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Effects of environment, genetics and data analysis pitfalls in an esophageal cancer genome-wide association study.
Statnikov A, Li C, Aliferis CF. Statnikov A, et al. PLoS One. 2007 Sep 26;2(9):e958. doi: 10.1371/journal.pone.0000958. PLoS One. 2007. PMID: 17895998 Free PMC article.
METHODOLOGY/PRINCIPAL FINDINGS: In the present paper we undertake a careful examination of the relative significance of genetics, environmental factors, and biases of the data analysis protocol that was used in a previously published genome-wide association study. . …
METHODOLOGY/PRINCIPAL FINDINGS: In the present paper we undertake a careful examination of the relative significance of genetics, env …
Factors influencing the statistical power of complex data analysis protocols for molecular signature development from microarray data.
Aliferis CF, Statnikov A, Tsamardinos I, Schildcrout JS, Shepherd BE, Harrell FE Jr. Aliferis CF, et al. Among authors: statnikov a. PLoS One. 2009;4(3):e4922. doi: 10.1371/journal.pone.0004922. Epub 2009 Mar 17. PLoS One. 2009. PMID: 19290050 Free PMC article.
Challenges in the analysis of mass-throughput data: a technical commentary from the statistical machine learning perspective.
Aliferis CF, Statnikov A, Tsamardinos I. Aliferis CF, et al. Among authors: statnikov a. Cancer Inform. 2007 Feb 16;2:133-62. Cancer Inform. 2007. PMID: 19458765 Free PMC article.
The novel nature and high-dimensionality in such datasets pose a series of nontrivial data analysis problems. This technical commentary discusses the problems of over-fitting, error estimation, curse of dimensionality, causal versus predictive modeling, integration of hete …
The novel nature and high-dimensionality in such datasets pose a series of nontrivial data analysis problems. This technical commenta …
A statistical reappraisal of the findings of an esophageal cancer genome-wide association study.
Statnikov A, Li C, Aliferis CF. Statnikov A, et al. Cancer Res. 2008 Apr 15;68(8):3074-5; author reply 3075. doi: 10.1158/0008-5472.CAN-07-2999. Cancer Res. 2008. PMID: 18413779 No abstract available.
The FAST-AIMS Clinical Mass Spectrometry Analysis System.
Fananapazir N, Statnikov A, Aliferis CF. Fananapazir N, et al. Among authors: statnikov a. Adv Bioinformatics. 2009;2009:598241. doi: 10.1155/2009/598241. Epub 2009 Jul 9. Adv Bioinformatics. 2009. PMID: 19956420 Free PMC article.
We have designed and implemented a stand-alone software system, FAST-AIMS, which seeks to meet this need through automation of data preprocessing, feature selection, classification model generation, and performance estimation. ...
We have designed and implemented a stand-alone software system, FAST-AIMS, which seeks to meet this need through automation of data p …
Are random forests better than support vector machines for microarray-based cancer classification?
Statnikov A, Aliferis CF. Statnikov A, et al. AMIA Annu Symp Proc. 2007 Oct 11;2007:686-90. AMIA Annu Symp Proc. 2007. PMID: 18693924 Free PMC article.
In the present paper we point to several biases of this prior work and conduct a new unbiased evaluation of the two algorithms. Our experiments using 18 diagnostic and prognostic datasets show that support vector machines outperform random forests often by a large m …
In the present paper we point to several biases of this prior work and conduct a new unbiased evaluation of the two algorithms. Our e …
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification.
Statnikov A, Wang L, Aliferis CF. Statnikov A, et al. BMC Bioinformatics. 2008 Jul 22;9:319. doi: 10.1186/1471-2105-9-319. BMC Bioinformatics. 2008. PMID: 18647401 Free PMC article.
As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. ...Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random fo …
As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classifi …
GEMS: a system for automated cancer diagnosis and biomarker discovery from microarray gene expression data.
Statnikov A, Tsamardinos I, Dosbayev Y, Aliferis CF. Statnikov A, et al. Int J Med Inform. 2005 Aug;74(7-8):491-503. doi: 10.1016/j.ijmedinf.2005.05.002. Int J Med Inform. 2005. PMID: 15967710
In order to determine and equip the system with the best performing diagnostic methodologies in this domain, we first conducted a comprehensive evaluation of classification algorithms using 11 cancer microarray datasets. ...Additionally, we performed a cross-dataset …
In order to determine and equip the system with the best performing diagnostic methodologies in this domain, we first conducted a com …
Methods for multi-category cancer diagnosis from gene expression data: a comprehensive evaluation to inform decision support system development.
Statnikov A, Aliferis CF, Tsamardinos I. Statnikov A, et al. Stud Health Technol Inform. 2004;107(Pt 2):813-7. Stud Health Technol Inform. 2004. PMID: 15360925
Cancer diagnosis is a major clinical applications area of gene expression microarray technology. We are seeking to develop a system for cancer diagnostic model creation based on microarray data. ...These results guided the development of a software system tha …
Cancer diagnosis is a major clinical applications area of gene expression microarray technology. We are seeking to develop a s …
A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis.
Statnikov A, Aliferis CF, Tsamardinos I, Hardin D, Levy S. Statnikov A, et al. Bioinformatics. 2005 Mar 1;21(5):631-43. doi: 10.1093/bioinformatics/bti033. Epub 2004 Sep 16. Bioinformatics. 2005. PMID: 15374862
We are seeking to develop a computer system for powerful and reliable cancer diagnostic model creation based on microarray data. ...CONTACT: alexander.statnikov@vanderbilt.edu....
We are seeking to develop a computer system for powerful and reliable cancer diagnostic model creation based on microarray data. ...C …
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