Dry computational approaches for wet medical problems

Journal of Translational Medicine

Peter Hamilton¹  Shu-Dong Zhang¹  Frank Emmert-Streib² 

[1] Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Lisburn Road 97, Belfast, UK;Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Lisburn Road 97, Belfast, UK

关键词: Pharmacogenomics;    Network medicine;    Translational bioinformatics;    Genomics;    Systems medicine;    Computational genomics;   

Others  :  821872
DOI  :  10.1186/1479-5876-12-26

 received in 2013-12-06, accepted in 2014-01-23,  发布年份 2014

【 摘 要 】

This is a report on the 4th international conference in ‘Quantitative Biology and Bioinformatics in Modern Medicine’ held in Belfast (UK), 19–20 September 2013. The aim of the conference was to bring together leading experts from a variety of different areas that are key for Systems Medicine to exchange novel findings and promote interdisciplinary ideas and collaborations.

【 授权许可】

   
2014 Emmert-Streib et al.; licensee BioMed Central Ltd.

【 参考文献 】

• [1]Quackenbush J: The human genome: The book of essential knowledge. Curiosity GuidesNew York; Imagine Publishing; (2011).
• [2]Chen C, Baumann WT, Clarke R, Tyson JJ: Modeling the estrogen receptor to growth factor receptor signaling switch in human breast cancer cells. FEBS Lett 2013, 587(20):3327-3334.
• [3]D’Antonio M, Ciccarelli FD: Modification of gene duplicability during the evolution of protein interaction network. PLoS Comput Biol 2011, 7(4):1002029.
• [4]Turnbull A, Kitchen R, Larionov A, Renshaw L, Dixon J, Sims A: Direct integration of intensity-level data from Affymetrix and Illumina microarrays improves statistical power for robust reanalysis. BMC Med Genom 2012, 5(1):35. BioMed Central Full Text
• [5]Quinn JE, Kennedy RD, Mullan PB, Gilmore PM, Carty M, Johnston PG, Harkin DP: Brca1 functions as a differential modulator of chemotherapy-induced apoptosis. Cancer Res 2003, 63(19):6221-6228.
• [6]Ahdesmäki M, Lancashire L, Proutski V, Wilson C, Davison T, Harkin D, Kennedy R: Model selection for prognostic time-to-event gene signature discovery with applications in early breast cancer data. Stat Appl Genet Mol Biol 2011, 12(5):619-635.
• [7]Ben-Hamo R, Efroni S: Gene expression and network-based analysis reveals a novel role for hsa-mir-9 and drug control over the p38 network in glioblastoma multiforme progression. Genome Med 2011, 3(11):77. BioMed Central Full Text
• [8]Ben-Hamo R, Efroni S: Network as biomarker: quantifying transcriptional co-expression to stratify cancer clinical phenotypes. Syst Biomed 2011, 1:1-35.
• [9]Zhang S-D, Gant T: sscMap: An extensible Java application for connecting small-molecule drugs using gene-expression signatures. BMC Bioinform 2009, 10(1):236. BioMed Central Full Text
• [10]Haibe-Kains B, El-Hachem N, Birkbak NJ, Jin AC, Beck AH, Aerts HJWL, Quackenbush J: Inconsistency in large pharmacogenomic studies. Nature 2013, 504(7480):389-393.
• [11]Martin-Sanchez F, Iakovidis I, Norager S, Maojo V, Groen Pd, Van der Lei J, Jones T, Abraham-Fuchs K, Apweiler R, Babic A, et al.: Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care. J Biomed Inform 2004, 37(1):30-42.
• [12]Butcher EC, Berg EL, Kunkel EJ: Systems biology in drug discovery. Nat Biotechnol 2004, 22(10):1253-1259.
• [13]Dehmer M, Mueller LAJ, Emmert-Streib F: Quantitative network measures as biomarkers for classifying prostate cancer disease states: a systems approach to diagnostic biomarkers. PLoS ONE 2013, 8(11):77602.
• [14]Service RF: Biology’s dry future. Science 2013, 342(6155):186-189.