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PubMed2005

Recording human evoked potentials that follow the pitch contour of a natural vowel.

We investigated whether pitch-synchronous neural activity could be recorded in humans, with a natural vowel and a vowel in which the fundamental frequency was suppressed. Small variations of speech periodicity were detected in the evoked responses using a fine structure spectrograph (FSS). A significant response (P < 0.001) was measured in all seven normal subjects even when the fundamental frequency was suppressed, and it very accurately tracked the acoustic pitch contour (normalized mean absolute error < 0.57%). Small variations in speech periodicity, which humans can detect, are therefore available to the perceptual system as pitch-synchronous neural firing. These findings suggest that the measurement of pitch-evoked responses may be a viable tool for objective speech audiometry.

Adult↗
PubMedDate not supplied

Text mining biomedical literature for discovering gene-to-gene relationships: a comparative study of algorithms.

Partitioning closely related genes into clusters has become an important element of practically all statistical analyses of microarray data. A number of computer algorithms have been developed for this task. Although these algorithms have demonstrated their usefulness for gene clustering, some basic problems remain. This paper describes our work on extracting functional keywords from MEDLINE for a set of genes that are isolated for further study from microarray experiments based on their differential expression patterns. The sharing of functional keywords among genes is used as a basis for clustering in a new approach called BEA-PARTITION in this paper. Functional keywords associated with genes were extracted from MEDLINE abstracts. We modified the Bond Energy Algorithm (BEA), which is widely accepted in psychology and database design but is virtually unknown in bioinformatics, to cluster genes by functional keyword associations. The results showed that BEA-PARTITION and hierarchical clustering algorithm outperformed k-means clustering and self-organizing map by correctly assigning 25 of 26 genes in a test set of four known gene groups. To evaluate the effectiveness of BEA-PARTITION for clustering genes identified by microarray profiles, 44 yeast genes that are differentially expressed during the cell cycle and have been widely studied in the literature were used as a second test set. Using established measures of cluster quality, the results produced by BEA-PARTITION had higher purity, lower entropy, and higher mutual information than those produced by k-means and self-organizing map. Whereas BEA-PARTITION and the hierarchical clustering produced similar quality of clusters, BEA-PARTITION provides clear cluster boundaries compared to the hierarchical clustering. BEA-PARTITION is simple to implement and provides a powerful approach to clustering genes or to any clustering problem where starting matrices are available from experimental observations.

Abstracting and Indexing↗
PubMed2004

Concept-oriented indexing of video databases: toward semantic sensitive retrieval and browsing.

Digital video now plays an important role in medical education, health care, telemedicine and other medical applications. Several content-based video retrieval (CBVR) systems have been proposed in the past, but they still suffer from the following challenging problems: semantic gap, semantic video concept modeling, semantic video classification, and concept-oriented video database indexing and access. In this paper, we propose a novel framework to make some advances toward the final goal to solve these problems. Specifically, the framework includes: 1) a semantic-sensitive video content representation framework by using principal video shots to enhance the quality of features; 2) semantic video concept interpretation by using flexible mixture model to bridge the semantic gap; 3) a novel semantic video-classifier training framework by integrating feature selection, parameter estimation, and model selection seamlessly in a single algorithm; and 4) a concept-oriented video database organization technique through a certain domain-dependent concept hierarchy to enable semantic-sensitive video retrieval and browsing.

Abstracting and Indexing↗
PubMed2005

A memory learning framework for effective image retrieval.

Most current content-based image retrieval systems are still incapable of providing users with their desired results. The major difficulty lies in the gap between low-level image features and high-level image semantics. To address the problem, this study reports a framework for effective image retrieval by employing a novel idea of memory learning. It forms a knowledge memory model to store the semantic information by simply accumulating user-provided interactions. A learning strategy is then applied to predict the semantic relationships among images according to the memorized knowledge. Image queries are finally performed based on a seamless combination of low-level features and learned semantics. One important advantage of our framework is its ability to efficiently annotate images and also propagate the keyword annotation from the labeled images to unlabeled images. The presented algorithm has been integrated into a practical image retrieval system. Experiments on a collection of 10,000 general-purpose images demonstrate the effectiveness of the proposed framework.

Algorithms↗
PubMed2005

A unified framework for image retrieval using keyword and visual features.

In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.

Algorithms↗
PubMed2004

Using XML technology for the ontology-based semantic integration of life science databases.

