11034725
Data Mining Tools for Biological Sequences
We describe a methodology, as well as some related data mining tools, for analyzing sequence data. The methodology comprises three steps: (a) generating candidate features from the sequences, (b) selecting relevant features from the candidates, and (c) integrating the selected features to build a system to recognize specific properties in sequence data. We also give relevant techniques for each of these three steps. For generating candidate features, we present various types of features based on the idea of k-grams. For selecting relevant features, we discuss signal-to-noise, t-statistics, and entropy measures, as well as a correlation-based feature selection method. For integrating selected features, we use machine learning methods, including C4.5, SVM, and Naive Bayes. We illustrate this methodology on the problem of recognizing translation initiation sites. We discuss how to generate and select features that are useful for understanding the distinction between ATG sites that are translation initiation sites and those that are not. We also discuss how to use such features to build reliable systems for recognizing translation initiation sites in DNA sequences.
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.
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.
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.
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.
2276859
Dynamically Generating a Protein Entity Dictionary Using Online Resources
With the overwhelming amount of biological knowledge stored in free text, natural language processing (NLP) has received much attention recently to make the task of managing information recorded in free text more feasible. One requirement for most NLP systems is the ability to accurately recognize biological entity terms in free text and the ability to map these terms to corresponding records in databases. Such task is called biological named entity tagging. In this paper, we present a system that automatically constructs a protein entity dictionary, which contains gene or protein names associated with UniProt identifiers using online resources. The system can run periodically to always keep up-to-date with these online resources. Using online resources that were available on Dec. 25, 2004, we obtained 4,046,733 terms for 1,640,082 entities. The dictionary can be accessed from the following website: http://biocreative.ifsm.umbc.edu/biothesaurus/.
52857898
Schema Discovery for Semistructured Data
To formulate a meaningful query on semistructured data, such as on the Web, that matches some of the source's structure, we need first to discover something about how the information is represented in the source. This is referred to as schema discovery and was considered for a single object recently. In the case of multiple objects, the task of schema discovery is to identify typical structuring information of those objects as a whole. We motivate the schema discovery in this general setting and propose a framework and algorithm for it. We apply the framework to a real Web database, the Internet Movies Database, to discover typical schema of most voted movies.
16551486
Feature Extraction for Image Mining
Due to the digitization of data and advances in technology, it has become extremely easy to obtain and store large quantities of data, particularly Multimedia data. Fields ranging from Commercial to Military need to analyze these data in an efficient and fast manner. Presently, tools for mining images are few and require human intervention. Feature selection and extraction is the pre-processing step of Image Mining. Obviously this is a critical step in the entire scenario of Image Mining. Our approach to mine from Images – to extract patterns and derive knowledge from large collections of images, deals mainly with identification and extraction of unique features for a particular domain. Though there are various features available, the aim is to identify the best features and thereby extract relevant information from the images. We have tried various methods for extraction; the features extracted and the techniques used are evaluated for their contribution to solving the problem. Experimental results show that the features used are sufficient to identify the patterns from the Images. The extracted features were evaluated for goodness and tested on test images. An interactive system was developed which allows the user to define new features and to resolve uncertain regions.
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.
10149257
Semi-supervised Feature Selection via Spectral Analysis
Feature selection is an important task in effective data mining. A new challenge to feature selection is the socalled “small labeled-sample problem” in which labeled data is small and unlabeled data is large. The paucity of labeled instances provides insufficient information about the structure of the target concept, and can cause supervised feature selection algorithms to fail. Unsupervised feature selection algorithms can work without labeled data. However, these algorithms ignore label information, which may lead to performance deterioration. In this work, we propose to use both (small) labeled and (large) unlabeled data in feature selection, which is a topic has not yet been addressed in feature selection research. We present a semi-supervised feature selection algorithm based on spectral analysis. The algorithm exploits both labeled and unlabeled data through a regularization framework, which provides an effective way to address the “small labeled-sample” problem. Experimental results demonstrated the efficacy of our approach and confirmed that small labeled samples can help feature selection with unlabeled data. Keyword: Feature Selection, Semi-supervised Learning, Machine Learning, Spectral Analysis