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SuperFeat:Quantitative feature learning from Single-cell RNA-seq Data Facilitates Drug Repurposing

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Genomics, Proteomics & Bioinformatics 2024年第3期22卷 91-105页

作者： Jianmei Zhong Junyao Yang Yinghui Song Zhihua Zhang Chunming Wang Renyang Tong Chenglong Li Nanhui Yu Lianhong Zou Sulai Liu Jun Pu Wei Lin State Key Laboratory for Oncogenes and Related Genes Department of CardiologyRenji HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghai Cancer InstituteShanghai 200127China Department of Laboratory Medicine Xin Hua HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghai 200092China Central Laboratory of Hunan Provincial People’s Hospital/The First Affiliated Hospital of Hunan Normal University Changsha 410005China

In this study,we devised a computational framework called Supervised feature learning and Scoring(SuperFeat)which enables the training of a machine learning model and evaluates the canonical cellular statuses/features in pathological tissues that underlie the progression of *** framework also enables the identification of potential drugs that target the presumed detrimental cellular *** framework was constructed on the basis of an artificial neural network with the gene expression profiles serving as input *** training data comprised single-cell RNA sequencing datasets that encompassed the specific cell lineage during the developmental progression of cell features.A few models of the canonical cancer-involved cellular statuses/features were tested by such ***,we illustrated the drug repurposing pipeline,utilizing the training parameters derived from the adverse cellular statuses/features,which yielded successful validation results both in vitro and in *** is accessible at https://***/weilin-genomics/rSuperFeat.

关键词： Single-cell transcriptomics Cell state transition Cell scoring Drug search feature learning

Deep learning,feature learning,and Clustering Analysis for SEM Image Classification

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Data Intelligence 2020年第4期2卷 513-528页

作者： Rossella Aversa Piero Coronica Cristiano De Nobili Stefano Cozzini National Research Council-Istituto Officina dei Materiali(CNR-IOM) 34136 TriesteItaly KIT-Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 176344 Eggenstein-LeopoldshafenGermany Research Software Engineering University of CambridgeCambridge CB30FAUK Freelance at *** Area Science Park Padriciano 9934149 TriesteItaly

In this paper,we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope(SEM).This is done by coupling supervised and unsupervised learning *** first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training ***,we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scales(from 1μm to 2μm).Finally,we compare different clustering methods to uncover intrinsic structures in the images.

关键词： Neural networks feature learning Clustering analysis Scanning Electron Microscope(SEM) Image classification

A spatial temporal neighborhood preserving method for feature learning with an industrial application

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A spatial temporal neighborhood preserving method for featur...

第32届中国过程控制会议(CPCC2021)

作者： Chenliang Liu Yalin Wang Kai Wang Xiaofeng Yuan School of Automation Central South University

Modern industrial process data often exhibit nonlinear and dynamic *** deep learning methods,such as stacked autoencoder(SAE),have excellent nonlinear feature learning capabilities,but they ignore the dynamic correlation between process *** learning based on manifold learning using neighborhood structure preserving has been widely used in industrial dynamic process ***,most of them extract linear features and the complex nonlinearities in process data are largely ***,a spatial temporal neighborhood preserving stack autoencoder(STNP-SAE) is proposed to learn static neighborhood features and dynamic neighborhood features of process data simultaneously in this ***,STNP-SAE is utilized to construct a soft sensor framework for quality *** effectiveness and prediction performance of the proposed method are validated on a practical hydrocracking process.

关键词： Spatial temporal neighborhood preserving feature learning stacked autoencoder quality prediction

A Tensorflow Based feature learning Method Application in Fault Detecting of Tract Motor

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A Tensorflow Based Feature Learning Method Application in Fa...