Several hundred internet accessible life science databases with constantly growing contents and varying areas of specialization are publicly available via the internet. Database integration, consequently, is a fundamental prerequisite to be able to answer complex biological questions. Due to the presence of syntactic, schematic, and semantic heterogeneities, large scale database integration at present takes considerable efforts. As there is a growing apprehension of extensible markup language (XML) as a means for data exchange in the life sciences, this article focuses on the impact of XML technology on database integration in this area. In detail, a general architecture for ontology-driven data integration based on XML technology is introduced, which overcomes some of the traditional problems in this area. As a proof of concept, a prototypical implementation of this architecture based on a native XML database and an expert system shell is described for the realization of a real world integration scenario.

Algorithms↗
PubMed2004

Multidimensional text classification for drug information.

This paper proposes a multidimensional model for classifying drug information text documents. The concept of multidimensional category model is introduced for representing classes. In contrast with traditional flat and hierarchical category models, the multidimensional category model classifies each document using multiple predefined sets of categories, where each set corresponds to a dimension. Since a multidimensional model can be converted to flat and hierarchical models, three classification approaches are possible, i.e., classifying directly based on the multidimensional model and classifying with the equivalent flat or hierarchical models. The efficiency of these three approaches is investigated using drug information collection with two different dimensions: 1) drug topics and 2) primary therapeutic classes. In the experiments, k-nearest neighbor, naive Bayes, and two centroid-based methods are selected as classifiers. The comparisons among three approaches of classification are done using two-way analysis of variance, followed by the Scheffé's test for post hoc comparison. The experimental results show that multidimensional-based classification performs better than the others, especially in the presence of a relatively small training set. As one application, a category-based search engine using the multidimensional category concept was developed to help users retrieve drug information.

Abstracting and Indexing↗
PubMed2004

Integrating context-aware public displays into a mobile hospital information system.

Hospitals are convenient settings for deployment of ubiquitous computing technology. Not only are they technology-rich environments, but their workers experience a high level of mobility resulting in information infrastructures with artifacts distributed throughout the premises. Hospital information systems (HISs) that provide access to electronic patient records are a step in the direction of providing accurate and timely information to hospital staff in support of adequate decision-making. This has motivated the introduction of mobile computing technology in hospitals based on designs which respond to their particular conditions and demands. Among those conditions is the fact that worker mobility does not exclude the need for having shared information artifacts particular locations. In this paper, we extend a handheld-based mobile HIS with ubiquitous computing technology and describe how public displays are integrated with handheld and the services offered by these devices. Public displays become aware of the presence of physicians and nurses in their vicinity and adapt to provide users with personalized, relevant information. An agent-based architecture allows the integration of proactive components that offer information relevant to the case at hand, either from medical guidelines or previous similar cases.

Artificial Intelligence↗
PubMed2003

A rapid speech synthesizing software on a PDA for Japanese with speech impairments.

We developed a Japanese-language, rapid synthesizing software application for use on a personal digital assistant. It has an unrestricted vocabulary and can synthesize words and sentences within 3 s. Eight hundred common sentences and words are preregistered. By touching the first character at the head of a preregistered sentence or word from an on-screen Kana (Japanese alphabet) chart, the user can select the sentence or word to be spoken. Characters on the Kana chart can also be input sequentially. Two Japanese subjects with speech impairments rated the device highly for its portability and quick response. Whereas communication previously had to be done by writing or sign language, it was easy for listeners with or without specialized training in communication with persons with speech impairments to understand the output from this device, making conversation easier which, in turn, improved the quality of life and social activity of these persons with speech impairments.

Asian People↗
PubMed2004

Text generation from Taiwanese Sign Language using a PST-based language model for augmentative communication.

This paper proposes a novel approach to the generation of Chinese sentences from ill-formed Taiwanese Sign Language (TSL) for people with hearing impairments. First, a sign icon-based virtual keyboard is constructed to provide a visualized interface to retrieve sign icons from a sign database. A proposed language model (LM), based on a predictive sentence template (PST) tree, integrates a statistical variable n-gram LM and linguistic constraints to deal with the translation problem from ill-formed sign sequences to grammatical written sentences. The PST tree trained by a corpus collected from the deaf schools was used to model the correspondence between signed and written Chinese. In addition, a set of phrase formation rules, based on trigger pair category, was derived for sentence pattern expansion. These approaches improved the efficiency of text generation and the accuracy of word prediction and, therefore, improved the input rate. For the assessment of practical communication aids, a reading-comprehension training program with ten profoundly deaf students was undertaken in a deaf school in Tainan, Taiwan. Evaluation results show that the literacy aptitude test and subjective satisfactory level are significantly improved.

