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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 …
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.
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 …
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 …
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 …
Extracting drug-drug interaction articles from MEDLINE to improve the content of drug databases.
Duda S, Aliferis C, Miller R, Statnikov A, Johnson K. Duda S, et al. Among authors: statnikov a. AMIA Annu Symp Proc. 2005;2005:216-20. AMIA Annu Symp Proc. 2005. PMID: 16779033 Free PMC article.
MEDLINE represents a respected source of peer-reviewed biomedical citations that potentially might serve as a valuable source of drug-drug interaction information, if relevant articles could be pinpointed effectively and efficiently. We evaluated the classification …
MEDLINE represents a respected source of peer-reviewed biomedical citations that potentially might serve as a valuable source …
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 …
Text categorization models for high-quality article retrieval in internal medicine.
Aphinyanaphongs Y, Tsamardinos I, Statnikov A, Hardin D, Aliferis CF. Aphinyanaphongs Y, et al. Among authors: statnikov a. J Am Med Inform Assoc. 2005 Mar-Apr;12(2):207-16. doi: 10.1197/jamia.M1641. Epub 2004 Nov 23. J Am Med Inform Assoc. 2005. PMID: 15561789 Free PMC article.
OBJECTIVE Finding the best scientific evidence that applies to a patient problem is becoming exceedingly difficult due to the exponential growth of medical publications. ...Naive Bayes, a specialized AdaBoost algorithm, and linear and polynomial support vector machi …
OBJECTIVE Finding the best scientific evidence that applies to a patient problem is becoming exceedingly difficult due to the exponen …
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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