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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

Biophysics Reports 2023年第2期9卷 57-66页

作者： Peng Chen Zhenlei Li Zhaolin Hong Haoran Zheng Rong Zeng School of Computer Science and Technology University of Science and Technology of China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Department of Systems Biology University of Science and Technology of China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences School of Life Science and Technology ShanghaiTech University

Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasetsbased semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles:(1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning;(2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality;(3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas(TCGA) and the Gene Expression Comprehensive Database(GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.

关键词： Deep learning Tumor type classification Cancer-specific biomarkers MSSL

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Zone routing protocol based on energy consumption

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Jisuanji Gongcheng/Computer Engineering 2006年第12期32卷 117-118+135页

作者： Zhai, Weifeng Gu, Naijie Zhang, Jianming Department of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Computing and Communication Software Hefei 230027 China

A zone routing protocol based on the energy consumption speed of nodes in mobile Adhoc networks is presented. A route select parameter computed by residual lifetime of nodes and hop count of route is used to be selected the best route, the nodes that have more energy have more probability of chosen. The mechanism can balance the energy consumption of node and prolong network's life. Simulation result comparing with zone routing protocol in NS-2 shows EZRP has less number of death nodes, lower routing overhead, and longer lifetime of network.

关键词： Energy consumption EZRP Hybrid routing protocol MANET

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Reversible Data Hiding Based on Automatic Contrast Enhancement Using Histogram Expansion

SSRN

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SSRN 2022年

作者： Lyu, WanLi Yue, YaJie Yin, Zhaoxia Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui Medical University 230601 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui Medical University 230601 China School of Communication & Electronic Engineering East China Normal University Shanghai200241 China

This paper proposes a novel ACERDH method using histogram expansion. Considering the local properties of the histogram, when an appropriate range rather than a global location is selected, the contrast of the whole image is improved. Meanwhile, the global average brightness is used as a reference to determine the shift direction to prevent the image from being over-enhanced. Moreover, the contrast can be improved adaptively when a reasonable number of data is embedded at selected embedding points. Experimental results show that compared with state-of-the-art methods, when the payloads are the same, the proposed method achieves better contrast and maintains good visual quality. © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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An adaptive strategy for updating the memory in Evolutionary Algorithms for dynamic optimization

An adaptive strategy for updating the memory in Evolutionary...

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IEEE Symposium on computational Intelligence in Dynamic and Uncertain Environments (CIDUE)

作者： Tao Zhu Wenjian Luo Zhifang Li Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology Hefei Anhui China University of Science and Technology of China Hefei Anhui CN

The memory scheme is one of the most widely employed techniques in Evolutionary Algorithms for solving dynamic optimization problems. The updating strategy is a key concern for the memory scheme. Unfortunately, the existent memory updating strategies neglect the characteristics of the memory updating behaviors, and sometimes this could lead results against the original intention. In this paper, a novel updating strategy is proposed, which can adaptively update the memory according to the characteristics of the memory updating behaviors. Experiments are carried out in different kinds of dynamic environments, and the experimental results show that the proposed strategy is better than the traditional strategies.

关键词： Memory management Heuristic algorithms Optimization Generators Evolutionary computation Associative memory Redundancy

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A qualitative modeling and simulation method based on cloud model

A qualitative modeling and simulation method based on cloud ...

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2013 5th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2013

作者： Shao, Chenxi Sun, Kelu Zhenzhong, Shao Computer Science and Technology Dept. University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027 China Jianghuai Automobile Co. Ltd. Hefei 230022 China

ISBN: (纸本)9781467358149

Qualitative simulation is effective methods processing incomplete knowledge, how to represent qualitative knowledge and reduce spurious behaviors is its research emphasis. Contraposing the limitations of existing methods representing qualitative knowledge and filtering spurious behaviors, an approach is proposed to qualitatively model and simulate on the basis of normal cloud model. In this research, the cloud numeric characteristics and cloud measurement are introduced into this approach;consequently, it can more accurately assess the qualitative knowledge, and expand the variables expression and quantity space;meanwhile, it is constructed the means of modeling and simulation by adopting more amply portraying the system status, more intuitively transforming status and more accurately filtering spurious behaviors. Furthermore, in simulation algorithm, cloud measurement indices are employed to significantly select the successor status which can not be determined by existing constraint means. The results of simulation example demonstrate that our approach effectively improves the ability to filter spurious behaviors, and it is a feasible method. © 2013 IEEE.

关键词： Cloud computing

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Scheduling aircraft landing based on clonal selection algorithm and receding horizon control

Scheduling aircraft landing based on clonal selection algori...

