11306780
Mean-entropy discretized features are effective for classifying high-dimensional biomedical data
This paper studies an empirical feature selection heuristics for classifying high-dimensional bio-medical data. A feature's discriminating power can be measured by its entropy value. Based on this idea, we do not consider those features that are ignored by the entropy idea. Such a selection can usually reduce the dimensionality of the data by 90-95%. Then we rank the remaining features, and select features whose entropy is smaller than the average of all the remaining features' entropies. This round of selection can usually further reduce two thirds of the features. So, we can achieve a reduction from tens of thousands of features to only hundreds of important features. Furthermore, we also observe that learning algorithms, including our new tree-committee classifier, generally improve their accuracy after the feature selection. This heuristics appears to be more systematic than the prevailing use of specific numbers of top-ranked features for classification.
10722883
Use of extreme patient samples for outcome prediction from gene expression data
MOTIVATION
Patient outcome prediction using microarray technologies is an important application in bioinformatics. Based on patients' genotypic microarray data, predictions are made to estimate patients' survival time and their risk of tumor metastasis or recurrence. So, accurate prediction can potentially help to provide better treatment for patients.


RESULTS
We present a new computational method for patient outcome prediction. In the training phase of this method, we make use of two types of extreme patient samples: short-term survivors who got an unfavorable outcome within a short period and long-term survivors who were maintaining a favorable outcome after a long follow-up time. These extreme training samples yield a clear platform for us to identify relevant genes whose expression is closely related to the outcome. The selected extreme samples and the relevant genes are then integrated by a support vector machine to build a prediction model, by which each validation sample is assigned a risk score that falls into one of the special pre-defined risk groups. We apply this method to several public datasets. In most cases, patients in high and low risk groups stratified by our method have clearly distinguishable outcome status as seen in their Kaplan-Meier curves. We also show that the idea of selecting only extreme patient samples for training is effective for improving the prediction accuracy when different gene selection methods are used.
15390292
Selection of patient samples and genes for outcome prediction
Gene expression profiles with clinical outcome data enable monitoring of disease progression and prediction of patient survival at the molecular level. We present a new computational method for outcome prediction. Our idea is to use an informative subset of original training samples. This subset consists of only short-term survivors who died within a short period and long-term survivors who were still alive after a long follow-up time. These extreme training samples yield a clear platform to identify genes whose expression is related to survival. To find relevant genes, we combine two feature selection methods - entropy measure and Wilcoxon rank sum test - so that a set of sharp discriminating features are identified. The selected training samples and genes are then integrated by a support vector machine to build a prediction model, by which each validation sample is assigned a survival/relapse risk score for drawing Kaplan-Meier survival curves. We apply this method to two data sets: diffuse large-B-cell lymphoma (DLBCL) and primary lung adenocarcinoma. In both cases, patients in high and low risk groups stratified by our risk scores are clearly distinguishable. We also compare our risk scores to some clinical factors, such as International Prognostic Index score for DLBCL analysis and tumor stage information for lung adenocarcinoma. Our results indicate that gene expression profiles combined with carefully chosen learning algorithms can predict patient survival for certain diseases.
13995613
DNAFSMiner: a web-based software toolbox to recognize two types of functional sites in DNA sequences
UNLABELLED
DNAFSMiner (DNA Functional Sites Miner) is a web-based software toolbox to recognize functional sites in nucleic acid sequences. Currently in this toolbox, we provide two software: TIS Miner and Poly(A) Signal Miner. The TIS Miner can be used to predict translation initiation sites in vertebrate DNA/mRNA/cDNA sequences, and the Poly(A) Signal Miner can be used to predict polyadenylation [poly(A)] signals in human DNA sequences. The prediction results are better than those by literature methods on two benchmark applications. This good performance is mainly attributable to our unique learning method. DNAFSMiner is available free of charge for academic and non-profit organizations.


