Literature DB >> 25221378

Ensemble Classification of Cancer Types and Biomarker Identification.

Hussein Hijazi1, Ming Wu1, Aritro Nath2, Christina Chan3.   

Abstract

Cancer classification is an important step in biomarker identification. Developing machine learning methods that correctly predict cancer subtypes/types can help in identifying potential cancer biomarkers. In this commentary, we presented ensemble classification approach and compared its performance with single classification approaches. Additionally, the application of cancer classification in identifying biomarkers for drug design was discussed.

Entities:  

Keywords:  biomarker; cancer classification; drug design; ensemble; gene expression

Year:  2012        PMID： 25221378      PMCID： PMC4162531          DOI： 10.1002/ddr.21032

Source DB:  PubMed          Journal:  Drug Dev Res        ISSN： 0272-4391            Impact factor:   4.360


  9 in total

Review 1.  Monitoring gene expression using DNA microarrays.

Authors:  C A Harrington; C Rosenow; J Retief
Journal:  Curr Opin Microbiol       Date:  2000-06       Impact factor: 7.934

2.  Biomarker identification by feature wrappers.

Authors:  M Xiong; X Fang; J Zhao
Journal:  Genome Res       Date:  2001-11       Impact factor: 9.043

3.  Inhibition of FLT3 in MLL. Validation of a therapeutic target identified by gene expression based classification.

Authors:  Scott A Armstrong; Andrew L Kung; Meghann E Mabon; Lewis B Silverman; Ronald W Stam; Monique L Den Boer; Rob Pieters; John H Kersey; Stephen E Sallan; Jonathan A Fletcher; Todd R Golub; James D Griffin; Stanley J Korsmeyer
Journal:  Cancer Cell       Date:  2003-02       Impact factor: 31.743

4.  MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia.

Authors:  Scott A Armstrong; Jane E Staunton; Lewis B Silverman; Rob Pieters; Monique L den Boer; Mark D Minden; Stephen E Sallan; Eric S Lander; Todd R Golub; Stanley J Korsmeyer
Journal:  Nat Genet       Date:  2001-12-03       Impact factor: 38.330

5.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

Authors:  T R Golub; D K Slonim; P Tamayo; C Huard; M Gaasenbeek; J P Mesirov; H Coller; M L Loh; J R Downing; M A Caligiuri; C D Bloomfield; E S Lander
Journal:  Science       Date:  1999-10-15       Impact factor: 47.728

6.  Ensemble machine learning on gene expression data for cancer classification.

Authors:  Aik Choon Tan; David Gilbert
Journal:  Appl Bioinformatics       Date:  2003

Review 7.  Gene expression microarray technologies in the development of new therapeutic agents.

Authors:  Paul A Clarke; Robert te Poele; Paul Workman
Journal:  Eur J Cancer       Date:  2004-11       Impact factor: 9.162

8.  Network-based classification of breast cancer metastasis.

Authors:  Han-Yu Chuang; Eunjung Lee; Yu-Tsueng Liu; Doheon Lee; Trey Ideker
Journal:  Mol Syst Biol       Date:  2007-10-16       Impact factor: 11.429

9.  A combinational feature selection and ensemble neural network method for classification of gene expression data.

Authors:  Bing Liu; Qinghua Cui; Tianzi Jiang; Songde Ma
Journal:  BMC Bioinformatics       Date:  2004-09-27       Impact factor: 3.169
  9 in total
  3 in total

1.  Applying Serum Proteins and MicroRNA as Novel Biomarkers for Early-Stage Cervical Cancer Detection.

Authors:  Shengye Du; Yinghui Zhao; Changyu Lv; Meiling Wei; Zheng Gao; Xianhua Meng
Journal:  Sci Rep       Date:  2020-06-03       Impact factor: 4.379

2.  Establishment and evaluation of prediction model for multiple disease classification based on gut microbial data.

Authors:  Sohyun Bang; DongAhn Yoo; Soo-Jin Kim; Soyun Jhang; Seoae Cho; Heebal Kim
Journal:  Sci Rep       Date:  2019-07-15       Impact factor: 4.379

3.  Improving Drug Sensitivity Prediction Using Different Types of Data.

Authors:  H A Hejase; C Chan
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2015-02-18
  3 in total

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