BioMed Research International Integrated Analysis of Multiscale Large-Scale Biological Data for Investigating Human Disease 2016 Guest Editors: Tao Huang, Lei Chen, Jiangning Song, Mingyue Zheng, Jialiang Yang, and Zhenguo Zhang Integrated Analysis of Multiscale Large-Scale Biological Data for Investigating Human Disease 2016 BioMed Research International Integrated Analysis of Multiscale Large-Scale Biological Data for Investigating Human Disease 2016 GuestEditors:TaoHuang,LeiChen,JiangningSong, Mingyue Zheng, Jialiang Yang, and Zhenguo Zhang Copyright © 2016 Hindawi Publishing Corporation. All rights reserved. This is a special issue published in “BioMed Research International.” All articles are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Contents Integrated Analysis of Multiscale Large-Scale Biological Data for Investigating Human Disease 2016 Tao Huang, Lei Chen, Jiangning Song, Mingyue Zheng, Jialiang Yang, and Zhenguo Zhang Volume 2016, Article ID 6585069, 2 pages New Trends of Digital Data Storage in DNA Pavani Yashodha De Silva and Gamage Upeksha Ganegoda Volume 2016, Article ID 8072463, 14 pages Analyzing the miRNA-Gene Networks to Mine the Important miRNAs under Skin of Human and Mouse Jianghong Wu, Husile Gong, Yongsheng Bai, and Wenguang Zhang Volume 2016, Article ID 5469371, 9 pages Differential Regulatory Analysis Based on Coexpression Network in Cancer Research Junyi Li, Yi-Xue Li, and Yuan-Yuan Li Volume 2016, Article ID 4241293, 8 pages Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data Shuaiqun Wang, Aorigele, Wei Kong, Weiming Zeng, and Xiaomin Hong Volume 2016, Article ID 9721713, 12 pages Predicting Diagnostic Gene Biomarkers for Non-Small-Cell Lung Cancer Bin Liang, Yang Shao, Fei Long, and Shu-Juan Jiang Volume 2016, Article ID 3952494, 8 pages A Five-Gene Expression Signature Predicts Clinical Outcome of Ovarian Serous Cystadenocarcinoma Li-Wei Liu, Qiuhao Zhang, Wenna Guo, Kun Qian, and Qiang Wang Volume 2016, Article ID 6945304, 6 pages DASAF: An R Package for Deep Sequencing-Based Detection of Fetal Autosomal Abnormalities from Maternal Cell-Free DNA Baohong Liu, Xiaoyan Tang, Feng Qiu, Chunmei Tao, Junhui Gao, Mengmeng Ma, Tingyan Zhong, JianPing Cai, Yixue Li, and Guohui Ding Volume 2016, Article ID 2714341, 7 pages The Use of Protein-Protein Interactions for the Analysis of the Associations between PM2.5 and Some Diseases Qing Zhang, Pei-Wei Zhang, and Yu-Dong Cai Volume 2016, Article ID 4895476, 7 pages The Occurrence of Genetic Alterations during the Progression of Breast Carcinoma Xiao-Chen Li, Chenglin Liu, Tao Huang, and Yang Zhong Volume 2016, Article ID 5237827, 5 pages Using Small RNA Deep Sequencing Data to Detect Human Viruses Fang Wang, Yu Sun, Jishou Ruan, Rui Chen, Xin Chen, Chengjie Chen, Jan F. Kreuze, ZhangJun Fei, Xiao Zhu, and Shan Gao Volume 2016, Article ID 2596782, 9 pages Motif-Based Text Mining of Microbial Metagenome Redundancy Profiling Data for Disease Classification Yin Wang, Rudong Li, Yuhua Zhou, Zongxin Ling, Xiaokui Guo, Lu Xie, and Lei Liu Volume 2016, Article ID 6598307, 11 pages Analysis and Identification of Aptamer-Compound Interactions with a Maximum Relevance Minimum Redundancy and Nearest Neighbor Algorithm ShaoPeng Wang, Yu-Hang Zhang, Jing Lu, Weiren Cui, Jerry Hu, and Yu-Dong Cai Volume 2016, Article ID 8351204, 9 pages The Subcellular Localization and Functional Analysis of Fibrillarin2, a Nucleolar Protein in Nicotiana benthamiana LupingZheng,JinaiYao,FangluanGao,LinChen,ChaoZhang, Lingli Lian, Liyan Xie, Zujian Wu, and Lianhui Xie Volume 2016, Article ID 2831287, 9 pages Mining for Candidate Genes Related to Pancreatic Cancer Using Protein-Protein Interactions and a Shortest Path Approach Fei Yuan, Yu-Hang Zhang, Sibao Wan, ShaoPeng Wang, and Xiang-Yin Kong Volume 2015, Article ID 623121, 12 pages Hindawi Publishing Corporation BioMed Research International Volume 2016, Article ID 6585069, 2 pages http://dx.doi.org/10.1155/2016/6585069 Editorial Integrated Analysis of Multiscale Large-Scale Biological Data for Investigating Human Disease 2016 Tao Huang,1 Lei Chen,2 Jiangning Song,3 Mingyue Zheng,4 Jialiang Yang,5 and Zhenguo Zhang6 1 Chinese Academy of Sciences, Shanghai, China 2Shanghai Maritime University, Pudong, Shanghai, China 3MonashUniversity,Clayton,VIC3800,Australia 4Shanghai Institute of Materia Medica, Zuchongzhi Rd, Pudong, Shanghai, China 5IcahnSchoolofMedicineatMountSinai,NewYork,NY10029,USA 6Department of Biology, University of Rochester, Rochester, NY 14627, USA Correspondence should be addressed to Tao Huang; [email