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学者姓名：韩九强
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Abstract ：
Aimed at the challenge of low accuracy of building segmentation caused by poor continuity of remote-sensing-image regions and blurred boundaries, a remote sensing building semantics segmentation algorithm based on multiscale regional consistent attention supervision is proposed. First, based on the Unet encoder-decoder architecture, the proposed algorithm constructs the region attention network (ReA-Net), which employs a multiscale receptive field-guidance model to simultaneously focus on regional features and edge details of remote sensing image objects. Second, the self-attention mechanism is employed to establish the correlation representation of regional-level features of remote sensing images, and multiscale regional attention features of remote sensing images are obtained through weighted regional-level correlation mapping. Finally, to address the lack of spatial correlation constraints on the prediction of remote sensing images segmentation, a loss function with multiscale neighborhood consistency supervision is suggested to constrain the consistency of pixel label assignment related to a local region. Experimental results on WHU building dataset showed that intersection over union (IOU) reached 91.6%, precision reached 95.61%, recall reached 95.68% recall, and F1-score reached 95.64%; On the Massachusetts building dataset, IOU reached 74.77% and precision reached 83.93%, recall reached 87.53%, and F1-score reached 85.69%. Therefore, the proposed algorithm not only has a good segmentation effect but also has a strong robustness for remote sensing building image segmentation.
Keyword ：
Buildings Building segmentation Correlation Feature extraction Image segmentation neighborhood consistency supervision region attention Remote sensing remote sensing image Semantics Sensors U-Net
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| GB/T 7714 | Xu, Shengjun , Deng, Bowen , Meng, Yuebo et al. ReA-Net: A Multiscale Region Attention Network With Neighborhood Consistency Supervision for Building Extraction From Remote Sensing Image [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2022 , 15 : 9033-9047 . |
| MLA | Xu, Shengjun et al. "ReA-Net: A Multiscale Region Attention Network With Neighborhood Consistency Supervision for Building Extraction From Remote Sensing Image" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 15 (2022) : 9033-9047 . |
| APA | Xu, Shengjun , Deng, Bowen , Meng, Yuebo , Liu, Guanghui , Han, Jiuqiang . ReA-Net: A Multiscale Region Attention Network With Neighborhood Consistency Supervision for Building Extraction From Remote Sensing Image . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2022 , 15 , 9033-9047 . |
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Abstract ：
The spatial interaction of chromosomes is regarded as an important issue affecting the regulation of gene expression, and the high-throughput chromosome conformation capture (Hi-C) technology has become the primary tool to explore the temporal and spatial interactions of chromosomes in three-dimensional genomics. With the continuous accumulation of Hi-C samples and the increasing complexity of pipelines, the bioinformatic analysis of Hi-C data has been considered an opportunity and a challenge for understanding the spatial regulation mechanism of gene expression. In this paper, the current status and development outline of bioinformatic methods for Hi-C data are introduced, including data normalization, multi-level structure analysis, data visualization and 3D modeling, especially of multi-level structure at A/B compartments, topological associated domains (TADs) and chromain looping levels. Based on this, we provide the outlook of future hotspots and trends in this area. Hopefully our insight will be beneficial for the exploration of gene expression regulation from the traditional linear model to the 3D mode.染色体的空间交互作用被视为影响基因表达调控的重要因素,高通量染色体构象捕获(high-throughput chromosome conformation capture, Hi-C)技术已成为3D基因组学中探索染色体空间交互作用的主要实验手段之一。随着Hi-C样本数据的持续累积以及分析处理流程复杂度的不断提升,基于生物信息学的Hi-C数据分析对探究基因表达的时空调控机制而言,是机遇也是挑战。本文从生物信息学角度,综合阐述了Hi-C的国内外研究现状及发展动态,包括数据标准化、多级结构分析、数据可视化以及三维建模,重点剖析了多级结构中的A/B区室(A/B compartments)、拓扑相关域(topological associated domains, TADs)和染色质环(chromain looping),在此基础上分析了该方向未来可能的研究热点及发展趋势,以期为将基因表达调控的探索从传统线性空间进一步拓展到三维结构空间提供支持。.
