29257622
Use of Built-in Features in the Interpretation of High-dimensional Cancer Diagnosis Data
Cancer diagnosis data, for example microarray gene expression profiling data and proteomic profiling data, are often described by thousands of features. To computationally make a diagnosis for new samples, these data are usually input to a learning algorithm, the algorithm then induces a classifier, the classifier then predicts a class label for any test sample. As the data is so high-dimensional, most of the resulting classifiers are very complicated particularly those based on kernel-functions such as support vector machines---the interpretation of the decision results must need all the features to be involved. In this paper, we discuss built-in features and use them to concisely characterize the data and to easily interpret the decisions. Built-in features are features that are used only in the classifiers, and that are only a small subset of the original features, e.g., the features in a decision tree. So, the notion of built-in features is different from input features and also from original features. As there is a significant reduction from the huge size of original features to a small number of relevant features, the complexity of the interpretation can be much eased. The use of built-in features also provides much potential for elucidating the translation between raw data and clinically useful knowledge. In this paper, we also report that the performance of classifiers using built-in features tends to remain stable even input feature space changes, but other types of classifiers fluctuate their performance. So, once again, we promote the use of classifiers that use built-in features since the algorithms can avoid the existing hard problem of selecting best number of features for learning.
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.
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.
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.
14767737
Dynamic co-scheduling of distributed computation and replication
We are interested in developing the infrastructural tools that allow a distributed data intensive computing environment to be shared by a group of collaborating but geographically separated researchers in an interactive manner, as opposed to a batch mode of operation. However, without advanced reservation, it is difficult to assure a certain level of performance on a large number of shared and heterogeneous servers. To achieve scalable parallel speedups in this scenario, we must closely integrate the management of computation and runtime data movement. In this paper, we first define the canonical scheduling problem for datasets distributed with k-way replication in the wide area. We then develop a dynamic co-scheduling algorithm that integrates the scheduling of computation and data movement. Using time-varying visualization as the driving application, we demonstrate that our co-scheduling approach improves not only application performance but also server utilization at a very reasonable cost.
40702608
Feature Selection for Clustering
Clustering is an important data mining task. Data mining often concerns large and high-dimensional data but unfortunately most of the clustering algorithms in the literature are sensitive to largeness or high-dimensionality or both. Different features affect clusters differently, some are important for clusters while others may hinder the clustering task. An efficient way of handling it is by selecting a subset of important features. It helps in finding clusters efficiently, understanding the data better and reducing data size for efficient storage, collection and processing. The task of finding original important features for unsupervised data is largely untouched. Traditional feature selection algorithms work only for supervised data where class information is available. For unsupervised data, without class information, often principal components (PCs) are used, but PCs still require all features and they may be difficult to understand. Our approach: first features are ranked according to their importance on clustering and then a subset of important features are selected. For large data we use a scalable method using sampling. Empirical evaluation shows the effectiveness and scalability of our approach for benchmark and synthetic data sets.
46879014
Feature Selection with Selective Sampling
7752326
Efficiently handling feature redundancy in high-dimensional data
High-dimensional data poses a severe challenge for data mining. Feature selection is a frequently used technique in pre-processing high-dimensional data for successful data mining. Traditionally, feature selection is focused on removing irrelevant features. However, for high-dimensional data, removing redundant features is equally critical. In this paper, we provide a study of feature redundancy in high-dimensional data and propose a novel correlation-based approach to feature selection within the filter model. The extensive empirical study using real-world data shows that the proposed approach is efficient and effective in removing redundant and irrelevant features.
10417855
Bias analysis in text classification for highly skewed data
Feature selection is often applied to high-dimensional data as a preprocessing step in text classification. When dealing with highly skewed data, we observe that typical feature selection metrics like information gain or chi-squared are biased toward selecting features for the minor class, and the metric of bi-normal separation can select features for both minor and major classes. In this work, we investigate how these feature selection metrics impact on the performance of frequently used classifiers such as decision trees, naive bayes, and support vector machines via bias analysis for highly skewed data. Three types of biases are metric bias, class bias, and classifier bias. Extensive experiments are designed to understand how these biases can be employed in concert and efficiently to achieve good classification performance. We report our findings and present recommended approaches to text classification based on bias analysis and the empirical study.