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ADAGE signature analysis: differential expression analysis with data-defined gene sets

BMC BIOINFORMATICS 2017年第1期18卷 512-512页

作者： Tan, Jie Huyck, Matthew Hu, Dongbo Zelaya, Rene A. Hogan, Deborah A. Greene, Casey S. Geisel Sch Med Dartmouth Dept Mol & Syst Biol Hanover NH 03755 USA Univ Penn Dept Syst Pharmacol & Translat Therapeut Philadelphia PA 19104 USA Geisel Sch Med Dartmouth Dept Microbiol & Immunol Hanover NH 03755 USA

Background: Gene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. Results: Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server (http://***) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and Delta anr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results;however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and A

关键词： Gene set analysis denoising autoencoder Unsupervised feature extraction Data-defined gene set Pseudomonas Aeruginosa

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Lifted Bregman training of neural networks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 10909-10959页

作者： Xiaoyu Wang Martin Benning Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge UK School of Mathematical Sciences Queen Mary University of London London UK

We introduce a novel mathematical formulation for the training of feed-forward neural networks with (potentially non-smooth) proximal maps as activation functions. This formulation is based on Bregman distances and a key advantage is that its partial derivatives with respect to the network's parameters do not require the computation of derivatives of the network's activation functions. Instead of estimating the parameters with a combination of first-order optimisation method and back-propagation (as is the state-of-the-art), we propose the use of non-smooth first-order optimisation methods that exploit the specific structure of the novel formulation. We present several numerical results that demonstrate that these training approaches can be equally well or even better suited for the training of neural network-based classifiers and (denoising) autoencoders with sparse coding compared to more conventional training frameworks.

关键词： lifted network training distributed optimisation Bregman distances sparse autoencoder denoising autoencoder classification compressed sensing

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A GAN noise modeling based blind denoising method for guided waves

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MEASUREMENT 2022年 188卷 110596-110596页

作者： Cui, Xiushi Li, Dongsheng Li, Ziqi Ou, Jinping Dalian Univ Technol Sch Civil Engn Dalian 116024 Liaoning Peoples R China

In the detection using guided waves, the signal often carries a high level of non-Gaussian noise. The traditional denoising method cannot estimate and use the prior information of the signal, which leads to poor denoising effect. To tackle this problem, this paper proposed a denoising network based on the combination of generative adversarial network (GAN) and autoencoder (AE). First, GAN is used to estimate the distribution characteristics of the extracted noise and generate samples. Second, according to the characteristics of the guided wave, a pair of datasets are generated to train DAE network. The trained denoising AE has strong robustness. As a result, the proposed GAN-AE based denoiser (GAD) can effectively can effectively reduce the noise level and has the ability to accurately recover the peak time of the wave packet. In particular, the proposed method significantly outperforms conventional denoising methods in low signal-to-noise (SNR) conditions.

关键词： Guided waves Generative adversarial network denoising autoencoder Prior information non-Gaussian features

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Minimizing Illumination Effect in License Plate Recognition

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2021年第2期28卷 363-369页

作者： Kim, Jae-Seoung Whangbo, Taeg-Keun Gachon Univ Dept Comp Engn Coll IT Seongnam Si South Korea

The intelligent transportation system is a key technology for efficient traffic control that has been applied in various fields. The existing intelligent transportation system detects the license plates of vehicles mainly through image feature analysis technology by using image-processing techniques. While this method has the advantage of quickly recognizing license plates by simple computing when the environment for recognizing license plate images is favourable, its accuracy is significantly compromised by various environmental changes. This study proposes a method using Faster region-based CNN (R-CNN) and denoising autoencoder technology to improve the recognition performance for tilted and broken plates and false recognition caused by illumination effects in the access control automation system installed at construction sites where these poor conditions frequently occur. This study investigated 3,000 images collected from actual construction sites, comparing the proposed method with the existing Faster R-CNN for license plates affected by various illumination environments, and found an accuracy improvement of more than 30%.