Keyword ：
3D genomics bioinformatics Hi-C
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| GB/T 7714 | Lyu Hong Qiang , Hao Le le , Liu Er Hu et al. Current status and future perspectives in bioinformatical analysis of Hi-C data. [J]. | Hereditas , 2020 , 42 (1) : 87-99 . |
| MLA | Lyu Hong Qiang et al. "Current status and future perspectives in bioinformatical analysis of Hi-C data." . | Hereditas 42 . 1 (2020) : 87-99 . |
| APA | Lyu Hong Qiang , Hao Le le , Liu Er Hu , Wu Zhi Fang , Han Jiu Qiang , Liu Yuan . Current status and future perspectives in bioinformatical analysis of Hi-C data. . | Hereditas , 2020 , 42 (1) , 87-99 . |
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Abstract ：
提出了一种基于多引导滤波器的单幅图像超分辨率方法.首先,该方法通过大量的自然图像建立高低分辨率图像块样本训练库,并通过聚类算法将具有相似性质的高低分辨率样本块进行聚类;其次,将输入低分辨率图像进行重叠分块,并在样本库中搜索最近邻的高低分辨率样本聚类;再次,将输入低分辨率图像块作为输入图像,与样本库中最近邻的低分辨率聚类样本作为引导图像,运用本文提出的多引导滤波器计算引导滤波器的参数;最后,利用样本库中最近邻的高分辨率聚类样本和引导滤波器的参数,通过多引导滤波器就可以重构高分辨率图像.实验结果表明,本文算法不仅能很好地重构图像的高频细节,还能很好地恢复图像的纹理特征.
Keyword ：
超分辨率 高频细节 样本训练库 引导滤波器
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| GB/T 7714 | 刘哲 , 韩九强 , 黄世奇 . 基于多引导滤波器的单幅图像超分辨率技术 [J]. | 红外技术 , 2017 , (10) : 920-927 . |
| MLA | 刘哲 et al. "基于多引导滤波器的单幅图像超分辨率技术" . | 红外技术 10 (2017) : 920-927 . |
| APA | 刘哲 , 韩九强 , 黄世奇 . 基于多引导滤波器的单幅图像超分辨率技术 . | 红外技术 , 2017 , (10) , 920-927 . |
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Keyword ：
超分辨率 高频细节 样本训练库 引导滤波器
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| GB/T 7714 | 刘哲 , 韩九强 , 黄世奇 . 基于多引导滤波器的单幅图像超分辨率技术 [J]. | 红外技术 , 2017 , (10) . |
| MLA | 刘哲 et al. "基于多引导滤波器的单幅图像超分辨率技术" . | 红外技术 10 (2017) . |
| APA | 刘哲 , 韩九强 , 黄世奇 . 基于多引导滤波器的单幅图像超分辨率技术 . | 红外技术 , 2017 , (10) . |
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Abstract ：
Integrase catalytic domain (ICD) is an essential part in the retrovirus for integration reaction, which enables its newly synthesized DNA to be incorporated into the DNA of infected cells. Owing to the crucial role of ICD for the retroviral replication and the absence of an equivalent of integrase in host cells, it is comprehensible that ICD is a promising drug target for therapeutic intervention. However, annotated ICDs in UniProtKB database have still been insufficient for a good understanding of their statistical characteristics so far. Accordingly, it is of great importance to put forward a computational ICD model in this work to annotate these domains in the retroviruses. The proposed model then discovered 11,660 new putative ICDs after scanning sequences without ICD annotations. Subsequently in order to provide much confidence in ICD prediction, it was tested under different cross-validation methods, compared with other database search tools, and verified on independent datasets. Furthermore, an evolutionary analysis performed on the annotated ICDs of retroviruses revealed a tight connection between ICD and retroviral classification. All the datasets involved in this paper and the application software tool of this model can be available for free download at http://sourceforge.net/project/icdtool/files/?source-navbar. (C) 2017 Elsevier Ltd. All rights reserved.
Keyword ：
Position weight matrix Pseudo amino acid composition Random forest Support vector machine
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| GB/T 7714 | Wu, Sijia , Han, Jiuqiang , Zhang, Xinman et al. A computational model for predicting integrase catalytic domain of retrovirus [J]. | JOURNAL OF THEORETICAL BIOLOGY , 2017 , 423 : 63-70 . |
| MLA | Wu, Sijia et al. "A computational model for predicting integrase catalytic domain of retrovirus" . | JOURNAL OF THEORETICAL BIOLOGY 423 (2017) : 63-70 . |
| APA | Wu, Sijia , Han, Jiuqiang , Zhang, Xinman , Zhong, Dexing , Liu, Ruiling . A computational model for predicting integrase catalytic domain of retrovirus . | JOURNAL OF THEORETICAL BIOLOGY , 2017 , 423 , 63-70 . |
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Abstract ：
Regulatory single nucleotide polymorphisms (rSNPs), kind of functional noncoding genetic variants, can affect gene expression in a regulatory way, and they are thought to be associated with increased susceptibilities to complex diseases. Here a novel computational approach to identify potential rSNPs is presented. Different from most other rSNPs finding methods which based on hypothesis that SNPs causing large allele-specific changes in transcription factor binding affinities are more likely to play regulatory functions, we use a set of documented experimentally verified rSNPs and nonfunctional background SNPs to train classifiers, so the discriminating features are found. To characterize variants, an extensive range of characteristics, such as sequence context, DNA structure and evolutionary conservation etc. are analyzed. Support vector machine is adopted to build the classifier model together with an ensemble method to deal with unbalanced data. 10-fold cross-validation result shows that our method can achieve accuracy with sensitivity of similar to 78% and specificity of similar to 82%. Furthermore, our method performances better than some other algorithms based on aforementioned hypothesis in handling false positives. The original data and the source matlab codes involved are available at https://sourceforge.net/ projects/rsnppredict/.
