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BDLR:lncRNA identification using ensemble learning

BIOCELL 2022年第4期46卷 951-960页

作者： LEJUN GONG SHEHAI ZHOU JINGMEI CHEN YONGMIN LI LI ZHANG ZHIHONG GAO Jiangsu Key Lab of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China

Long non-coding RNAs(lncRNAs)play an important role in many life activities such as epigenetic material regulation,cell cycle regulation,dosage compensation and cell differentiation regulation,and are associated with many human *** are many limitations in identifying and annotating lncRNAs using traditional biological experimental *** the development of high-throughput sequencing technology,it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning *** on the Bagging method and Decision Tree algorithm in ensemble learning,this paper proposes a method of lncRNAs gene sequence identification called *** identification results of this classification method are compared with the identification results of several models including Byes,Support Vector Machine,Logical Regression,Decision Tree and Random *** experimental results show that the lncRNAs identification method named BDLR proposed in this paper has an accuracy of 86.61%in the human test set and 90.34%in the mouse for lncRNAs,which is more than the identification results of the other ***,the proposed method offers a reference for researchers to identify lncRNAs using the ensemble learning.

关键词： lncRNAs High-throughput sequencing Ensemble learning Bagging Decision Tree

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Learning Disentangled Latent Factors for Individual Treatment Effect Estimation Using Variational Generative Adversarial Nets 25

Learning Disentangled Latent Factors for Individual Treatmen...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Bao, Qingsen Mao, Zeyong Chen, Lei Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (纸本)9781665405270

Estimating individual treatment effect (ITE) is a challenging task due to the need for individual potential outcomes to be learned from biased data and counterfactuals are inherently unobservable. Some researchers propose to use generative adversarial approaches to infer the counterfactual outcomes based on the distribution of factual outcomes. However, these methods assume that complete confounding factors are observed, and simply treat all observed variables as confounding factors, ignoring identification of possible instrumental factors and adjustment factors, which will bring large deviation to ITE estimation when facing biased data. To address these issues, we propose a novel Variational Generative Adversarial Nets for ITE estimation by designing the collaborative learning strategy with Variational AutoEncoder (VAE) and Generative Adversarial Nets (GAN). Specifically, we employ VAE to infer the latent representations of observed variables to access complete latent factors while using GAN to infer unseen counterfactual outcomes and guide VAE for disentangling these latent factors into three sets corresponding to the instrumental, confounding, and adjustment factors. Then the disentangled latent confounding factors can be used to further control data bias using an adaptive weighting scheme. Extensive experiments on real and synthetic data demonstrate learning disentangled latent factors for ITE estimation is effective, and our method has excellent performance even with high data bias. © 2022 IEEE.

关键词： Generative adversarial networks

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Community Detection Based on Deep Network Embedding with Dual Self-supervised Training 3rd

Community Detection Based on Deep Network Embedding with Dua...

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3rd International Conference on big data and security, ICBDS 2021

作者： Chen, Yunfang Mao, Haotian Wang, Li Zhang, Wei School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China

ISBN: (纸本)9789811908514

We propose a community discovery method based on deep auto-encoding (DGAE_DST). Firstly, we use the pre-trained two-layer neural network and k-means algorithm to initialize the centroid vector, and then use the DNN module and the GCN module to capture the structure features and attributes respectively. The attribute information is fed to the GCN layer to integrate the structure information and attributes. Finally, DNN and GCN respectively output the reconstructed adjacency matrix and feature matrix, and minimize their loss from the original matrix. The experimental results on four datasets show that the DGAE_DST method has higher stability and accuracy. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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An adaptive flow table adjustment algorithm for SDN 21

An adaptive flow table adjustment algorithm for SDN

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Xu, Xiaolong Hu, Liuyun Lin, Haowei Fan, Zexuan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728120584

SDN (Software-defined networking) are important for current network systems, such as cloud systems. The characteristics of flow in SDN and the impact of flow table entries and controllers on data packet transmission are analyzed. We propose an adaptive flow table adjustment algorithm for SDN called CFC (Combine flow table and controller). It is important for CFC to monitor the network traffic in real time. According to the proportion of active flow entries in the OpenFlow switch, the value of idle-timeout will be set dynamically based on the characteristics of different data flows. When the switch forwards packets, it will minimize the calculation load of controller, and allocate a reasonable value of idle-timeout to the idle flow table entries. So it can delete discarded flow table entries in time and improve the hit rate of data packets. The experiments show that the utilization rate of the flow table resource is enhanced. With the improvement of data packets' forwarding rate, the average throughput of the network has also increased. At the same time, it can also cope with the burst traffic in SDN and ensure them to be forwarded normally and effectively. © 2019 IEEE.

