Editorial Advanced Computational Approaches for Medical...
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Editorial Advanced Computational Approaches for Medical Genetics and Genomics Zhi Wei, 1 Xiao Chang, 2 and Junwen Wang 3 1 Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA 2 e Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA 3 Centre for Genomic Sciences and Department of Biochemistry, LKS Faculty of Medicine, e University of Hong Kong, Hong Kong Correspondence should be addressed to Zhi Wei; [email protected] Received 9 June 2015; Accepted 9 June 2015 Copyright © 2015 Zhi Wei et al. is 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. Remarkable advances have been made in genetics and genomics over the last decade due to the rapid technology innovation in microarray and sequencing. anks to biomed- ical discoveries, it is feasible now to improve the diagnosis and treatment by genetic and genomic tests, for paving the way for an era of personalized/precision medicine in health care. However, it remains challenging to identify causal mutations from massive amounts of genomic data. ere is an unprece- dented demand for novel computational methods and ana- lytical strategies to improve the accuracy of variants identi- fication and the power of association tests. is special issue aims to publish applications of innovative analysis pipelines and algorithms to find the better solutions of complex genetic and genomic problems in a time efficient manner. We look for original research findings and practical applications that con- tribute to the diagnosis and management of human disorders. In this special issue, we selected ten papers from dozens of submissions aſter in-depth review. We summarize their key contributions and findings as follows. In the field of medical genetics, we collected three research papers and one review paper. S. Perez-Alvarez et al. developed a statistical algorithm (FARMS) for variable selection aiming to identify variables with the optimal predication perfor- mance for a specific outcome. e authors further applied FARMS to a high-dimensional dataset of over 800 individuals and showed that the proposed method is more efficient than other approaches such as regression based method. Y. Wang et al., in another methodology paper, proposed a novel method (LRSDec) to identify gene modules and genetic inter- actions between them. It is based on regularized low-rank approximation and enjoys nearly optimal error bounds. We expect its wide applications in the field of genetic interaction data analysis, image processing, and so on. Using a statistical genomic approach T. Du et al. reported the discovery of the association between FSHR polymorphisms and polycystic ovary morphology in women with polycystic ovary syn- drome. In a review article, R. de Vlaming and P. J. F. Groenen surveyed the use of ridge regression for prediction in quan- titative genetics by genotyping data. ey also performed a suite of simulations to estimate the effect of sample size, the number of SNPs, and trait heritability on the accuracy of the results. In the field of medical genomics, we collected six research articles. Four of them employed network-based computa- tional strategies to investigate the molecular mechanisms of complex diseases including cancer, obesity, and Type 2 Dia- betes (T2D). By computing coexpression gene pairs in two types of lung cancers and the normal lung tissues, F. Long et al. identified molecular biomarkers that distinguish small-cell lung cancer and non-small-cell lung cancer. In another article, Q. Zou et al. proposed a network-based method to predict the association between microRNAs and diseases and further developed it into a web server (http://datamining.xmu.edu.cn/ ∼jinjinli/MircoDAP.html.) for use by the community. In “Network-Based Association Study of Obesity and Type 2 Diabetes with Gene Expression Profiles,” S. Zhang et al. inte- grated multiple omics data of obesity and T2D to construct a comprehensive biological network. eir novel strategy revealed the pathways associated with both obesity and T2D. Another article by S. Park et al. explored the impact of Hindawi Publishing Corporation BioMed Research International Volume 2015, Article ID 705469, 2 pages http://dx.doi.org/10.1155/2015/705469
Transcript of Editorial Advanced Computational Approaches for Medical...