Keyword ：
Hydroxyl radical cleavage patterns Imbalanced data Position weight matrix Support vector machine
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| GB/T 7714 | Li, Rong , Zhong, Dexing , Liu, Ruiling et al. A novel method for in silico identification of regulatory SNPs in human genome [J]. | JOURNAL OF THEORETICAL BIOLOGY , 2017 , 415 : 84-89 . |
| MLA | Li, Rong et al. "A novel method for in silico identification of regulatory SNPs in human genome" . | JOURNAL OF THEORETICAL BIOLOGY 415 (2017) : 84-89 . |
| APA | Li, Rong , Zhong, Dexing , Liu, Ruiling , Lv, Hongqiang , Zhang, Xinman , Liu, Jun et al. A novel method for in silico identification of regulatory SNPs in human genome . | JOURNAL OF THEORETICAL BIOLOGY , 2017 , 415 , 84-89 . |
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Abstract ：
Accurate identification of functionally relevant variants against the ubiquitous background genetic variations is a significant challenge facing bioinformatics researchers and the challenge becomes more severe for non-coding variants. In this study, a novel computational method to identify candidate disease-associated non-coding single nucleotide polymorphisms (SNPs) of human genome is presented. To characterize SNPs, an extensive range of features, such as sequence context, DNA structure, evolutionary conservation and histone modification signals etc. are extracted. Then random forest is adopted to build the classifier model together with an ensemble method to deal with unbalanced data. 10-fold cross-validation result shows that the proposed method can achieve accuracy with the area under ROC curve (AUC) of 0.74. All the original data and the source matlab codes involved are available at https://sourceforge.net/projects/dissnp-predict/.
Keyword ：
Histone modification signals Imbalanced data Random forest SNPs
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| GB/T 7714 | Han, Jiuqiang , Li, Rong , Zhang, Xinman et al. A Computational Method for Identification of Disease-associated Non-coding SNPs in Human Genome [C] . 2017 : 125-129 . |
| MLA | Han, Jiuqiang et al. "A Computational Method for Identification of Disease-associated Non-coding SNPs in Human Genome" . (2017) : 125-129 . |
| APA | Han, Jiuqiang , Li, Rong , Zhang, Xinman , Lv, Hongqiang , Zhong, Dexing . A Computational Method for Identification of Disease-associated Non-coding SNPs in Human Genome . (2017) : 125-129 . |
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Abstract ：
Human endogenous retroviruses (HERVs) encode active retroviral proteins, which may be involved in the progression of cancer and other diseases. Matrix protein (MA), in groupspecific antigen genes (gag) of retroviruses, is associated with the virus envelope glycoproteins in most mammalian retroviruses and may be involved in virus particle assembly, transport and budding. However, the amount of annotated MAs in ERVs is still at a low level so far. No computational method to predict the exact start and end coordinates of MAs in gags has been proposed yet. In this paper, a computational method to identify MAs in ERVs is proposed. A divide and conquer technique was designed and applied to the conventional prediction model to acquire better results when dealing with gene sequences with various lengths. Initiation sites and termination sites were predicted separately and then combined according to their intervals. Three different algorithms were applied and compared: weighted support vector machine (WSVM), weighted extreme learning machine (WELM) and random forest (RF). G-mean (geometric mean of sensitivity and specificity) values of initiation sites and termination sites under 5-fold cross validation generated by random forest models are 0.9869 and 0.9755 respectively, highest among the algorithms applied. Our prediction models combine RF & WSVM algorithms to achieve the best prediction results. 98.4% of all the collected ERV sequences with complete MAs (125 in total) could be predicted exactly correct by the models. 94,671 HERV sequences from 118 families were scanned by the model, 104 new putative MAs were predicted in human chromosomes. Distributions of the putative MAs and optimizations of model parameters were also analyzed. The usage of our predicting method was also expanded to other retroviruses and satisfying results were acquired.
