Support vector machine classification and validation of cancer tissue samples using microarray expression data
Motivation: DNA microarray experiments generating thousands of gene expression
measurements, are being used to gather information from tissue and cell samples regarding …
measurements, are being used to gather information from tissue and cell samples regarding …
[PDF][PDF] Support vector machine classification of microarray data
The Problem: Use the learning from examples paradigm to make class predictions and infer
genes involved in these predictions from DNA microarray expression data. Specifically, we …
genes involved in these predictions from DNA microarray expression data. Specifically, we …
Classifying microarray data using support vector machines
S Mukherjee - A practical approach to microarray data analysis, 2003 - Springer
Over the last few years the routine use of DNA microarrays has made possible the creation
of large data sets of molecular information characterizing complex biological systems …
of large data sets of molecular information characterizing complex biological systems …
Applications of support vector machines to cancer classification with microarray data
F Chu, L Wang - International journal of neural systems, 2005 - World Scientific
Microarray gene expression data usually have a large number of dimensions, eg, over ten
thousand genes, and a small number of samples, eg, a few tens of patients. In this paper, we …
thousand genes, and a small number of samples, eg, a few tens of patients. In this paper, we …
Classification of gene microarrays by penalized logistic regression
Classification of patient samples is an important aspect of cancer diagnosis and treatment.
The support vector machine (SVM) has been successfully applied to microarray cancer …
The support vector machine (SVM) has been successfully applied to microarray cancer …
[PDF][PDF] Support vector machine classification of microarray gene expression data
MPS Brown, WN Grundy, D Lin… - … of California, Santa …, 1999 - noble.gs.washington.edu
We introduce a new method of functionally classifying genes using gene expression data
from DNA microarray hybridization experiments. The method is based on the theory of …
from DNA microarray hybridization experiments. The method is based on the theory of …
[HTML][HTML] Are random forests better than support vector machines for microarray-based cancer classification?
A Statnikov, CF Aliferis - AMIA annual symposium proceedings, 2007 - ncbi.nlm.nih.gov
Cancer diagnosis and clinical outcome prediction are among the most important emerging
applications of gene expression microarray technology with several molecular signatures on …
applications of gene expression microarray technology with several molecular signatures on …
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification
A Statnikov, L Wang, CF Aliferis - BMC bioinformatics, 2008 - Springer
Background Cancer diagnosis and clinical outcome prediction are among the most
important emerging applications of gene expression microarray technology with several …
important emerging applications of gene expression microarray technology with several …
Recipe for uncovering predictive genes using support vector machines based on model population analysis
Selecting a small number of informative genes for microarray-based tumor classification is
central to cancer prediction and treatment. Based on model population analysis, here we …
central to cancer prediction and treatment. Based on model population analysis, here we …