16961001
ChipInfo: software for extracting gene annotation and gene ontology information for microarray analysis
To date, assembling comprehensive annotation information for all probe sets of any Affymetrix microarrays remains a time-consuming, error-prone and challenging task. ChipInfo is designed for retrieving annotation information from online databases such as NetAffx and Gene Ontology and organizing such information into easily interpretable tabular format outputs. As companion software to dChip and GoSurfer, ChipInfo enables users to independently update the information resource files of these software packages. It also has functions for computing related summary statistics of probe sets and Gene Ontology terms. ChipInfo is available at http://biosun1.harvard.edu/complab/chipinfo/.
22347603
Two supervised learning approaches for name disambiguation in author citations
14960694
Comparative analysis of gene sets in the gene ontology space under the multiple hypothesis testing framework
The gene ontology (GO) resource can be used as a powerful tool to uncover the properties shared among, and specific to, a list of genes produced by high-throughput functional genomics studies, such as microarray studies. In the comparative analysis of several gene lists, researchers maybe interested in knowing which GO terms are enriched in one list of genes but relatively depleted in another. Statistical tests such as Fisher's exact test or Chi-square test can be performed to search for such GO terms. However, because multiple GO terms are tested simultaneously, individual p-values from individual tests do not serve as good indicators for picking GO terms. Furthermore, these multiple tests are highly correlated, usual multiple testing procedures that work under an independence assumption are not applicable. In this paper we introduce a procedure, based on false discovery rate (FDR), to treat this correlated multiple testing problem. This procedure calculates a moderately conserved estimator of q-value for every GO term. We identify the GO terms with q-values that satisfy a desired level as the significant GO terms. This procedure has been implemented into the GoSurfer software. GoSurfer is a windows based graphical data mining tool. It is freely available at http://www.gosurfer.org.
10820274
Simultaneous and exact interval estimates for the contrast of two groups based on an extremely high dimensional variable: application to mass spec data
MOTIVATION
Analysis of high-throughput proteomic/genomic data, in particular, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) data and microarray data, has led to a multitude of techniques aimed at identifying potential biomarkers. Most of the statistical techniques for comparing two groups are based on qualitative measures such as P-value. A quantitative way such as interval estimation for the contrasts of two groups is more appealing.


RESULTS
We have devised a simultaneous confidence bands method capable of detecting potential biomarkers, while controlling for overall confidence coverage level, in high-dimensional datasets that discriminate two treatment groups using a permutation scheme. For example, for the SELDI-TOF MS data, we deal with the entire spectrum simultaneously and construct (1 - alpha) confidence bands for the mean differences between groups. Furthermore, peaks were identified based on the maximal differences between the groups as determined by the confidence bands. The analysis method herein described gives both qualitative (P-value) and quantitative data (magnitude of difference). The Clinical Proteomics Programs Databank's ovarian cancer dataset and data from in-house samples containing known spiked-in proteins were analyzed. We were able to identify potential biomarkers similar to those described in previous analysis of the ovarian cancer data, however, while these markers are highly significant between cancer and normal groups, our analysis indicated the absolute difference between the two groups was minimal. In addition, we found additional markers than those previously described with greater differences in average intensities. The proposed confidence bands method successfully detected the spiked-in peaks, as well as, secondary peaks generated by adducts and double-charged species. We also illustrate our method utilizing paired gene expression data from a prostate cancer microarray experiment by constructing confidence bands for the fold changes between cancer and normal samples.


AVAILABILITY
R-package, 'seie.zip' (license: GNU GPL), is publiclly available at http://research2.dfci.harvard.edu/dfci/MS_spike-in_data/
11863469
Processing Spatial-Keyword (SK) Queries in Geographic Information Retrieval (GIR) Systems
Location-based information contained in publicly available GIS databases is invaluable for many applications such as disaster response, national infrastructure protection, crime analysis, and numerous others. The information entities of such databases have both spatial and textual descriptions. Likewise, queries issued to the databases also contain spatial and textual components, for example, "Find shelters with emergency medical facilities in Orange County," or "Find earthquake-prone zones in Southern California." We refer to such queries as spatial-keyword queries or SK queries for short. In recent times, a lot of interest has been generated in efficient processing of SK queries for a variety of applications from Web-search to GIS decision support systems. We refer to systems built for enabling such applications as Geographic Information Retrieval (GIR) Systems. An example GIR system that we address in this paper is a search engine built on top of hundreds of thousands of publicly available GIS databases. Building a search engine over such large repositories is a challenge. One of the key aspects of such a search engine is the performance. In this paper, we propose a framework for GIR systems and focus on indexing strategies that can process SK queries efficiently. We show through experiments that our indexing strategies lead to significant improvement in efficiency of answering SK queries over existing techniques.
14325657
An approach to reducing the labeling cost of Markov random fields within an infinite label space
This work proposes a novel idea, called SOIL, for reducing the computational complexity of the maximum a posteriori optimization problem using Markov random fields (MRFs). The local characteristics of MRFs are employed so that the searching in a virtually infinite label space is confined in a small finite space. Globally, the number of labels allowed is as many as the number of image sites while locally the optimal label is sampled from a space consisting of the labels assigned to the four-neighbor plus a random one. Neither the prior knowledge about the number of classes nor the estimation phase of the class number is required in this work. The proposed method is applied to the problem of texture segmentation and the result is compared with those obtained from conventional methods.
14879989
On edge-transitive Cayley graphs of valency four
A characterization is given of a class of edge-transitive Cayley graphs, providing methods for constructing Cayley graphs with certain symmetry properties. Various new half-arc transitive graphs are constructed.
13583163
On certain convex matrix sets
Abstract We study some nonnegative matrix sets and investigate the relations between their extreme points. The results obtained answer a question posed by Marshall and Olkin and extend a result of Fulkerson concerning permutation matrices.
3090145
XGuard: a system for publishing XML documents without information leakage in the presence of data inference
In data publishing, if the data is published carelessly, public users could use common knowledge to infer more information from the published data, causing leakage of sensitive information. To address related research challenges, we develop a system called XGuard, which can help data owners publish a partial XML document without leaking sensitive information, even if public users can do inference. Specifically, the system has the following functionalities. i) It allows the data owner to define sensitive information and specify common knowledge as XML constraints. ii) Given a partial document, the system can validate if the document can cause information leakage due to common knowledge and how much data can be leaked. iii) The system can help the data owner interactively analyze the data inference and produce a secure valid partial document using the algorithms.