title intelligent informatics in translational medicine ... · wide association studies by...

Title Intelligent Informatics in Translational Medicine.

Author(s) Chang, Hao-Teng; Akutsu, Tatsuya; Draghici, Sorin; Ray,Oliver; Pai, Tun-Wen

Citation BioMed research international (2015), 2015

Issue Date 2015

URL http://hdl.handle.net/2433/214332

Right

© 2015 Hao-Teng Chang et al. This is an open access articledistributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproductionin any medium, provided the original work is properly cited.

Type Journal Article

Textversion publisher

Kyoto University

EditorialIntelligent Informatics in Translational Medicine

Hao-Teng Chang,1,2,3 Tatsuya Akutsu,4 Sorin Draghici,5 Oliver Ray,6 and Tun-Wen Pai7,8

1Graduate Institute of Basic Medical Science, College of Medicine, China Medical University, Taichung 40402, Taiwan2Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan3Department of Science Education, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang 322100, China4Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 606-8501, Japan5Department of Computer Science, Wayne State University, Detroit, MI 48202, USA6Department of Computer Science, University of Bristol, Bristol BS8 1UB, UK7Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan8Center of Excellence for the Oceans, National Taiwan Ocean University, Keelung 20224, Taiwan

Correspondence should be addressed to Tun-Wen Pai; [email protected]

Received 14 March 2015; Accepted 14 March 2015

Copyright © 2015 Hao-Teng Chang et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Starting from 2008, the organization committee members,Dr. Hui-Huang Hsu, Dr. Tun-Wen Pai, Dr. Oliver Ray, andDr. Hao-Teng Chang, yearly held the InternationalWorkshopon Intelligent Informatics in Biology and Medicine (IIBM),which is used to be held in conjugation with InternationalConference on Complex, Intelligent, and Software IntensiveSystems (CISIS). The main purpose for establishing thisforum is to gather scientists from multidisciplinary fieldsincluding biology, medicine, computer science, statistics, andinformatics to discuss how to face the new big data eraand how to employ the hundreds of thousands of biologicaldatasets including gene expression profiles, genetic informa-tion, proteomes, metabolomes, and even molecular imagingin clinical medicine. So many genomes from various specieshave been sequenced in quick succession. One importantreason to conduct these genome projects is to translateuseful and relevant information to biomedical research aswell as therapeutic strategy development. From bench tobedside, bioinformatics researches have been provided strongand powerful tools to accelerate the analyses of complicateddatasets.

In the past seven years, IIBM successfully broughttogether computer scientists, medical doctors, biologists,statisticians, and chemists to present and discuss currenttopics on genomics, epigenomics, GWAS/PheWAS, imag-ing processing, healthcare information, big data analyses,and so on. On the platform of IIBM, the scientists share

their know-how and research experiences on processingthe complexity and volume of experimental data from nextgeneration sequencing and mass spectrometry technologies.Many various sophisticated computational methodologieshave been designed and developed to support the new detec-tion techniques which can effectively improve the qualityof healthcare. Intelligent information technologies indeedfacilitate and accelerate researches from basic to clinicalinvestigation in terms of translational medicine.

To record the discoveries of talents and gather morecontributions to these fields, this special issue was launchedand supported by this journal. This special issue focuseson addressing the questions of biomedical sciences throughcomputational technologies. It also describes the informationprocesses employed, with an emphasis on forthcoming highthroughput technologies and biomedical systems, whichprovides opportunities to discuss recent hot topics andprogresses in the area of biomedicine for the academic andindustrial societies.This special issue received 19 submissionsfrom which 9 papers were selected for publication. Thesepapers address the data-analytical method design, algorithmdevelopment, mathematical modeling, and computationalsimulation techniques of some translational medical applica-tions.

In “Computational Biophysical, Biochemical, and Evo-lutionary Signature of Human R-Spondin Family Proteins,theMember ofCanonicalWnt/ß-Catenin Signaling Pathway,”

Hindawi Publishing CorporationBioMed Research InternationalVolume 2015, Article ID 717210, 2 pageshttp://dx.doi.org/10.1155/2015/717210

2 BioMed Research International

A. R. Sharma et al. applied biophysical, biochemical, andmolecular evolutionary approaches to investigate human R-spondins protein family which is involved in cell growthand disease development and has been noticed as a potentialtherapeutic target.This bioinformatics study could be appliedto the further investigation of Wnt/ß-catenin-System.

