

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Personalized tag recommendation via denoising auto-encoder

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2023年第1期26卷 95-114页

作者： Zhao, Weibin Shang, Lin Yu, Yonghong Zhang, Li Wang, Can Chen, Jiajun Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Posts & Telecommun TongDa Coll Yangzhou Jiangsu Peoples R China Royal Holloway Univ London Dept Comp Sci Surrey England Griffith Univ Sch Informat & Commun Technol Nathan Qld Australia

Personalized tag recommender systems automatically recommend users a set of tags used to annotate items according to users' past tagging information. Learning the representations of involved entities (i.e. users, items and tags) and capturing the complex relationships among them are crucial for personalized tag recommender systems. However, few studies have been conducted to simultaneously achieve these two sub-goals. In this research, we propose a novel personalized tag recommendation model based on the denoising auto-encoder, namely DAE-PTR, which learns the representations of entities and encodes the complex relationships by exploiting the denoising auto-encoder framework. Specifically, for each user, we firstly generate the corrupted version of the respective tagging information by adding the multiplicative mask-out/drop-out noise into the original input. Then, we learn the latent representations from the corrupted input via the auto-encoder framework by using the cross-entropy loss. More importantly, we integrate the latent user and item embeddings into the processing of encoding, which makes the learnt hidden representations of the auto-encoder network encode multiple types of relationships among entities, i.e. the relationships between users and tags, between items and tags, and among tags. Finally, we employ the decoder component to reconstruct the original input based on the learnt latent representations. Experimental results on the real-world datasets show that our proposed DAE-PTR model is superior to the traditional personalized tag recommendation models.

关键词： Recommender systems auto-encoder Personalized tag recommendation

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An efficient and effective convolutional auto-encoder extreme learning machine network for 3d feature learning

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NEUROCOMPUTING 2016年第PartB期174卷 988-998页

作者： Wang, Yueqing Xie, Zhige Xu, Kai Dou, Yong Lei, Yuanwu Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

3D shape features play a crucial role in graphics applications, such as 3D shape matching, recognition, and retrieval. Various 3D shape descriptors have been developed over the last two decades;however, existing descriptors are handcrafted features that are labor-intensively designed and cannot extract discriminative information for a large set of data. In this paper, we propose a rapid 3D feature learning method, namely, a convolutional auto-encoder extreme learning machine (CAE-ELM) that combines the advantages of the convolutional neuron network, auto-encoder, and extreme learning machine (ELM). This method performs better and faster than other methods. In addition, we define a novel architecture based on CAE-ELM. The architecture accepts two types of 3D shape representation, namely, voxel data and signed distance field data (SDF), as inputs to extract the global and local features of 3D shapes. Voxel data describe structural information, whereas SDF data contain details on 3D shapes. Moreover, the proposed CAE-ELM can be used in practical graphics applications, such as 3D shape completion. Experiments show that the features extracted by CAE-ELM are superior to existing hand-crafted features and other deep learning methods or ELM models. Moreover, the classification accuracy of the proposed architecture is superior to that of other methods on ModelNet10 (91.4%) and ModelNet40 (8435%). The training process also runs faster than existing deep learning methods by approximately two orders of magnitude. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Convolutional Extreme learning machine auto-encoder Feature learning

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Outlier Detection for Water Supply Data Based on Joint auto-encoder

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Computers, Materials & Continua 2020年第7期64卷 541-555页

作者： Shu Fang Lei Huang Yi Wan Weize Sun Jingxin Xu Guangdong Laboratory of Artificial-Intelligence and Cyber-Economics(SZ) College of Electronics and Information EngineeringShenzhen UniversityShenzhen518061China Water Resources Management Center of Ministry of Water Resources BeijingChina Departmet of Housing and Public Works QueenslandAustralia

With the development of science and technology,the status of the water environment has received more and more *** this paper,we propose a deep learning model,named a Joint auto-encoder network,to solve the problem of outlier detection in water supply *** Joint auto-encoder network first expands the size of training data and extracts the useful features from the input data,and then reconstructs the input data effectively into an *** outliers are detected based on the network’s reconstruction errors,with a larger reconstruction error indicating a higher rate to be an *** water supply data,there are mainly two types of outliers:outliers with large values and those with values closed to *** set two separate thresholds,and,for the reconstruction errors to detect the two types of outliers *** data samples with reconstruction errors exceeding the thresholds are voted to be *** two thresholds can be calculated by the classification confusion matrix and the receiver operating characteristic(ROC)*** have also performed comparisons between the Joint auto-encoder and the vanilla auto-encoder in this paper on both the synthesis data set and the MNIST data *** a result,our model has proved to outperform the vanilla auto-encoder and some other outlier detection approaches with the recall rate of 98.94 percent in water supply data.