关键词： Controllers

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TSC-ECFA:A Trusted Service Composition Scheme for Edge Cloud 27

TSC-ECFA:A Trusted Service Composition Scheme for Edge Cloud

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27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Jiang, Yu Xu, Xiaolong Lin, Kunda Duan, Weihua Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781665408783

In order to select a composition scheme that meets user's needs and high performance from large-scale web services in the edge cloud, this paper proposes a trusted service composition optimization scheme called TSC-ECFA for edge cloud, which applies the predation strategy to the firefly algorithm (FA) and divides the services with the same input and output into one category, reducing the number of combinations to search for feasible solutions. The cotangent chaos theory is used to generate the initial firefly population and disturb the location of the individual to improve the overall search efficiency of FA. This paper also improves the step factor and attractiveness formula of FA to give full play to the detection ability of the step factor in the early stage of the algorithm, and balance the local search and the global search. Considering that the QoS attribute values of the service are vulnerable to be tampered with, the blockchain is used to store the QoS attribute values to ensure the tamper-proof and reliability of the QoS attribute value. Finally, simulation experiments compare the number of iterations and optimization of the three algorithms. The experimental results show that the comprehensive performance of TSC-ECFA is better than other algorithms. © 2021 IEEE.

关键词： Quality of service

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APDPk-means: A new differential privacy clustering algorithm based on arithmetic progression privacy budget allocation 21

APDPk-means: A new differential privacy clustering algorithm...

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作者： Fan, Zexuan Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security AND Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728120584

How to protect users' private data during network data mining has become a hot issue in the fields of big data and network information security. Most current researches on differential privacy k-means clustering algorithms focus on optimizing the selection of initial centroids. However, the traditional privacy budget allocation has the problem that the random noise becomes too large as the number of iterations increases, which will reduce the performance of data clustering. To solve the problem, we improved the way of privacy budget allocation in differentially private clustering algorithm DPk-means, and proposed APDPk-means, a new differential privacy clustering algorithm based on arithmetic progression privacy budget allocation. APDPk-means decomposes the total privacy budget into a decreasing arithmetic progression, allocating the privacy budgets from large to small in the iterative process, so as to ensure the rapid convergence in early iteration. The experiment results show that compared with the other differentially private k-means algorithms, APDPk-means has better performance in availability and quality of the clustering result under the same level of privacy protection. © 2019 IEEE.

关键词： data mining

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Design Guidance for Lightweight Object Detection Models 3rd

Design Guidance for Lightweight Object Detection Models

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3rd International Conference on big data and security, ICBDS 2021

作者： Wang, Rui Wang, Xueli Chen, Yunfang Zhang, Wei School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China

ISBN: (纸本)9789811908514

The lightweight target detection model is deployed in an environment with limited computing power and power consumption, which is widely used in many fields. Most of the current lightweight technologies only focus on a few steps of the model implementation and lack a global perspective. Therefore, this paper proposes a general lightweight model implementation framework, including network construction indicators, lightweight backbone network design, and model optimization. By analyzing the complexity indicator of network structure, the factors that affect network performance such as depth and width are summarized. On this basis, combine the One-Shot Aggregation (OSA) idea and Cross-Stage Partial Network (CSPNet) transformation to construct a general lightweight detection network CSPOSA. Further specific optimization strategies are proposed to prune the network structure and training process. For the network structure, the width and depth of the network are adjusted, the amount of parameters of the model is compressed. The training process is divided into the first, middle and last three stages to improve the detection performance of the model without adding extra computation. Taking embedded platform helmet detection as the experimental scene, the parameter amount of the realized model is 1/10 of the mainstream models YOLOv3 and YOLOv4, and the detection accuracy is similar, so it is more suitable for deployment on devices with limited computing power. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Computing power

