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Selected Papers from 2017 Health Informatics Conference (ChongqingBioinfo2017)
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Selected Papers from 2017 Health Informatics Conference (ChongqingBioinfo2017)

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Biochemistry".

Deadline for manuscript submissions: closed (20 November 2017) | Viewed by 39985

Special Issue Editors

Prof. Quan Zou

E-Mail Website1 Website2
Guest Editor
School of Computer Science and Technology, Tianjin University, Tianjin, China
Interests: bioinformatics; machine learning; string algorithm
Special Issues, Collections and Topics in MDPI journals
Prof. Le Zhang

E-Mail Website
Guest Editor
College of Computer and Information Science, Southwest University, Chongqing, China
Interests: computational biology; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

The 2017 Health Informatics Conference (ChongqingBioinfo2017), organized by the China Computer Federation Yocsef Chongqing, the Chongqing Association of Artificial Intelligence, the Chinese Medical Association (Digital Medicine), and the Chongqing Bioinformatics Association, will be held in Chongqing, China, 3–5 November, 2017.

This conference focuses on bioinformatics research. Bioinformatics have become an intensive research topics in the past decade, and have attracted many leading scientists, working in biology, physics, mathematics and computer science. Optimization, statistics, algorithms, and many other informatic methods have been widely used in the field.

The purpose of ChongqingBioinfo2017 is to extend the international forum for scientists, researchers, educators, and practitioners, in order to exchange ideas and approaches, to present research findings, as well as state-of-the-art solutions, in this interdisciplinary field, including theoretical methodology development and its applications in biosciences and research on various aspects of bioinformatics. Speakers in China will present their results. For further details, please see http://123.207.33.240:8080/cbc2017, where a full list of presenters is available.

Prof. Dr. Quan Zou
Prof. Dr. Le Zhang
Guest Editors

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Keywords

  • Bioinformatics
  • Machine learning
  • System biology
  • Biological networks
  • Computational biology

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Published Papers (6 papers)