EditorialAdvanced Computational Approaches forMedical Genetics and Genomics
Zhi Wei,1 Xiao Chang,2 and Junwen Wang3
1Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA2The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA3Centre for Genomic Sciences and Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
Correspondence should be addressed to Zhi Wei; [email protected]
Received 9 June 2015; Accepted 9 June 2015
Copyright © 2015 Zhi Wei et al. This is an open access article distributed under the Creative Commons Attribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Remarkable advances have been made in genetics andgenomics over the last decade due to the rapid technologyinnovation inmicroarray and sequencing.Thanks to biomed-ical discoveries, it is feasible now to improve the diagnosis andtreatment by genetic and genomic tests, for paving the wayfor an era of personalized/precision medicine in health care.However, it remains challenging to identify causal mutationsfrommassive amounts of genomic data. There is an unprece-dented demand for novel computational methods and ana-lytical strategies to improve the accuracy of variants identi-fication and the power of association tests. This special issueaims to publish applications of innovative analysis pipelinesand algorithms to find the better solutions of complex geneticand genomic problems in a time efficientmanner.We look fororiginal research findings and practical applications that con-tribute to the diagnosis andmanagement of human disorders.
In this special issue, we selected ten papers from dozensof submissions after in-depth review.We summarize their keycontributions and findings as follows.
In the field ofmedical genetics, we collected three researchpapers and one review paper. S. Perez-Alvarez et al. developeda statistical algorithm (FARMS) for variable selection aimingto identify variables with the optimal predication perfor-mance for a specific outcome. The authors further appliedFARMS to a high-dimensional dataset of over 800 individualsand showed that the proposed method is more efficientthan other approaches such as regression based method. Y.Wang et al., in another methodology paper, proposed a novelmethod (LRSDec) to identify genemodules and genetic inter-actions between them. It is based on regularized low-rank
approximation and enjoys nearly optimal error bounds. Weexpect its wide applications in the field of genetic interactiondata analysis, image processing, and so on. Using a statisticalgenomic approach T. Du et al. reported the discovery of theassociation between FSHR polymorphisms and polycysticovary morphology in women with polycystic ovary syn-drome. In a review article, R. de Vlaming and P. J. F. Groenensurveyed the use of ridge regression for prediction in quan-titative genetics by genotyping data. They also performed asuite of simulations to estimate the effect of sample size, thenumber of SNPs, and trait heritability on the accuracy of theresults.
In the field ofmedical genomics, we collected six researcharticles. Four of them employed network-based computa-tional strategies to investigate the molecular mechanisms ofcomplex diseases including cancer, obesity, and Type 2 Dia-betes (T2D). By computing coexpression gene pairs in twotypes of lung cancers and the normal lung tissues, F. Long et al.identified molecular biomarkers that distinguish small-celllung cancer andnon-small-cell lung cancer. In another article,Q. Zou et al. proposed a network-based method to predictthe association betweenmicroRNAs and diseases and furtherdeveloped it into a web server (http://datamining.xmu.edu.cn/∼jinjinli/MircoDAP.html.) for use by the community. In“Network-Based Association Study of Obesity and Type 2Diabetes with Gene Expression Profiles,” S. Zhang et al. inte-grated multiple omics data of obesity and T2D to constructa comprehensive biological network. Their novel strategyrevealed the pathways associated with both obesity and T2D.Another article by S. Park et al. explored the impact of
Hindawi Publishing CorporationBioMed Research InternationalVolume 2015, Article ID 705469, 2 pageshttp://dx.doi.org/10.1155/2015/705469
2 BioMed Research International
inflammatory responses to the risk of colorectal cancer andAlzheimer’s disease in the view of systems biology. Y. Sun andQ. Liu presented a computational framework for decipheringthe correlation between breast tumor samples and cell lines.They proposed to integrate both copy-number changes andgene expression profiles. Their investigation can serve as auseful guide to bridge the gap between cell lines and tumorsand help to select the most suitable cell line models forpersonalized cancer studies. The article by T. Liu et al. con-ducted a gene coexpression and evolutionary conservationanalysis of the human preimplantation embryos. Their studydemonstrated the patterns of evolutionary constraints thatwere imposed on different stages of human preimplantationembryos.
We believe the publications in this collection will attractand benefit readers from multiple disciplines, not onlybioinformaticians and statistical geneticists for methodologydevelopment, but also researchers and clinicians in using thenovel tools for their science discovery.
Acknowledgments
We would like to express our sincere gratitude and appreci-ation to the anonymous reviewers for their time and effort.We are also grateful to all authors for their excellent and fun-damental contributions and their patience in communicatingwith us.
Zhi WeiXiao Chang
Junwen Wang
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