第30届中国控制与决策会议

作者： Wang Huizhong Qiao Linhan He Keke Lanzhou University of Technology

In purpose of detecting the inner and outer ring faults of tractor motor,one feature learning method,Variationa AutoEncoder,which based on Tensorflow,was cited to process the motor vibration *** method firstly normalized all data sets Next,these data sets were input into the built Variational AutoEncoder model to train the weights and biases as the feature learning is going ***,a Softmax Regression model is used for multi-faults *** final results showed that this method can be used fo finishing multi-faults detecting missions excellently,and for every metric,the results are betterthan traditional Back Propagation Neura Network,from 87.51% to 93.61%.Hence,this unsupervised feature learning method decreased lots of Machine learning model’s dependency on feature *** would be a good guidance of actual projects.

关键词： Tensorflow Variational AutoEncoder Softmax Regression feature learning Fault Detection

Industrial Process Fault Diagnosis Based on Contrastive Clustering-Assisted Discriminative feature learning

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Industrial Process Fault Diagnosis Based on Contrastive Clus...

第43届中国控制会议

作者： Sheng Wang Yinghua Han Qiang Zhao Jinkuan Wang Northeastern University Northeastern University at Qinhuangdao

In the realm of industrial process automation,the escalating complexity of systems and the surge of data from developments like the Industrial Internet of Things(IIoT) have underscored the urgent need for efficient and precise data-driven fault *** methods,despite advancements through deep learning,often struggle to differentiate between various fault *** issue is exacerbated in industrial contexts where acquiring and labeling fault data is a resource-intensive *** response,our innovative approach presents an self-supervised framework,namely contrastive clustering-assisted discriminative feature learning(CCDFL),that ingeniously combines a temporal gate encoder(TGE) for efficient time-series data processing and feature extraction,with a contrastive clustering-assisted discriminator(CCD) leveraging contrastive learning for improved feature *** synergistic fusion is crucial for accurately identifying and classifying different fault types,marking a significant advancement in intelligent manufacturing and fault *** is particularly adept at handling diverse and unforeseen faults,offering practical solutions in complex *** effectiveness of this method is further substantiated in the experimental section of our study.

关键词： Industrial processes Fault diagnosis Self-supervised feature learning

An unsupervised feature learning for quantum-classical convolutional network with applications to fault detection

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An unsupervised feature learning for quantum-classical convo...

第40届中国控制会议

作者： Tong Dou Kaiwei Wang Zhenwei Zhou Shilu Yan Wei Cui School of Automation Seience and Engineering South China University of Technology The Fifth Institute of Electronics Ministry of Industry and Information Technology

Combining the advantages of quantum computing and neural networks, quantum neural networks（QNNs） have gained considerable attention recently. However, because of the lack of quantum resource, it is costly to train QNNs. In this work, we presented a simple unsupervised method for quantum-classical convolutional networks to learn a hierarchy of quantum feature extractors. Each level of the resulting feature extractors consist of multiple quanvolution filters, followed by a pooling layer. The main contribution of the proposed approach is to use the K-means clustering to maximize the difference of quantum properties in quantum circuit ansatz. One experiment on the bearing fault detection task shows the effectiveness of the proposed method.

关键词： Quantum Computing feature learning K-means Fault Detection

Exploiting multi-channels deep convolutional neural networks for multivariate time series classification

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Frontiers of Computer Science 2016年第1期10卷 96-112页

作者： Yi ZHENG QiLIU Enhong CHEN Yong GE J. Leon ZHAO School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Department of Computer Science University of North Carolina at Charlotte Charlotte 28223 USA Department of Information Systems City University of Hong Kong Hong Kong China

