Complexity Biomolecular Networks for Complex Diseases Guest Editors: Fang X. Wu, Jianxin Wang, Min Li, and Haiying Wang Biomolecular Networks for Complex Diseases Complexity Biomolecular Networks for Complex Diseases Guest Editors: Kazuo Toda, Jorge L. Zeredo, Sae Uchida, and Vitaly Napadow Copyright © 2018 Hindawi. All rights reserved. This is a special issue published in “Complexity.” All articles are open access articles distributed under the Creative Commons Attribu- tion License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Editorial Board José Ángel Acosta, Spain Mattia Frasca, Italy Daniela Paolotti, Italy Rodrigo Aldecoa, USA Lucia Valentina Gambuzza, Italy Luis M. Rocha, USA Juan A. Almendral, Spain Carlos Gershenson, Mexico Miguel Romance, Spain David Arroyo, Spain Peter Giesl, UK Matilde Santos, Spain Arturo Buscarino, Italy Sergio Gómez, Spain Hiroki Sayama, USA Guido Caldarelli, Italy Sigurdur F. Hafstein, Iceland Michele Scarpiniti, Italy Danilo Comminiello, Italy Giacomo Innocenti, Italy Enzo Pasquale Scilingo, Italy Manlio De Domenico, Italy Jeffrey H. Johnson, UK Samuel Stanton, USA Pietro De Lellis, Italy Vittorio Loreto, Italy Roberto Tonelli, Italy Albert Diaz-Guilera, Spain Didier Maquin, France Shahadat Uddin, Australia Jordi Duch, Spain Eulalia Martínez, Spain Gaetano Valenza, Italy Joshua Epstein, USA Ch. P. Monterola, Philippines Dimitri Volchenkov, USA Thierry Floquet, France Roberto Natella, Italy Christos Volos, Greece Contents Biomolecular Networks for Complex Diseases Fang-Xiang Wu , Jianxin Wang ,MinLi ,andHaiyingWang Volume 2018, Article ID 4210160, 3 pages SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures Cheng Yan, Jianxin Wang, Wei Lan, Fang-Xiang Wu, and Yi Pan Volume 2017, Article ID 2713280, 10 pages Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey Jin Liu, Min Li, Yi Pan, Wei Lan, Ruiqing Zheng, Fang-Xiang Wu, and Jianxin Wang Volume 2017, Article ID 8362741, 27 pages Building Up a Robust Risk Mathematical Platform to Predict Colorectal Cancer Le Zhang, Chunqiu Zheng, Tian Li, Lei Xing, Han Zeng, Tingting Li, Huan Yang, Jia Cao, Badong Chen, and Ziyuan Zhou Volume 2017, Article ID 8917258, 14 pages miRNA-Disease Association Prediction with Collaborative Matrix Factorization ZhenShen,You-HuaZhang,KyungsookHan,AsokeK.Nandi,BarryHonig,andDe-ShuangHuang Volume 2017, Article ID 2498957, 9 pages Exploring the Limitations of Peripheral Blood Transcriptional Biomarkers in Predicting Influenza Vaccine Responsiveness Luca Marchetti, Emilio Siena, Mario Lauria, Denise Maffione, Nicola Pacchiani, Corrado Priami, and Duccio Medini Volume 2017, Article ID 3017632, 9 pages FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis Lin Yuan, Chang-An Yuan, and De-Shuang Huang Volume 2017, Article ID 5024867, 10 pages Predicting Protein Complexes in Weighted Dynamic PPI Networks Based on ICSC Jie Zhao, Xiujuan Lei, and Fang-Xiang Wu Volume 2017, Article ID 4120506, 11 pages DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes Pi-Jing Wei, Di Zhang, Hai-Tao Li, Junfeng Xia, and Chun-Hou Zheng Volume 2017, Article ID 4826206, 10 pages Identifying the Risky SNP of Osteoporosis with ID3-PEP Decision Tree Algorithm Jincai Yang, Huichao Gu, Xingpeng Jiang, Qingyang Huang, Xiaohua Hu, and Xianjun Shen Volume 2017, Article ID 9194801, 8 pages A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering Xiaowei Li, Zhuang Jing, Bin Hu, Jing Zhu, Ning Zhong, Mi Li, Zhijie Ding, Jing Yang, Lan Zhang, Lei Feng, and Dennis Majoe Volume 2017, Article ID 9514369, 11 pages Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes Ling-Yun Dai, Chun-Mei Feng, Jin-Xing Liu, Chun-Hou Zheng, Jiguo Yu, and Mi-Xiao Hou Volume 2017, Article ID 4216797, 11 pages Hindawi Complexity Volume 2018, Article ID 4210160, 3 pages https://doi.org/10.1155/2018/4210160 Editorial Biomolecular Networks for Complex Diseases Fang-Xiang Wu ,1,2 Jianxin Wang ,3 Min Li ,3 and Haiying Wang4 1 School of Mathematical Sciences, Nankai University, Tianjin 300071, China 2Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada S7N 5A9 3School of Information Science and Engineering, Central South University, Changsha, Hunan 410012, China 4School of Computing and Mathematics, Ulster University, Belfast BT37 0QB, UK Correspondence should be addressed to Fang-Xiang Wu; [email protected] Received 18 January 2018; Accepted 18 January 2018; Published 15 February 2018 Copyright © 2018 Fang-Xiang Wu 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. It