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Flexibility and rigidity index for chromosome packing,flexibility and dynamics analysis

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Frontiers of Computer Science 2022年第4期16卷 203-213页

作者： Jiajie Peng Jinjin Yang D Vijay Anand xuequn shang Kelin Xia School of Computer Science Northwestern Polytechnical UniversityXi’an710129China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityMinistry of Industry and Information TechnologyXi’an710129China Division of Mathematical Sciences School of Physical and Mathematical SciencesNanyang Technological UniversitySingapore637371Singapore School of Biological Sciences Nanyang Technological UniversitySingapore637371Singapore

The packing of genomic DNA from double helix into highly-order hierarchical assemblies has a great impact on chromosome flexibility,dynamics and *** open and accessible regions of chromosomes are primary binding positions for regulatory elements and are crucial to nuclear processes and biological *** by the success of flexibility-rigidity index(FRI)in biomolecular flexibility analysis and drug design,we propose an FRI-based model for quantitatively characterizing chromosome *** on Hi-C data,a flexibility index for each locus can be ***,flexibility is tightly related to packing *** compacted regions are usually more rigid,while loosely packed regions are more ***,a strong correlation is found between our flexibility index and DNase and ATAC values,which are measurements for chromosome *** addition,the genome regions with higher chromosome flexibility have a higher chance to be bound by transcription ***,the Gaussian network model(GNM)is applied to analyze the chromosome accessibility and a mobility profile has been proposed to characterize chromosome *** with GNM,our FRI is slightly more accurate(1%to 2%increase)and significantly more efficient in both computational time and *** a 5Kb resolution Hi-C data,the flexibility evaluation process only takes FRI a few minutes on a single-core *** contrast,GNM requires 1.5 hours on 10 ***,interchromosome interactions can be easily combined into the flexibility evaluation,thus further enhancing the accuracy of our *** contrast,the consideration of interchromosome information into GNM will significantly increase the size of its Laplacian(or Kirchhoff)matrix,thus becoming computationally extremely challenging for the current *** software and supplementary document are available at https://***/jiajiepeng/FRI_chrFle.

关键词： flexibility-rigidity index 3D genome chromosome flexibility

Copy number variation related disease genes

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Frontiers of Electrical and Electronic Engineering in China 2018年第2期6卷 99-112页

作者： Chaima Aouiche xuequn shang Bolin Chen School of Computer Science Northwestern Polytechnical University Xi'an 710072 China

Background： One of the most important and challenging issues in biomedicine and genomics is how to identify disease related genes. Datasets from high-throughput biotechnologies have been widely used to overcome this issue from various perspectives, e.g., epigenomics, genomics, transcriptomics, proteomics, metabolomics. At the genomic level, copy number variations （CNVs） have been recognized as critical genetic variations, which contribute significantly to genomic diversity. They have been associated with both common and complex diseases, and thus have a large influence on a variety of Mendelian and somatic genetic disorders. Results： In this review, based on a variety of complex diseases, we give an overview about the critical role of using CNVs for identifying disease related genes, and discuss on details the different high-throughput and sequencing methods applied for CNV detection. Some limitations and challenges concerning CNV are also highlighted. Conclusions： Reliable detection of CNVs will not only allow discriminating driver mutations for various diseases, but also helps to develop personalized medicine when integrating it with other genomic features.

关键词： CNV disease gene complex disease targeted approach genome-wide approach whole exome sequencing

Federated learning-outcome prediction with multi-layer privacy protection

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Frontiers of Computer Science 2024年第6期18卷 186604页

作者： ZHANG Yupei LI Yuxin WANG Yifei WEI Shuangshuang XU Yunan shang xuequn School of Computer Science Northwestern Polytechnical University MIIT Lab of Big Data Storage and Management