Time series classification is related to many dif- ferent domains, such as health informatics, finance, and bioinformatics. Due to its broad applications, researchers have developed many algorithms for this kind of tasks, e.g., multivariate time series classification. Among the classifi- cation algorithms, k-nearest neighbor （k-NN） classification （particularly 1-NN） combined with dynamic time warping （DTW） achieves the state of the art performance. The defi- ciency is that when the data set grows large, the time con- sumption of 1-NN with DTW will be very expensive. In con- trast to 1-NN with DTW, it is more efficient but less ef- fective for feature-based classification methods since their performance usually depends on the quality of hand-crafted features. In this paper, we aim to improve the performance of traditional feature-based approaches through the feature learning techniques. Specifically, we propose a novel deep learning framework, multi-channels deep convolutional neu- ral networks （MC-DCNN）, for multivariate time series classi- fication. This model first learns features from individual uni- variate time series in each channel, and combines information from all channels as feature representation at the final layer. Then, the learnt features are applied into a multilayer percep- tron （MLP） for classification. Finally, the extensive experi- ments on real-world data sets show that our model is not only more efficient than the state of the art but also competitive in accuracy. This study implies that feature learning is worth to be investigated for the problem of time series classification.

关键词： convolutional neural networks time series clas-sification feature learning deep learning

Improving Semantic Part features for Person Re-identification with Supervised Non-local Similarity

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Tsinghua Science and Technology 2020年第5期25卷 636-646页

作者： Yifan Sun Zhaopeng Dou Yali Li Shengjin Wang the Department of Electronic Engineering Tsinghua UniversityBeijing 100084China

In person re-IDentification (re-ID) task,the learning of part-level features benefits from fine-grained *** facilitate part alignment,which is a prerequisite for learning part-level features,a popular approach is to detect semantic parts with the use of human parsing or pose *** methods of semantic partition do offer cues to good part alignment but are prone to noisy part detection,especially when they are employed in an off-the-shelf *** response,this paper proposes a novel part feature learning method for re-ID,that suppresses the impact of noisy semantic part detection through Supervised Non-local Similarity (SNS) *** several detected semantic parts,SNS first locates their center points on the convolutional feature maps for use as a set of anchors and then evaluates the similarity values between these anchors and each pixel on the feature *** non-local similarity learning is supervised such that:each anchor should be similar to itself and simultaneously dissimilar to any other anchors,thus yielding the ***,each anchor absorbs features from all of the similar pixels on the convolutional feature maps to generate a corresponding part feature (SNS feature).We evaluate our method with extensive experiments conducted under both holistic and partial re-ID *** results confirm that SNS consistently improves re-ID accuracy using human parsing or pose estimation,and that our results are on par with state-of-the-art methods.

关键词： person re-identification non-local similarity feature learning semantic parts

Anchor-based manifold binary pattern for finger vein recognition

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Science China(Information Sciences) 2019年第5期62卷 129-144页

作者： Haiying LIU Gongping YANG Lu YANG Kun SU Yilong YIN School of Software Shandong University School of Computer Science and Technology Shandong University of Finance and Economics

This paper proposes a novel learning method of binary local features for recognition of the finger vein. The learning methods existing in local features for image recognition intend to maximize the data variance, reduce quantitative errors, exploit the contextual information within each binary code, or utilize the label information, which all ignore the local manifold structure of the original data. The manifold structure actually plays a very important role in binary code learning, but constructing a similarity matrix for large-scale datasets involves a lot of computational and storage cost. The study attempts to learn a map, which can preserve the manifold structure between the original data and the learned binary codes for large-scale situations. To achieve this goal, we present a learning method using an anchor-based manifold binary pattern(AMBP) for finger vein recognition. Specifically, we first extract the pixel difference vectors(PDVs) in the local patches by calculating the differences between each pixel and its neighbors. Second,we construct an asymmetric graph, on which each data point can be a linear combination of its K-nearest neighbor anchors, and the anchors are randomly selected from the training samples. Third, a feature map is learned to project these PDVs into low-dimensional binary codes in an unsupervised manner, where(i) the quantization loss between the original real-valued vectors and learned binary codes is minimized and(ii) the manifold structure of the training data is maintained in the binary space. Additionally, the study fuses the discriminative binary descriptor and AMBP methods at the image representation level to further boost the performance of the recognition system. Finally, experiments using the MLA and PolyU databases show the effectiveness of our proposed methods.

关键词： finger vein recognition feature learning local linear embedding fusion manifold learning anchor