Application of the GA/KNN method to SELDI proteomics data
نویسندگان
چکیده
SUMMARY Proteomics technology has shown promise in identifying biomarkers for disease, toxicant exposure and stress. We show by example that the genetic algorithm/k-nearest neighbors method, developed for mining high-dimensional microarray gene expression data, is also capable of mining surface enhanced laser desorption/ionization-time-of-flight proteomics data. AVAILABILITY The source code of the program and documentation on how to use it are freely available to non-commercial users at http://dir.niehs.nih.gov/dirbb/lifiles/softlic.htm
منابع مشابه
Master Thesis 20p Analysis of Proteomic Patterns for Detection of Prostate Cancer
The SELDI process is a relatively new medical technique that measures the content of di erent proteins in blood samples from patients. Recently, many research teams have shown that there is a relation between the concentrations of speci c proteins and cancer disease. This report has focused on the area of prostate cancer. The output from the SELDI system is created through mass-spectrometry and...
متن کاملGene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method
MOTIVATION We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of...
متن کاملEvolutionary Nearest Neighbour Classification Framework
Data classification attempts to assign a category or a class label to an unknown data object based on an available similar data set with class labels already assigned. K nearest neighbor (KNN) is a widely used classification technique in data mining. KNN assigns the majority class label of its closest neighbours to an unknown object, when classifying an unknown object. The computational efficie...
متن کاملA full ranking method using integrated DEA models and its application to modify GA for finding Pareto optimal solution of MOP problem
This paper uses integrated Data Envelopment Analysis (DEA) models to rank all extreme and non-extreme efficient Decision Making Units (DMUs) and then applies integrated DEA ranking method as a criterion to modify Genetic Algorithm (GA) for finding Pareto optimal solutions of a Multi Objective Programming (MOP) problem. The researchers have used ranking method as a shortcut way to modify GA to d...
متن کاملApplication of the random forest classification algorithm to a SELDI-TOF proteomics study in the setting of a cancer prevention trial.
A thorough discussion of the random forest (RF) algorithm as it relates to a SELDI-TOF proteomics study is presented, with special emphasis on its application for cancer prevention: specifically, what makes it an efficient, yet reliable classifier, and what makes it optimal among the many available approaches. The main body of the paper treats the particulars of how to successfully apply the RF...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Bioinformatics
دوره 20 10 شماره
صفحات -
تاریخ انتشار 2004