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Research

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2710 KiB  
Article
Using MOEA with Redistribution and Consensus Branches to Infer Phylogenies
by Xiaoping Min, Mouzhao Zhang, Sisi Yuan, Shengxiang Ge, Xiangrong Liu, Xiangxiang Zeng and Ningshao Xia
Int. J. Mol. Sci. 2018, 19(1), 62; https://doi.org/10.3390/ijms19010062 - 26 Dec 2017
Cited by 7 | Viewed by 3611
Abstract
In recent years, to infer phylogenies, which are NP-hard problems, more and more research has focused on using metaheuristics. Maximum Parsimony and Maximum Likelihood are two effective ways to conduct inference. Based on these methods, which can also be considered as the optimal [...] Read more.
In recent years, to infer phylogenies, which are NP-hard problems, more and more research has focused on using metaheuristics. Maximum Parsimony and Maximum Likelihood are two effective ways to conduct inference. Based on these methods, which can also be considered as the optimal criteria for phylogenies, various kinds of multi-objective metaheuristics have been used to reconstruct phylogenies. However, combining these two time-consuming methods results in those multi-objective metaheuristics being slower than a single objective. Therefore, we propose a novel, multi-objective optimization algorithm, MOEA-RC, to accelerate the processes of rebuilding phylogenies using structural information of elites in current populations. We compare MOEA-RC with two representative multi-objective algorithms, MOEA/D and NAGA-II, and a non-consensus version of MOEA-RC on three real-world datasets. The result is, within a given number of iterations, MOEA-RC achieves better solutions than the other algorithms. Full article
(This article belongs to the Special Issue Selected Papers from 2017 Health Informatics Conference (ChongqingBioinfo2017))
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3186 KiB  
Article
A Genome-Wide Association Study and Complex Network Identify Four Core Hub Genes in Bipolar Disorder
by Zengyan Xie, Xianyan Yang, Xiaoya Deng, Mingyue Ma and Kunxian Shu
Int. J. Mol. Sci. 2017, 18(12), 2763; https://doi.org/10.3390/ijms18122763 - 19 Dec 2017
Cited by 10 | Viewed by 6073
Abstract
Bipolar disorder is a common and severe mental illness with unsolved pathophysiology. A genome-wide association study (GWAS) has been used to find a number of risk genes, but it is difficult for a GWAS to find genes indirectly associated with a disease. To [...] Read more.
Bipolar disorder is a common and severe mental illness with unsolved pathophysiology. A genome-wide association study (GWAS) has been used to find a number of risk genes, but it is difficult for a GWAS to find genes indirectly associated with a disease. To find core hub genes, we introduce a network analysis after the GWAS was conducted. Six thousand four hundred fifty eight single nucleotide polymorphisms (SNPs) with p < 0.01 were sifted out from Wellcome Trust Case Control Consortium (WTCCC) dataset and mapped to 2045 genes, which are then compared with the protein–protein network. One hundred twelve genes with a degree >17 were chosen as hub genes from which five significant modules and four core hub genes (FBXL13, WDFY2, bFGF, and MTHFD1L) were found. These core hub genes have not been reported to be directly associated with BD but may function by interacting with genes directly related to BD. Our method engenders new thoughts on finding genes indirectly associated with, but important for, complex diseases. Full article
(This article belongs to the Special Issue Selected Papers from 2017 Health Informatics Conference (ChongqingBioinfo2017))
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1992 KiB  
Article
Icariin Regulates Cellular Functions and Gene Expression of Osteoarthritis Patient-Derived Human Fibroblast-Like Synoviocytes
by Lianhong Pan, Yonghui Zhang, Na Chen and Li Yang
Int. J. Mol. Sci. 2017, 18(12), 2656; https://doi.org/10.3390/ijms18122656 - 8 Dec 2017
Cited by 39 | Viewed by 4197
Abstract
Synovial inflammation plays an important role in the pathogenesis and progress of osteoarthritis (OA). There is an urgent need to find safe and effective drugs that can reduce the inflammation and regulate the pathogenesis of cytokines of the OA disease. Here, we investigated [...] Read more.
Synovial inflammation plays an important role in the pathogenesis and progress of osteoarthritis (OA). There is an urgent need to find safe and effective drugs that can reduce the inflammation and regulate the pathogenesis of cytokines of the OA disease. Here, we investigated the effect of icariin, the major pharmacological active component of herb Epimedium on human osteoarthritis fibroblast-like synoviocytes (OA–FLSs). The OA–FLSs were isolated from patients with osteoarthritis and cultured in vitro with different concentrations of icariin. Then, cell viability, proliferation, and migration were investigated; MMP14, GRP78, and IL-1β gene expression levels were detected via qRT-PCR. Icariin showed low cytotoxicity to OA–FLSs at a concentration of under 10 μM and decreased the proliferation of the cells at concentrations of 1 and 10 μM. Icariin inhibited cell migration with concentrations ranging from 0.1 to 1 μM. Also, the expression of three cytokines for the pathogenesis of OA which include IL-1β, MMP14 and GRP78 was decreased by the various concentrations of icariin. These preliminary results imply that icariin might be an effective compound for the treatment of OA disease. Full article
(This article belongs to the Special Issue Selected Papers from 2017 Health Informatics Conference (ChongqingBioinfo2017))
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10362 KiB  
Article
Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection
by Tingting Li, Zhengguo Cheng and Le Zhang
Int. J. Mol. Sci. 2017, 18(12), 2592; https://doi.org/10.3390/ijms18122592 - 1 Dec 2017
Cited by 16 | Viewed by 4575
Abstract
Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of [...] Read more.
Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency. Full article
(This article belongs to the Special Issue Selected Papers from 2017 Health Informatics Conference (ChongqingBioinfo2017))
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2085 KiB  
Article
Identifying the Epitope Regions of Therapeutic Antibodies Based on Structure Descriptors
by Jingxuan Qiu, Tianyi Qiu, Yin Huang and Zhiwei Cao
Int. J. Mol. Sci. 2017, 18(12), 2457; https://doi.org/10.3390/ijms18122457 - 24 Nov 2017
Cited by 4 | Viewed by 4499
Abstract
Therapeutic antibodies are widely used for disease detection and specific treatments. However, as an exogenous protein, these antibodies can be detected by the human immune system and elicit a response that can lead to serious illnesses. Therapeutic antibodies can be engineered through antibody [...] Read more.
Therapeutic antibodies are widely used for disease detection and specific treatments. However, as an exogenous protein, these antibodies can be detected by the human immune system and elicit a response that can lead to serious illnesses. Therapeutic antibodies can be engineered through antibody humanization, which aims to maintain the specificity and biological function of the original antibodies, and reduce immunogenicity. However, the antibody drug effect is synchronously reduced as more exogenous parts are replaced by human antibodies. Hence, a major challenge in this area is to precisely detect the epitope regions in immunogenic antibodies and guide point mutations of exogenous antibodies to balance both humanization level and drug effect. In this article, the latest dataset of immunoglobulin complexes was collected from protein data bank (PDB) to discover the spatial features of immunogenic antibody. Furthermore, a series of structure descriptors were generated to characterize and distinguish epitope residues from non-immunogenic regions. Finally, a computational model was established based on structure descriptors, and results indicated that this model has the potential to precisely predict the epitope regions of therapeutic antibodies. With rapid accumulation of immunoglobulin complexes, this methodology could be used to improve and guide future antibody humanization and potential clinical applications. Full article
(This article belongs to the Special Issue Selected Papers from 2017 Health Informatics Conference (ChongqingBioinfo2017))
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Review

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757 KiB  
Review
LncRNA Structural Characteristics in Epigenetic Regulation
by Chenguang Wang, Lianzong Wang, Yu Ding, Xiaoyan Lu, Guosi Zhang, Jiaxin Yang, Hewei Zheng, Hong Wang, Yongshuai Jiang and Liangde Xu
Int. J. Mol. Sci. 2017, 18(12), 2659; https://doi.org/10.3390/ijms18122659 - 8 Dec 2017
Cited by 129 | Viewed by 16330
Abstract
The rapid development of new generation sequencing technology has deepened the understanding of genomes and functional products. RNA-sequencing studies in mammals show that approximately 85% of the DNA sequences have RNA products, for which the length greater than 200 nucleotides (nt) is called [...] Read more.
The rapid development of new generation sequencing technology has deepened the understanding of genomes and functional products. RNA-sequencing studies in mammals show that approximately 85% of the DNA sequences have RNA products, for which the length greater than 200 nucleotides (nt) is called long non-coding RNAs (lncRNA). LncRNAs now have been shown to play important epigenetic regulatory roles in key molecular processes, such as gene expression, genetic imprinting, histone modification, chromatin dynamics, and other activities by forming specific structures and interacting with all kinds of molecules. This paper mainly discusses the correlation between the structure and function of lncRNAs with the recent progress in epigenetic regulation, which is important to the understanding of the mechanism of lncRNAs in physiological and pathological processes. Full article
(This article belongs to the Special Issue Selected Papers from 2017 Health Informatics Conference (ChongqingBioinfo2017))
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