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| GB/T 7714 | Ma, Yucheng , Liu, Ruiling , Lv, Hongqiang et al. A computational method for prediction of matrix proteins in endogenous retroviruses [J]. | PLOS ONE , 2017 , 12 (5) . |
| MLA | Ma, Yucheng et al. "A computational method for prediction of matrix proteins in endogenous retroviruses" . | PLOS ONE 12 . 5 (2017) . |
| APA | Ma, Yucheng , Liu, Ruiling , Lv, Hongqiang , Han, Jiuqiang , Zhong, Dexing , Zhang, Xinman . A computational method for prediction of matrix proteins in endogenous retroviruses . | PLOS ONE , 2017 , 12 (5) . |
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Abstract ：
Integrase catalytic domain (ICD) is an essential part in the retrovirus for integration reaction, which enables its newly synthesized DNA to be incorporated into the DNA of infected cells. Owing to the crucial role of ICD for the retroviral replication and the absence of an equivalent of integrase in host cells, it is comprehensible that ICD is a promising drug target for therapeutic intervention. However, annotated ICDs in UniProtKB database have still been insufficient for a good understanding of their statistical characteristics so far. Accordingly, it is of great importance to put forward a computational ICD model in this work to annotate these domains in the retroviruses. The proposed model then discovered 11,660 new putative ICDs after scanning sequences without ICD annotations. Subsequently in order to provide much confidence in ICD prediction, it was tested under different cross-validation methods, compared with other database search tools, and verified on independent datasets. Furthermore, an evolutionary analysis performed on the annotated ICDs of retroviruses revealed a tight connection between ICD and retroviral classification. All the datasets involved in this paper and the application software tool of this model can be available for free download at https://sourceforge.net/projects/icdtool/files/?source=navbar.
Keyword ：
Position weight matrix Pseudo amino acid composition Random forest Support vector machine
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| GB/T 7714 | Wu Sijia , Han Jiuqiang , Zhang Xinman et al. A computational model for predicting integrase catalytic domain of retrovirus. [J]. | Journal of theoretical biology , 2017 , 423 : 63-70 . |
| MLA | Wu Sijia et al. "A computational model for predicting integrase catalytic domain of retrovirus." . | Journal of theoretical biology 423 (2017) : 63-70 . |
| APA | Wu Sijia , Han Jiuqiang , Zhang Xinman , Zhong Dexing , Liu Ruiling . A computational model for predicting integrase catalytic domain of retrovirus. . | Journal of theoretical biology , 2017 , 423 , 63-70 . |
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Abstract ：
A filter feature selection technique has been widely used to mine biomedical data. Recently, in the classical filter method minimal-Redundancy-Maximal-Relevance (mRMR), a risk has been revealed that a specific part of the redundancy, called irrelevant redundancy, may be involved in the minimal-redundancy component of this method. Thus, a few attempts to eliminate the irrelevant redundancy by attaching additional procedures to mRMR, such as Kernel Canonical Correlation Analysis based mRMR (KCCAmRMR), have been made. In the present study, a novel filter feature selection method based on the Maximal Information Coefficient (MIC) and Gram-Schmidt Orthogonalization (GSO), named Orthogonal MIC Feature Selection (OMICFS), was proposed to solve this problem. Different from other improved approaches under the max-relevance and min-redundancy criterion, in the proposed method, the MIC is used to quantify the degree of relevance between feature variables and target variable, the GSO is devoted to calculating the orthogonalized variable of a candidate feature with respect to previously selected features, and the max-relevance and min-redundancy can be indirectly optimized by maximizing the MIC relevance between the GSO orthogonalized variable and target. This orthogonalization strategy allows OMICFS to exclude the irrelevant redundancy without any additional procedures. To verify the performance, OMICFS was compared with other filter feature selection methods in terms of both classification accuracy and computational efficiency by conducting classification experiments on two types of biomedical datasets. The results showed that OMICFS outperforms the other methods in most cases. In addition, differences between these methods were analyzed, and the application of OMICFS in the mining of high-dimensional biomedical data was discussed. The Matlab code for the proposed method is available at https://github.com/ Ihqxinghun/bioinformatics/tree/master/OMICFS/.
Keyword ：
Biomedical data mining Filter feature selection Gram-Schmidt Orthogonalization (GSO) Maximal Information Coefficient (MIC)
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| GB/T 7714 | Lyu, Hongqiang , Wan, Mingxi , Han, Jiuqiang et al. A filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization for biomedical data mining [J]. | COMPUTERS IN BIOLOGY AND MEDICINE , 2017 , 89 : 264-274 . |
| MLA | Lyu, Hongqiang et al. "A filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization for biomedical data mining" . | COMPUTERS IN BIOLOGY AND MEDICINE 89 (2017) : 264-274 . |
| APA | Lyu, Hongqiang , Wan, Mingxi , Han, Jiuqiang , Liu, Ruiling , Wang, Cheng . A filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization for biomedical data mining . | COMPUTERS IN BIOLOGY AND MEDICINE , 2017 , 89 , 264-274 . |
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