protected] Received 29 August 2016; Accepted 29 August 2016 Copyright © 2016 Tao Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With the development of high-throughput omics technolo- (TS) which conducts fine-tune search. The performance of gies, more and more omics data are generated. It has become their method was superior to other similar works. common to have multiomics data for the same samples which J. Li et al. reviewed the paradigm of differential regulatory make the integrative analysis possible. But the data integra- analysis (DRA) based on gene coexpression network (GCN). tionisstillchallengingsincethereareonlyalimitednumber They found that DRA can reveal underlying molecular ofmethodstodosuchanalysis.Tostimulatethemethodology mechanism in large-scale carcinogenesis studies. development and applications of multiomics analysis, we B. Liang et al. constructed a non-small cell lung can- collected 14 novel studies of large scale multiomics data for cer- (NSCLC-) specific functional association network and biomedical researches. applied a network partition algorithm to divide the network P. Y. De Silva and G. U. Ganegoda critically analyzed into gene modules. From these modules, they identified various methods used for encoding and encrypting data onto NSCLC biomarkers. DNA and identified the advantages and capability of every B. Liu et al. developed an R package, detection of auto- scheme to overcome the drawbacks of previous methods. somal abnormalities for fetus (DASAF), which implements J. Wu et al. integrated the MGI, GEO, and miRNA data- the three most popular trisomy detection methods—the base to analyze the genetic regulatory networks under mor- standard -score method (STDZ); the GC correction -score phology difference of integument of humans and mice. And (GCCZ) method; and the internal reference -score (IRZ) they found that the gene expression network in the skin was method—together with one subchromosome abnormality highly divergent between human and mouse. identification method (SCAZ). L.-W. Liu L. et al. analyzed 303 samples of ovarian serous Q. Zhang et al. investigated the associations between cystadenocarcinoma and the corresponding RNA-seq data. PM2.5 and 22 disease classes, such as respiratory diseases, They established a risk assessment model of five genes and the cardiovascular diseases, and gastrointestinal diseases. They AUROC value was 0.67 when predicting the survival time in found that several diseases, such as diseases related to ear, testing set. nose, and throat and gastrointestinal, nutritional, renal, and S. Wang et al. proposed a new hybrid algorithm called cardiovascular diseases, are influenced by PM2.5. HICATS that incorporated imperialist competition algo- F.Wangetal.used931sRNA-seqdatasetsfromtheNCBI rithm (ICA) which performs global search and tabu search SRA database to detect and identify viruses in human cells 2 BioMed Research International or tissues. Six viruses including HPV-18, HBV, HCV, HIV-1, SMRV, and EBV were detected from 36 datasets and SMRV was found in Diffuse Large B Cell Lymphoma cells for the first time. S. Wang et al. attempted to extract important features for aptamer-compound interactions using feature selection methods, such as maximum relevance minimum redun- dancy, and incremental feature selection. They found that quantum-chemical and electrostatic descriptors were impor- tant for aptamer-compound interaction prediction. X.-C. Li et al. constructed oncogenetic tree to imitate the occurrence of genetic and cytogenetic alterations in human breast cancer. They found that ErbB2 copy number variation is the frequent early event of human breast cancer. Y. Wang et al. proposed a Phylogenetic Tree-Based Motif Finding Algorithm (PMF) to analyze 16S rRNA text data. By integrating phylogenic rules and other statistical indexes for classification, it can effectively reduce the dimension of the large feature spaces generated by the text datasets. L. Zheng et al. analyzed the subcellular localization and biological functions of Fibrillarin2, a nucleolar protein in Nicotiana benthamiana.Theyfoundthattheproteinwas localized in the nucleolus and cajal body of leaf epidermal cells of N. benthamiana and involved growth retardation, organ deformation, chlorosis, and necrosis. F. Yuan et al. tried to predict candidate genes related to pancreatic cancer using protein-protein interactions and a shortest path approach. The genes on the shortest path among known pancreatic cancer genes were considered as candidates that were further filtered by permutation test. Sev-