关键词： Water supply data outlier detection auto-encoder deep learning

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Online deep learning based on auto-encoder

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APPLIED INTELLIGENCE 2021年第8期51卷 5420-5439页

作者： Zhang, Si-si Liu, Jian-wei Zuo, Xin Lu, Run-kun Lian, Si-ming ChinaUnivers Petr Coll Informat Sci & Engn Dept Automat Beijing Campus CUP260 Mailbox China Univ Petr Beijing 102249 Peoples R China

Online learning is an important technical means for sketching massive real-time and high-speed data. Although this direction has attracted intensive attention, most of the literature in this area ignore the following three issues: (1) they think little of the underlying abstract hierarchical latent information existing in examples, even if extracting these abstract hierarchical latent representations is useful to better predict the class labels of examples;(2) the idea of preassigned model on unseen datapoints is not suitable for modeling streaming data with evolving probability distribution. This challenge is referred as "model flexibility". And so, with this in minds, the online deep learning model we need to design should have a variable underlying structure;(3) moreover, it is of utmost importance to fusion these abstract hierarchical latent representations to achieve better classification performance, and we should give different weights to different levels of implicit representation information when dealing with the data streaming where the data distribution changes. To address these issues, we propose a two-phase Online Deep Learning based on auto-encoder (ODLAE). Based on auto-encoder, considering reconstruction loss, we extract abstract hierarchical latent representations of instances;Based on predictive loss, we devise two fusion strategies: the output-level fusion strategy, which is obtained by fusing the classification results of encoder's each hidden layer;and feature-level fusion strategy, which is leveraged self-attention mechanism to fusion the every hidden layer's output. Finally, in order to improve the robustness of the algorithm, we also try to utilize the denoising auto-encoder to yield hierarchical latent representations. Experimental results on different datasets are presented to verify the validity of our proposed algorithm (ODLAE) outperforms several baselines.

关键词： Online deep learning auto-encoder Output-level fusion Feature-level fusion Denoising auto-encoder

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Image colorization using deep convolutional auto-encoder with multi-skip connections

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SOFT COMPUTING 2023年第6期27卷 3037-3052页

作者： Jin, Xin Di, Yide Jiang, Qian Chu, Xing Duan, Qing Yao, Shaowen Zhou, Wei Yunnan Univ Sch Software Kunming 650091 Yunnan Peoples R China

The colorization of grayscale images is a challenging task in image processing. Recently, deep learning has shown remarkable performance in image colorization. However, the detail loss and color distortion are still serious problem for most existing methods, and some useful features may be lost in the processes of various convolutional layers because of the vanishing gradient problem. Therefore, there is still a considerable space to reach the roof of image colorization. In this work, we propose a deep convolutional auto-encoder with special multi-skip connections for image colorization in YUV color space, and the specific contributions or designs of this work are shown as the following five points. First, a given gray image is used as the Y channel to input a deep learning model to predict U and V channel. Second, the adopted encoder-decoder consists of a main path and two branch paths, and the branch path has two skip connection ways that include one shortcut in each three layers and one shortcut in each six layers. Third, the convolutional kernel size is set as 2*2 that is a special consideration in the path of one shortcut in each six layers. Fourth, a composite loss function is proposed based on the mean square error and gradient that is defined to calculate the errors between the ground truth and the predicted result. Finally, we also discuss the reasonable network parameters, such as the way of shortcut connection, the convolutional kernel size of shortcut connection, and loss function parameters. Experiments on different image datasets show that the proposed image colorization model is effective, and the scores of the PNSR, RMSE, SSIM, and Pearson correlation coefficient are, respectively, to 27.0595, 0.1311, 0.561, and 0.9771.

关键词： auto-encoder Convolutional neural network Deep learning Image processing Image colorization Residual neural network

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