AVAILABILITY
http://research.i2r.a-star.edu.sg/DNAFSMiner/


CONTACT
huiqing@i2r.a-star.edu.sg.
9642703
Discovering typical structures of documents: a road map approach
The structure of a document refers to the role and hierarchy of subdocument references. Many online documents are similarly structured, though not identically structured. We study the problem of discovering “typical” structures of a collection of such documents, where the user specifies the minimum frequency of a typical structure. We will consider structural features of subdocument references such as labeling, nesting, ordering, cyclicity, and wild-card references, like those found on the Web and digital libraries. Typical structures can be used to serve the following purposes. (a) The “table-ofcontent” for gaining the general information of a source. (b) A road map for browsing and querying a source. (c) A basis for clustering documents. (d) Partial schemas for building structured layers to provide standard database access methods. (e) User/customer’s interests and browsing patterns. We present a solution to the discovery problem.
36964410
A Knowledge Object Oriented System for High Throughput Collection and Analysis of Data
7580221
Efficient Feature Selection via Analysis of Relevance and Redundancy
Feature selection is applied to reduce the number of features in many applications where data has hundreds or thousands of features. Existing feature selection methods mainly focus on finding relevant features. In this paper, we show that feature relevance alone is insufficient for efficient feature selection of high-dimensional data. We define feature redundancy and propose to perform explicit redundancy analysis in feature selection. A new framework is introduced that decouples relevance analysis and redundancy analysis. We develop a correlation-based method for relevance and redundancy analysis, and conduct an empirical study of its efficiency and effectiveness comparing with representative methods.
6969845
Real-time Application Monitoring and Diagnosis for Service Hosting Platforms of Black Boxes
Service hosting platforms typically run a large number of third-party applications that are composed of multiple communicating components distributed on a dynamic set of hosting servers. Understanding the real-time behaviors of these applications and the intricate interactions/dependency relationships among these application components is very important to service management tasks such as load balancing, capacity planning, performance debugging and fault diagnosis. In this paper, we present the scalable real-time application monitoring and diagnosis (SRAMD) tool, for applications consisting of "black box" components: software without source code available, and usually without desired logging instrumentation. SRAMD runs at application layer and requires no modification to existing applications, middleware, or messages. For each application component collocated at its hosting server, a SRAMD monitor traces the component's packet-level traffic unobtrusively, summarizes its local resource utilization and performance (e.g. response time) online, performs interactive queries (e.g. per- request resource utilization) to locate possible bottlenecks on- demand, and discovers inter-component dependence relationships statistically. The SRAMD controller simply aggregates reports from distributed monitors to construct real-time application topologies with rich runtime information. We have developed mechanisms to decentralize the computation overhead and minimize the communication cost in the monitoring and diagnosis process, and two schemes to discover application component dependency relationships in different scenarios. The SRAMD tool offers an alternative to server logs and message-level traces for service monitoring and performance diagnosis.
34878858
Dimensionality reduction of unsupervised data
Dimensionality reduction is an important problem for efficient handling of large databases. Many feature selection methods exist for supervised data having class information. Little work has been done for dimensionality reduction of unsupervised data in which class information is not available. Principal component analysis (PCA) is often used. However, PCA creates new features. It is difficult to obtain intuitive understanding of the data using the new features only. We are concerned with the problem of determining and choosing the important original features for unsupervised data. Our method is based on the observation that removing an irrelevant feature from the feature set may not change the underlying concept of the data, but not so otherwise. We propose an entropy measure for ranking features, and conduct extensive experiments to show that our method is able to find the important features. Also it compares well with a similar feature ranking method (Relief) that requires class information unlike our method.
16336933
Adaptive Video Watermarking Scheme
In this paper, a new adaptive watermarking scheme for uncompressed and compressed video is presented. The proposed method embeds the watermark in DC components by quantizing DC coefficients to reference values, instead of embedding the watermark in AC components. To increase robustness and perceptual invisibility, adaptive quantization control strategy (AQCS) and error compensation strategy (ECS) are introduced. Experimental results demonstrate the proposed watermarking scheme is robust and invisible, and is feasible for real time system implementation.
5884956
Query Selection Techniques for Efficient Crawling of Structured Web Sources
The high quality, structured data from Web structured sources is invaluable for many applications. Hidden Web databases are not directly crawlable by Web search engines and are only accessible through Web query forms or via Web service interfaces. Recent research efforts have been focusing on understanding these Web query forms. A critical but still largely unresolved question is: how to efficiently acquire the structured information inside Web databases through iteratively issuing meaningful queries? In this paper we focus on the central issue of enabling efficient Web database crawling through query selection, i.e. how to select good queries to rapidly harvest data records from Web databases. We model each structured Web database as a distinct attribute-value graph. Under this theoretical framework, the database crawling problem is transformed into a graph traversal one that follows "relational" links. We show that finding an optimal query selection plan is equivalent to finding a Minimum Weighted Dominating Set of the corresponding database graph, a well-known NP-Complete problem. We propose a suite of query selection techniques aiming at optimizing the query harvest rate. Extensive experimental evaluations over real Web sources and simulations over controlled database servers validate the effectiveness of our techniques and provide insights for future efforts in this