关键词： denoising autoencoder Faster R-CNN License Plate Recognition

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Latent-space embedding of expression data identifies gene signatures from sputum samples of asthmatic patients

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BMC BIOINFORMATICS 2020年第1期21卷 457-457页

作者： Lou, Shaoke Li, Tianxiao Spakowicz, Daniel Yan, Xiting Chupp, Geoffrey Lowell Gerstein, Mark Yale Univ Program Computat Biol & Bioinformat New Haven CT 06520 USA Yale Univ Dept Mol Biophys & Biochem New Haven CT 06520 USA Ohio State Univ Div Med Oncol Columbus OH 43210 USA Yale Sch Med Pulm & Crit Care New Haven CT 06520 USA

BackgroundThe pathogenesis of asthma is a complex process involving multiple genes and pathways. Identifying biomarkers from asthma datasets, especially those that include heterogeneous subpopulations, is challenging. Potentially, autoencoders provide ideal frameworks for such tasks as they can embed complex, noisy high-dimensional gene expression data into a low-dimensional latent space in an unsupervised fashion, enabling us to extract distinguishing features from expression ***, we developed a framework combining a denoising autoencoder and a supervised learning classifier to identify gene signatures related to asthma severity. Using the trained autoencoder with 50 hidden units, we found that hierarchical clustering on the low-dimensional embedding corresponds well with previously defined and clinically relevant clusters of patients. Moreover, each hidden unit has contributions from each of the genes, and pathway analysis of these contributions shows that the hidden units are significantly enriched in known asthma-related pathways. We then used genes that contribute most to the hidden units to develop a secondary random-forest classifier for directly predicting asthma severity. The feature importance metric from this classifier identified a signature based on 50 key genes, which are associated with severity. Furthermore, we can use these key genes to successfully estimate FEV1/FVC ratios across patients, via support-vector-machine *** found that the denoising autoencoder framework can extract meaningful patterns corresponding to functional gene groups and patient clusters from the gene expression of asthma patients.

关键词： Asthma Asthma subtypes denoising autoencoder Biomarker Non-invasive

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Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE

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FRONTIERS IN ENERGY RESEARCH 2024年 12卷

作者： Pu, Zengxin Huang, Yu Weng, Min Meng, Yang Zhao, Yunbin He, Gengsheng Guizhou Power Grid Co Ltd Elect Power Res Inst Guiyang Peoples R China Guizhou Power Grid Co Ltd Guiyang Power Supply Bur Guiyang Peoples R China Guizhou Power Grid Co Ltd Duyun Power Supply Bur Duyun Peoples R China China Southern Power Grid Energy Dev Res Inst Guangzhou Peoples R China

The construction of modern power system is key to achieving dual carbon goals, where non-intrusive load monitoring (NILM) plays a vital role in enhancing energy utilization efficiency and energy management. For example, to enable prosumers to better understand the extent of their flexible loads for demand response and peer-to-peer trading, it is essential to be aware of the types and states of loads using the method of NILM. To improve the predictive accuracy and implementation effectiveness of NILM technology, this paper proposes a novel NILM method integrating meteorological and calendar features. It delves deeply into the close connection between external factors such as temperature, precipitation, wind speed, and holidays, and the energy consumption of electrical appliances, constructing additional associative mappings in the training of the denoising autoencoder (DAE) model. Test results on the UK-DALE public dataset show that the NILM method proposed in this paper has significant advantages over traditional NILM methods that consider only single-dimensional electrical data features, in terms of load pattern recognition and accuracy in load energy consumption monitoring. This confirms the potential of multi-dimensional feature fusion technology in the application of NILM.

关键词： deep learning non-intrusive load monitoring denoising autoencoder weather feature calendar feature

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Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction

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ENERGIES 2021年第8期14卷 2163-2163页

作者： Berghout, Tarek Benbouzid, Mohamed Mouss, Leila-Hayet Univ Batna 2 Lab Automat & Mfg Engn Batna 05000 Algeria Univ Brest Inst RechercheDupuy Lome UMR CNRS 6027 F-29238 Brest France Shanghai Maritime Univ Logist Engn Coll Shanghai 201306 Peoples R China

Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long-short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path.

关键词： bearings prognosis remaining useful life data-driven knowledge-driven transfer learning labels information exploiting labels denoising autoencoder convolutional LSTM

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Electricity theft detection using big data and genetic algorithm in electric power systems

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期209卷 1页

作者： Shehzad, Faisal Javaid, Nadeem Aslam, Sheraz Javed, Muhammad Umar COMSATS Univ Islamabad Dept Comp Sci Islamabad 44000 Pakistan Cyprus Univ Technol Dept Elect Engn Comp Engn & Informat CY-3036 Limassol Cyprus

