Human Heredity最新文献_第7页

Principal Component Analysis Based on Graph Laplacian and Double Sparse Constraints for Feature Selection and Sample Clustering on Multi-View Data 基于图拉普拉斯和双稀疏约束的主成分分析在多视图数据特征选择和样本聚类中的应用

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-08-29 DOI: 10.1159/000501653

Ming-Juan Wu, Ying-Lian Gao, Jin-Xing Liu, Rong Zhu, Juan Wang

{"title":"Principal Component Analysis Based on Graph Laplacian and Double Sparse Constraints for Feature Selection and Sample Clustering on Multi-View Data","authors":"Ming-Juan Wu, Ying-Lian Gao, Jin-Xing Liu, Rong Zhu, Juan Wang","doi":"10.1159/000501653","DOIUrl":"https://doi.org/10.1159/000501653","url":null,"abstract":"Principal component analysis (PCA) is a widely used method for evaluating low-dimensional data. Some variants of PCA have been proposed to improve the interpretation of the principal components (PCs). One of the most common methods is sparse PCA which aims at finding a sparse basis to improve the interpretability over the dense basis of PCA. However, the performances of these improved methods are still far from satisfactory because the data still contain redundant PCs. In this paper, a novel method called PCA based on graph Laplacian and double sparse constraints (GDSPCA) is proposed to improve the interpretation of the PCs and consider the internal geometry of the data. In detail, GDSPCA utilizes L2,1-norm and L1-norm regularization terms simultaneously to enforce the matrix to be sparse by filtering redundant and irrelative PCs, where the L2,1-norm regularization term can produce row sparsity, while the L1-norm regularization term can enforce element sparsity. This way, we can make a better interpretation of the new PCs in low-dimensional subspace. Meanwhile, the method of GDSPCA integrates graph Laplacian into PCA to explore the geometric structure hidden in the data. A simple and effective optimization solution is provided. Extensive experiments on multi-view biological data demonstrate the feasibility and effectiveness of the proposed approach.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 1","pages":"47 - 58"},"PeriodicalIF":1.8,"publicationDate":"2019-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000501653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43266794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Low-Rank Representation Method Regularized by Dual-Hypergraph Laplacian for Selecting Differentially Expressed Genes 用双超图拉普拉斯正则化的低秩表示方法选择差异表达基因

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-08-29 DOI: 10.1159/000501482

Xiu-Xiu Xu, Lingyun Dai, Xiangzhen Kong, Jin-Xing Liu

{"title":"A Low-Rank Representation Method Regularized by Dual-Hypergraph Laplacian for Selecting Differentially Expressed Genes","authors":"Xiu-Xiu Xu, Lingyun Dai, Xiangzhen Kong, Jin-Xing Liu","doi":"10.1159/000501482","DOIUrl":"https://doi.org/10.1159/000501482","url":null,"abstract":"Differentially expressed genes selection becomes a hotspot and difficulty in recent molecular biology. Low-rank representation (LRR) uniting graph Laplacian regularization has gained good achievement in the above field. However, the co-expression information of data cannot be captured well by graph regularization. Therefore, a novel low-rank representation method regularized by dual-hypergraph Laplacian is proposed to reveal the intrinsic geometrical structures hidden in the samples and genes direction simultaneously, which is called dual-hypergraph Laplacian regularized LRR (DHLRR). Finally, a low-rank matrix and a sparse perturbation matrix can be recovered from genomic data by DHLRR. Based on the sparsity of differentially expressed genes, the sparse disturbance matrix can be applied to extracting differentially expressed genes. In our experiments, two gene analysis tools are used to discuss the experimental results. The results on two real genomic data and an integrated dataset prove that DHLRR is efficient and effective in finding differentially expressed genes.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"127 8","pages":"21 - 33"},"PeriodicalIF":1.8,"publicationDate":"2019-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000501482","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41331149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A Novel Feature Selection Method for High-Dimensional Biomedical Data Based on an Improved Binary Clonal Flower Pollination Algorithm 基于改进二元克隆花授粉算法的高维生物医学数据特征选择方法

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-08-29 DOI: 10.1159/000501652

Chaokun Yan, Jingjing Ma, Huimin Luo, Ge Zhang, Junwei Luo

引用次数: 21

Prediction of the RNA Secondary Structure Using a Multi-Population Assisted Quantum Genetic Algorithm 利用多种群辅助量子遗传算法预测RNA二级结构

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-08-28 DOI: 10.1159/000501480

Sha Shi, Xin-Li Zhang, Xian-Li Zhao, Le Yang, Wei Du, Yun-Jiang Wang

引用次数: 10

PSO-CFDP: A Particle Swarm Optimization-Based Automatic Density Peaks Clustering Method for Cancer Subtyping PSO-CFDP：一种基于粒子群优化的癌症亚型自动密度峰值聚类方法

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-08-14 DOI: 10.1159/000501481

Xuhui Zhu, J. Shang, Y. Sun, Feng Li, Jin-Xing Liu, Shasha Yuan

{"title":"PSO-CFDP: A Particle Swarm Optimization-Based Automatic Density Peaks Clustering Method for Cancer Subtyping","authors":"Xuhui Zhu, J. Shang, Y. Sun, Feng Li, Jin-Xing Liu, Shasha Yuan","doi":"10.1159/000501481","DOIUrl":"https://doi.org/10.1159/000501481","url":null,"abstract":"Cancer subtyping is of great importance for the prediction, diagnosis, and precise treatment of cancer patients. Many clustering methods have been proposed for cancer subtyping. In 2014, a clustering algorithm named Clustering by Fast Search and Find of Density Peaks (CFDP) was proposed and published in Science, which has been applied to cancer subtyping and achieved attractive results. However, CFDP requires to set two key parameters (cluster centers and cutoff distance) manually, while their optimal values are difficult to be determined. To overcome this limitation, an automatic clustering method named PSO-CFDP is proposed in this paper, in which cluster centers and cutoff distance are automatically determined by running an improved particle swarm optimization (PSO) algorithm multiple times. Experiments using PSO-CFDP, as well as LR-CFDP, STClu, CH-CCFDAC, and CFDP, were performed on four benchmark datasets and two real cancer gene expression datasets. The results show that PSO-CFDP can determine cluster centers and cutoff distance automatically within controllable time/cost and, therefore, improve the accuracy of cancer subtyping.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 1","pages":"9 - 20"},"PeriodicalIF":1.8,"publicationDate":"2019-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000501481","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45683405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Front & Back Matter 正面和背面

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-06-01 DOI: 10.1159/000501780