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Identifying Outliers in Astronomical Images with Unsupervised Machine Learning

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Research in Astronomy and Astrophysics 2022年第8期22卷 74-84页

作者： Yang Han Zhiqiang Zou Nan Li Yanli Chen School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210023China Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

Astronomical outliers,such as unusual,rare or unknown types of astronomical objects or phenomena,constantly lead to the discovery of genuinely unforeseen knowledge in *** unpredictable outliers will be uncovered in principle with the increment of the coverage and quality of upcoming survey ***,it is a severe challenge to mine rare and unexpected targets from enormous data with human inspection due to a significant *** learning is also unsuitable for this purpose because designing proper training sets for unanticipated signals is *** by these challenges,we adopt unsupervised machine learning approaches to identify outliers in the data of galaxy images to explore the paths for detecting astronomical *** comparison,we construct three methods,which are built upon the k-nearest neighbors(KNN),Convolutional Auto-Encoder(CAE)+KNN,and CAE+KNN+Attention Mechanism(att CAE_KNN)*** sets are created based on the Galaxy Zoo image data published online to evaluate the performance of the above *** show that att CAE_KNN achieves the best recall(78%),which is 53%higher than the classical KNN method and 22%higher than CAE+*** efficiency of att CAE_KNN(10 minutes)is also superior to KNN(4 h)and equal to CAE+KNN(10 minutes)for accomplishing the same ***,we believe that it is feasible to detect astronomical outliers in the data of galaxy images in an unsupervised ***,we will apply att CAE_KNN to available survey data sets to assess its applicability and reliability.

关键词： Galaxy Physical data and Processes Galaxy:fundamental parameters

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A Fraud Detection Approach Based on Combined Feature Weighting 10th

A Fraud Detection Approach Based on Combined Feature Weighti...

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10th EAI International Conference on Cloud Computing, CloudComp 2020

作者： Liu, Xiaoqian Yu, Chenfei Xia, Bin Gu, Haiyan Wang, Zhenli Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing210031 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Nanjing China Jiangsu Provincial Public Security Department Key Laboratory of Digital Forensics Nanjing China

ISBN: (纸本)9783030699918

data mining technology has yielded fruitful results in the area of crime discovery and intelligent decision making. Credit card is one of the most popular payment methods, providing great convenience and efficiency. However, due to the vulnerabilities of credit card transactions, criminals are able to commit fraud to infringe on the interests of the state and citizens. How to discover potential fraudsters while guaranteeing high efficiency becomes an extremely valuable problem to solve. In this work, we talk about the advantages and disadvantages of different models to detect credit card fraud. We first introduce the data preprocessing measures for handling imbalanced fraud detection dataset. Then we compare related models to implement fraudster recognition. We also propose a feature selection approach based on combined feature weights. Some future research interests are also envisioned. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Crime

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Model-Based Offline Policy Optimization with Adversarial Network 26

Model-Based Offline Policy Optimization with Adversarial Net...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Yang, Junming Chen, Xingguo Wang, Shengyuan Zhang, Bolei Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China School of Computer Science Nanjing University of Posts and Telecommunications China School of Modern Posts Nanjing University of Posts and Telecommunications China

ISBN: (纸本)9781643684369

Model-based offline reinforcement learning (RL), which builds a supervised transition model with logging dataset to avoid costly interactions with the online environment, has been a promising approach for offline policy optimization. As the discrepancy between the logging data and online environment may result in a distributional shift problem, many prior works have studied how to build robust transition models conservatively and estimate the model uncertainty accurately. However, the over-conservatism can limit the exploration of the agent, and the uncertainty estimates may be unreliable. In this work, we propose a novel Model-based Offline policy optimization framework with Adversarial Network (MOAN). The key idea is to use adversarial learning to build a transition model with better generalization, where an adversary is introduced to distinguish between in-distribution and out-of-distribution samples. Moreover, the adversary can naturally provide a quantification of the model's uncertainty with theoretical guarantees. Extensive experiments showed that our approach outperforms existing state-of-the-art baselines on widely studied offline RL benchmarks. It can also generate diverse in-distribution samples, and quantify the uncertainty more accurately. © 2023 The Authors.

关键词： Reinforcement learning

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