Learning-outcome prediction (LOP) is a long-standing and critical problem in educational routes. Many studies have contributed to developing effective models while often suffering from data shortage and low generalization to various institutions due to the privacy-protection issue. To this end, this study proposes a distributed grade prediction model, dubbed FecMap, by exploiting the federated learning (FL) framework that preserves the private data of local clients and communicates with others through a global generalized model. FecMap considers local subspace learning (LSL), which explicitly learns the local features against the global features, and multi-layer privacy protection (MPP), which hierarchically protects the private features, including model-shareable features and not-allowably shared features, to achieve client-specific classifiers of high performance on LOP per institution. FecMap is then achieved in an iteration manner with all datasets distributed on clients by training a local neural network composed of a global part, a local part, and a classification head in clients and averaging the global parts from clients on the server. To evaluate the FecMap model, we collected three higher-educational datasets of student academic records from engineering majors. Experiment results manifest that FecMap benefits from the proposed LSL and MPP and achieves steady performance on the task of LOP, compared with the state-of-the-art models. This study makes a fresh attempt at the use of federated learning in the learning-analytical task, potentially paving the way to facilitating personalized education with privacy protection.

关键词： federated learning local subspace learning hierarchical privacy protection learning outcome prediction privacy-protected representation learning

Constrained query of order-preserving submatrix in gene expression data

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Frontiers of Computer Science 2016年第6期10卷 1052-1066页

作者： Tao JIANG Zhanhuai LI xuequn shang Bolin CHEN Weibang LI Zhilei YIN School of Computer Science and Technology Northwestern Polytechnical University Xi'an 710072 China

Order-preserving submatrix （OPSM） has become important in modelling biologically meaningful subspace cluster, capturing the general tendency of gene expressions across a subset of conditions. With the advance of microarray and analysis techniques, big volume of gene expression datasets and OPSM mining results are produced. OPSM query can efficiently retrieve relevant OPSMs from the huge amount of OPSM datasets. However, improving OPSM query relevancy remains a difficult task in real life exploratory data analysis processing. First, it is hard to capture subjective interestingness aspects, e.g., the analyst＇s expectation given her/his domain knowledge. Second, when these expectations can be declaratively specified, it is still challenging to use them during the computational process of OPSM queries. With the best of our knowledge, existing methods mainly fo- cus on batch OPSM mining, while few works involve OPSM query. To solve the above problems, the paper proposes two constrained OPSM query methods, which exploit userdefined constraints to search relevant results from two kinds of indices introduced. In this paper, extensive experiments are conducted on real datasets, and experiment results demonstrate that the multi-dimension index （cIndex） and enumerating sequence index （esIndex） based queries have better performance than brute force search.

关键词： gene expression data OPSM constrained query brute-force search feature sequence cIndex

DeepRisk:A deep learning approach for genome-wide assessment of common disease risk

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Fundamental Research 2024年第4期4卷 752-760页

作者： Jiajie Peng Zhijie Bao Jingyi Lia Ruijiang Han Yuxian Wang Lu Han Jinghao Peng Tao Wang Jianye Hao Zhongyu Wei xuequn shang AI for Science Interdisciplinary Research Center Schoolof Computer ScienceNorthwestern Polyechnical UniversityXi'an 129China Key Laboratoryof Bg Data Sorage and Management Northwestern Polytechnical UniversityMinisty of Industy and Information TechnologyX'an 129Chin College of Intelligence and Computing Tianjin UniversityTianjin 300072China School of Data Science Fudan UniversityShanghai 200433China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518000China

The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant *** widely applied,traditional polygenic risk scoring methods fall short,as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms(SNPs).This presents a limitation,as genetic diseases often arise from complex interactions between multiple *** address this challenge,we developed DeepRisk,a biological knowledge-driven deep learning method for modeling these complex,nonlinear associations among SNPs,to provide a more effective method for scoring the risk of common diseases with genome-wide genotype *** demonstrated that DeepRisk outperforms existing PRs-based methods in identifying individuals at high risk for four common diseases:Alzheimer's disease,inflammatory bowel disease,type 2diabetes,and breast cancer.