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11th International IEEE Conference on Intelligent Transportation Systems, ITSC 2008

作者： Jia, Xiaolan Cao, Xianbin Guo, Yuanping Qiao, Hong Zhang, Jun University of Science and Technology of China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230026 China Institute of Automation Chinese Academy of Sciences Beijing 100080 China School of Electronic and Information Engineering Beihang University Beijing 100083 China

ISBN: (纸本)9781424421121

The task of Aircraft Landing Scheduling (ALS) is to give a landing sequence and landing times for a given set of aircrafts where many constraints must be satisfied. ALS is an NP-hard problem with large-scale and multi-constraint characteristics, thus it is hard to find optimal solution efficiently. In this paper, a hybrid algorithm of Clonal Selection Algorithm (CSA) and Receding Horizon Control (RHC) was proposed for ALS problem. In details, constrained CSA based on infeasibility degree (IFD) schedules aircrafts in current receding horizon, and then RHC repeats that optimization procedure using excellent gene segment spread (EGSS) until all aircrafts have landed. Comparative experiments show that the CSA-RHC hybrid algorithm is able to obtain an optimal landing sequence and landing times rapidly and effectively. © 2008 IEEE.

关键词： computational complexity

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NURBS model for chaotic time series

NURBS model for chaotic time series

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2011 3rd International Conference on Computer Research and Development, ICCRD 2011

作者： Shao, Chenxi Xiao, Lipeng Department of Computer Science and Technology University of Science and Technology of China Hefei China Department of Modern Physics Nonlinear Science Center University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Software in Computing and Communication Hefei China

ISBN: (纸本)9781612848372

NURBS method is extensively used in the field of Geometric Modeling. Since the original NURBS method does not have time factor, it can not display the huge advantage in the dynamics field. In this article, inspired by the segmented nature of basis functions, we propose a time factor method which can be directly applied in reconstruction of missing data, with remarkable performance in matching the original data. This work does not only give an expression of chaotic time series, but also highlights a possible way for the challenge in reconstruction of missing data. © 2011 IEEE.

关键词： Time series

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Extracting Qualitative States from Nonlinear Time Series Using Integration of Fuzzy c-Means and Hierarchical Clustering

Extracting Qualitative States from Nonlinear Time Series Usi...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Chenxi Shao Jinfeng Fan Xiaoping Chen Department of Computer Science and Technology University of Science and Technology of China Hefei Anhui China Anhui Province Key Laboratory of Software in Computing and Communication Hefei Anhui China

A novel method combined fuzzy c-means (FCM) and hierarchical agglomerative clustering (HAC) is presented to extract the qualitative states from reconstructed phase space by considering the dynamical relations of underlying nonlinear system. The qualitative state, in which all points have similar properties, is characterized by three measures, linear density, temporal reachability, and stable degree, which are defined as the criteria of merging subclasses. The time series sampled from a classic nonlinear system, Lorenz system, and electroencephalogram (EEG) signals are used to test this method. The results show that the qualitative states can be efficiently distinguished from these time series by the proposed method.

关键词： Space technology Nonlinear systems Clustering methods Fuzzy systems Merging Electroencephalography Chaotic communication Delay Computer science Laboratories

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Error-minimization approach to reduce protease bias 2

Error-minimization approach to reduce protease bias

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2nd International Conference on Biological Information and Biomedical Engineering, BIBE 2018

作者： Chen, Chong Zheng, Haoran School of Computer Science and Technology University of Science and Technology of China Hefei China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei China Department of Systems Biology University of Science and Technology of China Hefei China

ISBN: (纸本)9783800747276

The main goal in proteomics is to describe the proteome in a comprehensive and accurate way, and enzymolysis is a key step in the process of large-scale proteomics experiments. For the same protein samples, using different proteases may introduce a strong bias into protein analysis results. This situation will make proteomics experiments irreproducible and will have a serious impact on the experimental results. To reduce this protease bias, we present EMQ (Error-Minimization-based Quantification for Protein), an approach based on error minimization. In contrast to the existing method, our model includes information on shared peptides, which are ubiquitous in mass spectrometric datasets. The abundance of shared peptides will be assigned to related proteins accurately, thereby determining the protein abundance. Based on this feature, we used the proposed model with several datasets, and compared its performance with the other. Finally, our model is shown to have better performance than other approach (average Pearson correlation of ~0.83 obtained by EMQ, while ~0.63 by other model). © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Peptides

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AN APPROACH BASED ON TSA-TREE FOR ACCURATE TIME SERIES CLASSIFICATION

AN APPROACH BASED ON TSA-TREE FOR ACCURATE TIME SERIES CLASS...

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2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems

作者： Xiaoxu He Chenxi Shao School of Computer Science and Technology University of Science and Technology of China Anhui Province Key Laboratory of Software in Computing and Communication

In order to improve the performance of time series classification, we introduce a new approach of time series classification. The first step of the approach is to design a feature exaction model based on Trend and Surprise Abstraction tree （TSA-tree）. The second step of the approach is to combine the exacted global feature and 1 nearest neighbor to classify time series. The proposed approach is compared with a number of known classifiers by experiments in artificial and real-world data sets. The experimental results show it can reduce the error rates of time series classification, so it is highly competitive with previous approaches.

关键词： Feature exaction dimension reduction classification TSA-tree

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