15608177
Gene 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 the algorithm.


METHODS
Our approach combines a Genetic Algorithm (GA) and the k-Nearest Neighbor (KNN) method to identify genes that can jointly discriminate between different classes of samples (e.g. normal versus tumor). The GA/KNN method is a stochastic supervised pattern recognition method. The genes identified are subsequently used to classify independent test set samples.


RESULTS
The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification. It is a multivariate approach that can capture the correlated structure in the data. We find that for a given data set gene selection is highly repeatable in independent runs using the GA/KNN method. In general, however, gene selection may be less robust than classification.


AVAILABILITY
The method is available at http://dir.niehs.nih.gov/microarray/datamining


CONTACT
LI3@niehs.nih.gov
26302874
Application of the GA/KNN method to SELDI proteomics data
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
13836655
Dimension reduction methods for microarrays with application to censored survival data
MOTIVATION
Recent research has shown that gene expression profiles can potentially be used for predicting various clinical phenotypes, such as tumor class, drug response and survival time. While there has been extensive studies on tumor classification, there has been less emphasis on other phenotypic features, in particular, patient survival time or time to cancer recurrence, which are subject to right censoring. We consider in this paper an analysis of censored survival time based on microarray gene expression profiles.


RESULTS
We propose a dimension reduction strategy, which combines principal components analysis and sliced inverse regression, to identify linear combinations of genes, that both account for the variability in the gene expression levels and preserve the phenotypic information. The extracted gene combinations are then employed as covariates in a predictive survival model formulation. We apply the proposed method to a large diffuse large-B-cell lymphoma dataset, which consists of 240 patients and 7399 genes, and build a Cox proportional hazards model based on the derived gene expression components. The proposed method is shown to provide a good predictive performance for patient survival, as demonstrated by both the significant survival difference between the predicted risk groups and the receiver operator characteristics analysis.


AVAILABILITY
R programs are available upon request from the authors.


SUPPLEMENTARY INFORMATION
http://dna.ucdavis.edu/~hli/bioinfo-surv-supp.pdf.
11083681
MARD: a new method to detect differential gene expression in treatment-control time courses
MOTIVATION
Characterizing the dynamic regulation of gene expression by time course experiments is becoming more and more important. A common problem is to identify differentially expressed genes between the treatment and control time course. It is often difficult to compare expression patterns of a gene between two time courses for the following reasons: (1) the number of sampling time points may be different or hard to be aligned between the treatment and the control time courses; (2) estimation of the function that describes the expression of a gene in a time course is difficult and error-prone due to the limited number of time points. We propose a novel method to identify the differentially expressed genes between two time courses, which avoids direct comparison of gene expression patterns between the two time courses.


RESULTS
Instead of attempting to 'align' and compare the two time courses directly, we first convert the treatment and control time courses into neighborhood systems that reflect the underlying relationships between genes. We then identify the differentially expressed genes by comparing the two gene relationship networks. To verify our method, we apply it to two treatment-control time course datasets. The results are consistent with the previous results and also give some new biologically meaningful findings.


AVAILABILITY
The algorithm in this paper is coded in C++ and is available from http://leili-lab.cmb.usc.edu/yeastaging/projects/MARD/
14092393
Lazy Approximation for Solving Continuous Finite-Horizon MDPs
Solving Markov decision processes (MDPs) with continuous state spaces is a challenge due to, among other problems. the well-known curse of dimensionality. Nevertheless, numerous real-world applications such as transportation planning and telescope observation scheduling exhibit a critical dependence on continuous states. Current approaches to continuous-state MDPs include discretizing their transition models. In this paper, we propose and study an alternative, discretization-free approach we call lazy approximation. Empirical study shows that lazy approximation performs much better than discretization, and we successfully applied this new technique to a more realistic planetary rover planning problem.
14311449
A software framework for matchmaking based on semantic web technology
An important objective of the Semantic Web is to make Electronic Commerce interactions more flexible and automated. To achieve this, standardization of ontologies, message content and message protocols will be necessary.In this paper we investigate how Semantic and Web Services technologies can be used to support service advertisement and discovery in e-commerce. In particular, we describe the design and implementation of a service matchmaking prototype which uses a DAML-S based ontology and a Description Logic reasoner to compare ontology based service descriptions. We also present the results of initial experiments testing the performance of this prototype implementation in a realistic agent based e-commerce scenario.
1303692
Batch Reinforcement Learning with State Importance
We investigate the problem of using function approximation in reinforcement learning where the agent's policy is represented as a classifier mapping states to actions. High classification accuracy is usually deemed to correlate with high policy quality. But this is not necessarily the case as increasing classification accuracy can actually decrease the policy's quality. This phenomenon takes place when the learning process begins to focus on classifying less "important" states. In this paper, we introduce a measure of state's decision-making importance that can be used to improve policy learning. As a result, the focused learning process is shown to converge faster to better policies.