关键词： Disease risk prediction Deep learning Polygenic risk score Common disease risk Disease prevention

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一种基于特征子图的不确定图分类算法

陕西师范大学学报（自然科学版） 2014年第5期42卷 16-19页

作者：刘意王勇尚学群西北工业大学理学院西北工业大学计算机学院陕西西安710072

采用频繁子图作为特征子图,对不确定图进行分类.提出AGF频繁子图挖掘算法,该算法将频繁子图挖掘问题转换为频繁项挖掘问题,可有效提高频繁子图生成效率.利用频繁子图构造分类模型,首次应用于不确定图,通过实验证明,给出的分类算法具有... 详细信息

采用频繁子图作为特征子图,对不确定图进行分类.提出AGF频繁子图挖掘算法,该算法将频繁子图挖掘问题转换为频繁项挖掘问题,可有效提高频繁子图生成效率.利用频繁子图构造分类模型,首次应用于不确定图,通过实验证明,给出的分类算法具有良好的分类正确率.

关键词：不确定图分类频繁子图特征子图

论现代生产力社会化基本结构对应的价值关系结构

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生产力研究 2003年第1期 59-60,83页

作者：商学群湖北师范学院湖北黄石435002

人类任何一种生产力都生产着一定的价值。生产力基本结构有对应的价值关系结构。人类社会发展中 ,自然生产力、手工生产力、大机器社会化生产力、现代社会化生产力的基本结构变化 ,使对应的价值关系结构也发生变化。现代社会化生产力具... 详细信息

人类任何一种生产力都生产着一定的价值。生产力基本结构有对应的价值关系结构。人类社会发展中 ,自然生产力、手工生产力、大机器社会化生产力、现代社会化生产力的基本结构变化 ,使对应的价值关系结构也发生变化。现代社会化生产力具有二重性 :既推进着商品价值关系结构的发展走向极限。

关键词：生产力社会化基本结构价值关系结构

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相对行常量差异共表达双聚类挖掘算法

计算机应用 2013年第8期33卷 2188-2193,2239页

作者：谢华博尚学群王淼西北工业大学计算机学院西安710129

在生物信息学上,挖掘差异共表达双聚类有助于研究衰老、癌变类变化的生物过程。以往的差异共表达双聚类定义仅仅从一组基因的角度来衡量差异,导致包含了很多噪声。为了克服上述缺点提出新的差异共表达支持度MiSupport,可以将一组基因的... 详细信息

在生物信息学上,挖掘差异共表达双聚类有助于研究衰老、癌变类变化的生物过程。以往的差异共表达双聚类定义仅仅从一组基因的角度来衡量差异,导致包含了很多噪声。为了克服上述缺点提出新的差异共表达支持度MiSupport,可以将一组基因的差异细化到基因级别;并由此定义提出MiCluster算法,可以在两个真实的基因芯片数据中挖掘最大的差异共表达双聚类。MiCluster算法首先基于两个基因芯片数据构建差异共表达权值图,然后基于权值图,采用样本扩展和层次扩展,并利用精确的候选产生方法和高效的剪枝策略,挖掘出最大的差异共表达双聚类。实验结果证明,MiCluster算法比现有的算法快速高效,而且通过均方误差(MSE)测试和基因本体(GO)评价,挖掘出来结果具有更大的统计意义和生物学意义。

关键词：基因芯片基因共表达双聚类差异行常量

CoClique:从生物网络中挖掘频繁关联相似模式

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计算机工程与应用 2011年第32期47卷 155-158,220页

作者：雷小刚尚学群王淼西北工业大学计算机学院西安710129

以前的许多研究已经充证明了挖掘频繁子图是非常有意义的。从单个图中很难挖掘出一些潜在的很有意义的频繁模式,因而应该从多个图中去挖掘频繁模式。以前的研究诸如相似模式(Quasi-Clique)不能解决图中的中心问题。介绍了一个新的概念... 详细信息

以前的许多研究已经充证明了挖掘频繁子图是非常有意义的。从单个图中很难挖掘出一些潜在的很有意义的频繁模式,因而应该从多个图中去挖掘频繁模式。以前的研究诸如相似模式(Quasi-Clique)不能解决图中的中心问题。介绍了一个新的概念关联相似模式(Correlated-Quasi-Clique)同时也介绍了一个有效的算法,CoClique,该算法可以解决挖掘过程中所存在的中心问题并且提高挖掘频繁关联相似模式的效率。同时,也提出了一些有效的剪枝策略来缩小搜索空间。在真实数据集上的实验分析结果证明了所提出的算法比以前的算法更有效,结果更好。

关键词：图挖掘中心问题相似模式关联相似模式