is widely acknowledged that complex diseases or disorders potential miRNA-disease associations by integrating miRNA (e.g., cancer, AIDS, and obesity) stem from the dysfunction functional similarity, disease semantic similarity, and experi- of biomolecular networks, not only their isolated compo- mentally verified miRNA-disease associations. Experiments nents (e.g., genes, proteins, and metabolites). Biomolecu- verified that CMFMDA achieved intended purpose and lar networks typically include gene regulatory networks, application values with its short consuming-time and high protein-protein interaction networks, metabolic networks, prediction accuracy. In addition, CMFMDA was applied to and signal transduction networks. With advances in high reveal the potential related miRNAs of Esophageal Neo- throughput measurement techniques such as microarray, plasms and Kidney Neoplasms. RNA-seq, ChIP-chip, yeast two-hybrid analysis, and mass The identification of target molecules associated with spectrometry, large-scale biological data have been and will specific complex diseases is the basis of modern drug discov- continuously be produced. Such data contain insightful ery and development. The computational methods provide information for understanding the mechanism of molecular biological systems and have proved useful in diagnosis, a low-cost and high-efficiency way for predicting drug- treatment, and drug design for complex diseases or disorders. target interactions (DTIs) from biomolecular networks. In For this special issue, we have invited the researchers to the paper “SDTRLS: Predicting Drug-Target Interactions for contribute their original studies in modeling/construction, Complex Diseases Based on Chemical Substructures,” C. Yan analysis, synthesis, and control of complex disease-related et al. proposed a method (called SDTRLS) for predicting biomolecular networks. This special issue accepted eleven DTIs through RLS-Kron model with chemical substructure articles for inclusion after rigorous review. We would like to similarity fusion and Gaussian Interaction Profile (GIP) ker- introduceeachofthembyashortdescription. nels. Their computational experiments showed that SDTRLS Existing studies have shown that microRNAs (miRNAs) outperformed the state-of-the-art methods such as SDTNBI. are involved in the development and progression of various Vaccines represent one of the most effective interven- complex diseases. Experimental identification of miRNA- tions to control infectious diseases. Despite the many suc- disease association is expensive and time-consuming and cesses, an effective vaccine against current global pandemics thus it is appealing to design efficient algorithms to identify such as HIV, malaria, and tuberculosis is still missing. In novel miRNA-disease association. In the paper “miRNA- the paper “Exploring the Limitations of Peripheral Blood Disease Association Prediction with Collaborative Matrix Transcriptional Biomarkers in Predicting Influenza Vaccine Factorization,” Z. Shen et al. developed the computational Responsiveness,” L. Marchetti et al. applied systems biology method of Collaborative Matrix Factorization for miRNA- tovaccinologyandemployedarecentlyestablishedalgorithm Disease Association (CMFMDA) prediction to identify for signature-based clustering of expression profiles, SCUDO, 2 Complexity to provide new insights into why blood-derived transcrip- dataset showed that the proposed method outperformed tome biomarkers often fail to predict the seroresponse to other methods in epistasis detection and could contribute to the influenza virus vaccination. Their analysis revealed that the research of mechanism underlying the disease. composite measures provided a more accurate assessment of Clinical disorders of human brains, such as Alzheimer’s the seroresponse to multicomponent influenza vaccines. disease (AD), schizophrenia (SCZ), and Parkinson’s disease Protein complexes are involved in multiple biological pro- (PD), are among the most complex diseases and therapeu- cesses, and thus detection of protein complexes is essential to tically intractable health problems. In recent years, brain the understanding of complex diseases. In the paper “Predict- regions and their interactions can be modeled as complex ing Protein Complexes in Weighted Dynamic PPI Networks brain networks, which describe highly efficient informa- BasedonICSC,”J.Zhaoetal.proposedanovelalgorithm tion transmission in a brain.