Non-technical losses (NTLs) are one of the major causes of revenue losses for electric utilities. In the literature, various machine learning (ML)/deep learning (DL) approaches are employed to detect NTLs. The existing studies are mostly concerned with tuning the hyperparameters of ML/DL methods for efficient detection of NTL, i.e., electricity theft detection. Some of them focus on the selection of prominent features from data to improve the performance of electricity theft detection. However, the curse of dimensionality affects the generalization ability of ML/DL classifiers and leads to computational, storage, and overfitting problems. Therefore, to deal with the above-mentioned issues, this study proposes a system based on metaheuristic techniques (artificial bee colony and genetic algorithm) and denoising autoencoder for electricity theft detection using big data in electric power systems. The former (metaheuristics) are used to select prominent features, while the latter is utilized to extract high variance features from electricity consumption data. Firstly, 11 new features are synthesized using statistical and electrical parameters from the user's consumption history. Then, the synthesized features are used as input to metaheuristic techniques to find a subset of optimal features. Finally, the optimal features are fed as input to the denoising autoencoder to extract features with high variance. The ability of both metaheuristic and autoencoder techniques to select and extract features is measured using a support vector machine. The proposed system reduces the overfitting, storage, and computational overhead of ML classifiers. Moreover, we perform several experiments to verify the effectiveness of our proposed system and results reveal that the proposed system has better performance than its counterparts.

关键词： Electricity theft detection Power systems Machine learning denoising autoencoder Metaheuristics PRECON SGCC

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Latent feature learning via autoencoder training for automatic classification configuration recommendation

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KNOWLEDGE-BASED SYSTEMS 2023年第1期261卷

作者： Deng, Liping Xiao, MingQing Southern Illinois Univ Carbondale Sch Math & Stat Sci Carbondale IL 62901 USA

The Combined Algorithm Selection and Hyperparameter Optimization problem, in short, CASH, seeks the most suitable classifiers and hyperparameters for the underlying classification problems. In current literature, the common approaches in dealing with CASH problem are conducted via search-based methods such as sequential model-based optimization (SMBO) along with various active tests. Different from current existing approaches, in this paper, we propose a new method by incorporating the so-called denoising autoencoder (DAE) approach into meta-learning (MtL) for automatic configuration (both algorithms and their hyperparameters) recommendation, which appears to be quite effective compared to standard search-based approaches. More specifically, we set up the configuration search space for CASH and produce the metadata, and generate the classification performance on a set of collected historical datasets. Then both encoder and decoder in the DAE system are trained with the masked metadata as inputs and the unmasked metadata as targets to extract the subtle latent variables of metadata and recover the unmasked inputs subsequently. Under our framework, the performance over the entire configuration space can be predicted effectively through two different settings, and the configuration with the highest predictive performance is thus recommended. The first recommendation approach is by inactivating some inputs and then to recover their entries via the trained encoder and decoder for new problems, while in the second approach, the relationship between the acquired latent variables and the meta-features of historical datasets via kernel multivariate multiple regression (MMR) is enacted, leading to the performance estimation of new datasets being pursued directly through MMR and the decoder of DAE without requiring any new configuration evaluations. An automatic classification configuration recommendation system, including 81 historical problems and 11 common classifiers with

关键词： Classifier and hyperparameter recommendation denoising autoencoder Meta-learning

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Supervised deep learning embeddings for the prediction of cervical cancer diagnosis

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PEERJ COMPUTER SCIENCE 2018年第5期4卷 e154页

作者： Fernandes, Kelwin Chicco, Davide Cardoso, Jaime S. Fernandes, Jessica INESC TEC Inst Engn Sistemas & Comp Tecnol & Ciencia Porto Portugal Univ Porto Porto Portugal Princess Margaret Canc Ctr Toronto ON Canada Univ Cent Venezuela Caracas Venezuela

Cervical cancer remains a significant cause of mortality all around the world, even if it can be prevented and cured by removing affected tissues in early stages. Providing universal and efficient access to cervical screening programs is a challenge that requires identifying vulnerable individuals in the population, among other steps. In this work, we present a computationally automated strategy for predicting the outcome of the patient biopsy, given risk patterns from individual medical records. We propose a machine learning technique that allows a joint and fully supervised optimization of dimensionality reduction and classification models. We also build a model able to highlight relevant properties in the low dimensional space, to ease the classification of patients. We instantiated the proposed approach with deep learning architectures, and achieved accurate prediction results (top area under the curve AUC = 0.6875) which outperform previously developed methods, such as denoising autoencoders. Additionally, we explored some clinical findings from the embedding spaces, and we validated them through the medical literature, making them reliable for physicians and biomedical researchers.

关键词： Dimensionality reduction Health-care informatics denoising autoencoder autoencoder Biomedical informatics Binary classification Deep learning Cervical cancer Artificial neural networks Health informatics

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