Literature DB >> 15208183

Diagnostic classification of cancer using DNA microarrays and artificial intelligence.

Braden T Greer1, Javed Khan.   

Abstract

The application of artificial intelligence (AI) to microarray data has been receiving much attention in recent years because of the possibility of automated diagnosis in the near future. Studies have been published predicting tumor type, estrogen receptor status, and prognosis using a variety of AI algorithms. The performance of intelligent computing decisions based on gene expression signatures is in some cases comparable to or better than the current clinical decision schemas. The goal of these tools is not to make clinicians obsolete, but rather to give clinicians one more tool in their armamentarium to accurately diagnose and hence better treat cancer patients. Several such applications are summarized in this chapter, and some of the common pitfalls are noted.

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Year:  2004        PMID: 15208183     DOI: 10.1196/annals.1310.007

Source DB:  PubMed          Journal:  Ann N Y Acad Sci        ISSN: 0077-8923            Impact factor:   5.691


  7 in total

1.  Analysis of time-series gene expression data: methods, challenges, and opportunities.

Authors:  I P Androulakis; E Yang; R R Almon
Journal:  Annu Rev Biomed Eng       Date:  2007       Impact factor: 9.590

2.  Molecular classification of rhabdomyosarcoma--genotypic and phenotypic determinants of diagnosis: a report from the Children's Oncology Group.

Authors:  Elai Davicioni; Michael J Anderson; Friedrich Graf Finckenstein; James C Lynch; Stephen J Qualman; Hiroyuki Shimada; Deborah E Schofield; Jonathan D Buckley; William H Meyer; Poul H B Sorensen; Timothy J Triche
Journal:  Am J Pathol       Date:  2009-01-15       Impact factor: 4.307

3.  Novel methods to identify biologically relevant genes for leukemia and prostate cancer from gene expression profiles.

Authors:  Austin H Chen; Yin-Wu Tsau; Ching-Heng Lin
Journal:  BMC Genomics       Date:  2010-04-30       Impact factor: 3.969

4.  Analysis of alcoholism data using support vector machines.

Authors:  Robert Yu; Sanjay Shete
Journal:  BMC Genet       Date:  2005-12-30       Impact factor: 2.797

5.  Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data.

Authors:  Ronglai Shen; Debashis Ghosh; Arul M Chinnaiyan
Journal:  BMC Genomics       Date:  2004-12-14       Impact factor: 3.969

6.  A comparative study of different machine learning methods on microarray gene expression data.

Authors:  Mehdi Pirooznia; Jack Y Yang; Mary Qu Yang; Youping Deng
Journal:  BMC Genomics       Date:  2008       Impact factor: 3.969

7.  Cancer-related genes in the transcription signature of facioscapulohumeral dystrophy myoblasts and myotubes.

Authors:  Petr Dmitriev; Ulykbek Kairov; Thomas Robert; Ana Barat; Vladimir Lazar; Gilles Carnac; Dalila Laoudj-Chenivesse; Yegor S Vassetzky
Journal:  J Cell Mol Med       Date:  2013-12-17       Impact factor: 5.310

  7 in total

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