In “Detecting Epistatic Interactions in Metagenome-Wide Association Studies by metaBOOST,” M. Wu andR. Jiang proposed a method called metaBOOST to detectepistatic interactions between such metagenomic biomark-ers as microbial genus and high-level functional KEGGorthologs. They performed comprehensive simulations toevaluate metaBOOST and applied the method to analyze tworeal genome-wide datasets for pathological mechanisms ofmicrobial communities in human complex diseases.

In “Predicting Flavin and Nicotinamide AdenineDinucleotide-Binding Sites in Proteins Using the FragmentTransformation Method,” C.-H. Lu et al. utilized fragmenttransformation methods to predict flavin and nicotinamideadenine dinucleotide-binding sites. The proposed methodpresents 68.4% and 67.1% true-positive rates for FAD andNAD binding site prediction under the false-positive rate at5%, employing BioLiP dataset.

In “A Survey on the Computational Approaches toIdentify Drug Targets in the Postgenomic Era,” Y.-F. Daiand X.-M. Zhao made a survey on the recent progressbeing made on computational methodologies that have beendeveloped to predict drug targets based on different kinds ofomics data and drug properties. This information could beutilized to improve prediction accuracywhendeveloping newmethodologies in the future.

In “Identification of Gene and MicroRNA Signatures forOral Cancer Developed from Oral Leukoplakia,” G. Zhu etal. presented a new pipeline to identify oral cancer relatedgenes and microRNAs by integrating both gene and miRNAexpression profiles. They found some network modules aswell as their miRNA regulators that play important roles inthe development of oral leukoplakia to oral cancer. Amongthese network modules, 91.67% of genes and 37.5% of miR-NAs have been previously reported to be related to oral cancerin literature.

In “A Heparan Sulfate-Binding Cell Penetrating Peptidefor Tumor Targeting andMigration Inhibition,” C.-J. Chen etal. analyzed a set of heparan sulfate-binding cell penetratingpeptides derived fromnatural proteins. In addition to cellularbinding and internalization, these peptides demonstratedmultiple functions including strong binding activities totumor cell surface, significant inhibitory effects on cancer cellmigration, and suppression of angiogenesis in vitro and invivo.

In “A Large-Scale Structural Classification of Antimi-crobial Peptides,” C.-C. Lee et al. presented a database ofantimicrobial peptides (abbreviated as ADAM) which con-tains 7,007 unique sequences and 759 structures. ADAM sys-tematically establishes comprehensive associations betweenAMP sequences and structures through structural folds andprovides an easy access to view their relationships. Thirtydistinct AMP structural fold clusters were detected andreported. According to ADAM, AMP structural folds are

limited, only covering about 3% of the overall protein foldspace.

In “Predict Metabolic Gene Biomarkers for Neurodegen-erative Disease by an Integrated Network-Based Approach,”W. Tian et al. utilized Met-express method to predict keyenzyme-coding genes in both Parkinson’s and Huntingtondiseases. They found that the predicted genes might beinvolved in some common pathogenic metabolic pathwaysand had significant functional association with known dis-ease genes. The predicted genes could be used as novelbiomarkers for potential therapeutic treatments.

In “TheTF-miRNACoregulationNetwork inOral LichenPlanus,” Y.-L. Zuo et al. employed the gene regulatory net-works derived from transcriptomic and miRNA datasets toidentify OLP related gene modules. In particular, they foundthat the gene modules were regulated by both transcriptionfactors and miRNAs played important roles in the pathogen-esis of OLP. Some of the genes in the modules have beenreported to be related to the disease.

This special issue presents a broad spectrum of compu-tational methodologies, biological investigation, and bioin-formatics prediction. The papers included in this specialissue provide useful messages of intelligent informatics fortranslational medicine applications. This issue illustratesthat bioinformatics approaches can often be used by lifescientists as a first step in the investigation of various diseasemechanisms.

Acknowledgments

Here, we want to thank the authors and reviewers for theirscientific contribution and congratulate them for the highquality of their work.

Hao-Teng ChangTatsuya AkutsuSorin Draghici

Oliver RayTun-Wen Pai

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