Editorial Data Mining in Translational Bioinformaticsdownloads.hindawi.com/journals/bmri/2014/656519.pdf · Editorial Data Mining in Translational Bioinformatics Xing-MingZhao, 1

EditorialData Mining in Translational Bioinformatics

Xing-Ming Zhao,1 Jean X. Gao,2 and Jose C. Nacher3

1 School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China2Department of Computer Science & Engineering, University of Texas, Arlington, TX 76019, USA3Department of Information Science, Faculty of Science, Toho University, Chiba 274-8510, Japan

Correspondence should be addressed to Xing-Ming Zhao; xm [email protected]

Received 28 May 2014; Accepted 28 May 2014; Published 12 June 2014

Copyright © 2014 Xing-Ming Zhao 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.

Translational bioinformatics is an emerging field that aims toexploit various kinds of biological data for useful knowledgeto be translated into clinical practice. However, the floodingof the huge amount of omics data makes it a big challengeto analyze and to interpret these data. Therefore, it is highlydemanded to develop new efficient computational method-ologies, especially data mining approaches, for translationalbioinformatics. Under these circumstances, this special issueaims to present the recent progress on datamining techniquesthat have been developed for handling the huge amount ofbiological data arising in translational bioinformatics field.

In data mining, one of the most important problemsis how to represent the data so that the computationalapproaches could handle these data appropriately. In thisspecial issue, B. Gan et al. utilized the latent low-rank repre-sentation to extract useful signals from noisy gene expressiondata and then classified tumors with sparse representationclassifier and obtained promising results on benchmarkdatasets. C. Zhao et al. proposed a new feature representationof facial complexion for diagnosis in traditional Chinesemedicine and achieved high recognition accuracy. G. Zhanget al. formulated the skin biopsy image annotation as amulti-instance multilabel (MLML) problem and automati-cally annotated the skin biopsy images with a sparse BayesianMLML algorithm based on region structures and texturefeatures. Except for feature extraction, feature selection is alsovery important in data mining. Z. Ji et al. proposed a particleswarm optimization-based feature selection approach to pre-dict syndromes for hepatocellular carcinoma and improveddiagnosis accuracy. With the accumulation of various data in

translational bioinformatics, it is becoming a challenging taskfor traditional intelligent approaches to handle and interpretthese data; S. Li et al. presented a survey on the recentprogress about the hybrid intelligences and their applicationsin bioinformatics, where the hybrid intelligence is morepowerful and robust compared with traditional intelligentapproaches.

The rapid accumulation of various kinds of biologicaldata requires more powerful statistical approaches to extractuseful signals from the huge amount of noisy data. L. Sun etal. built a new pipeline to investigate the DNA methylationprofiles in male and female nonagenarians/centenarians andidentified some differentially methylated probes betweenmale and female nonagenarians/centenarians, which provideinsights into the mechanism of longevity gender gap ofhuman beings. Z. Teng et al. developed a new algorithmto predict protein function based on weighted mapping ofdomains and GO terms, which outperforms other popu-lar approaches on benchmark datasets. J.-L. Huang et al.presented an online cross-species comparative system toidentify conserved and exclusive simple sequence repeatswithin model species, which can facilitate both evolutionarystudies and understanding of gene functions. L. Guo et al.proposed a new approach to identify microRNAs (miRNAs)associated with breast cancer and found that miRNA geneclusters demonstrate consistent deregulation patterns despitetheir different expression levels, which may provide insightsinto the regulatory roles of miRNAs in tumors.

Recently, network biology is becoming a promisingresearch field by organizing different kinds of data into

Hindawi Publishing CorporationBioMed Research InternationalVolume 2014, Article ID 656519, 2 pageshttp://dx.doi.org/10.1155/2014/656519

2 BioMed Research International

a network representation. T. Jacquemin et al. proposed anew approach to identify disease associated protein com-plexes based on a heterogeneous network that consists ofa disease similarity network and a tissue-specific protein-protein interactions network and successfully found diseaseassociated complexes. X. Li et al. proposed a new pipelineto detect symptom-gene associations by integrating multipledata sources and found some potential disease genes. Itis known that DNA mutations will affect gene expression.However, it is difficult to knowwhichmutationswill affect thegene expression and how the genes are regulated within thebiological system. D. Kim et al. developed a novel approachthat can both identify theQuantitative Trait Loci and infer thegene regulation network and successfully identified the genesassociated with psychiatric disorder. R. Zhang et al. presenteda new approach to identify the pathways linking TGF 𝛽 toovarian carcinoma immunoreactive antigen-like protein 2(OCIAD2) by exploring the pathway bridge, and the resultantpathway explained how TGF 𝛽 affects the expression ofOCIAD2 in cancer microenvironment.

Xing-Ming ZhaoJean X. Gao

Jose C. Nacher

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Editorial Data Mining in Translational Bioinformaticsdownloads.hindawi.com/journals/bmri/2014/656519.pdf · Editorial Data Mining in Translational Bioinformatics Xing-MingZhao, 1