Adolescent↗
PubMed2004

Estimating research productivity and quality in assistive technology: a bibliometric analysis spanning four decades.

OBJECTIVE: Conduct a quantitative assessment of the number of papers contained in MEDLINE related to selected types of assistive technology (AT), and to identify journals publishing significant numbers of papers related to AT, and evaluate them with quantitative productivity and quality measures. DESIGN: Consecutive sample of all papers in MEDLINE identified by standard medical subject headings for selected types of AT from 1963-2003. MAIN OUTCOME MEASURES: Number of journals carrying AT papers, papers per journal (both total number and those specific to AT), journal impact factor, circulation, and number of AT citations per year over time for each area of AT. RESULTS: We present search terms, estimates of the numbers of AT citations in MEDLINE, the journals most likely to contain articles related to AT, journal impact factors, and journal circulations (when available). We also present the number of citations in various areas of AT over time from 1963-2003. Suggestions are presented for possible future modifications of the MEDLINE controlled vocabulary, based on terminology used in existing AT classifications schemes, such as ISO 9999. CONCLUSION: Research papers in the areas of AT examined showed publication across a wide variety of journals. There are a number of journals publishing articles in AT that have impact factors above the median. Some areas of AT have shown an increase in publications per year over time, while others have shown a more constant level of productivity.

Abstracting and Indexing↗
PubMed2004

Error-tolerant sign retrieval using visual features and maximum a posteriori estimation.

This paper proposes an efficient error-tolerant approach to retrieving sign words from a Taiwanese Sign Language (TSL) database. This database is tagged with visual gesture features and organized as a multilist code tree. These features are defined in terms of the visual characteristics of sign gestures by which they are indexed for sign retrieval and displayed using an anthropomorphic interface. The maximum a posteriori estimation is exploited to retrieve the most likely sign word given the input feature sequence. An error-tolerant mechanism based on mutual information criterion is proposed to retrieve a sign word of interest efficiently and robustly. A user-friendly anthropomorphic interface is also developed to assist learning TSL. Several experiments were performed in an educational environment to investigate the system's retrieval accuracy. Our proposed approach outperformed a dynamic programming algorithm in its task and shows tolerance to user input errors.

Algorithms↗
PubMed2004

An optimization criterion for generalized discriminant analysis on undersampled problems.

An optimization criterion is presented for discriminant analysis. The criterion extends the optimization criteria of the classical Linear Discriminant Analysis (LDA) through the use of the pseudoinverse when the scatter matrices are singular. It is applicable regardless of the relative sizes of the data dimension and sample size, overcoming a limitation of classical LDA. The optimization problem can be solved analytically by applying the Generalized Singular Value Decomposition (GSVD) technique. The pseudoinverse has been suggested and used for undersampled problems in the past, where the data dimension exceeds the number of data points. The criterion proposed in this paper provides a theoretical justification for this procedure. An approximation algorithm for the GSVD-based approach is also presented. It reduces the computational complexity by finding subclusters of each cluster and uses their centroids to capture the structure of each cluster. This reduced problem yields much smaller matrices to which the GSVD can be applied efficiently. Experiments on text data, with up to 7,000 dimensions, show that the approximation algorithm produces results that are close to those produced by the exact algorithm.

Algorithms↗
PubMed2004

Generalizing discriminant analysis using the generalized singular value decomposition.

Discriminant analysis has been used for decades to extract features that preserve class separability. It is commonly defined as an optimization problem involving covariance matrices that represent the scatter within and between clusters. The requirement that one of these matrices be nonsingular limits its application to data sets with certain relative dimensions. We examine a number of optimization criteria, and extend their applicability by using the generalized singular value decomposition to circumvent the nonsingularity requirement. The result is a generalization of discriminant analysis that can be applied even when the sample size is smaller than the dimension of the sample data. We use classification results from the reduced representation to compare the effectiveness of this approach with some alternatives, and conclude with a discussion of their relative merits.

Algorithms↗
PubMed2005

Grammatical inference in bioinformatics.

Bioinformatics is an active research area aimed at developing intelligent systems for analyses of molecular biology. Many methods based on formal language theory, statistical theory, and learning theory have been developed for modeling and analyzing biological sequences such as DNA, RNA, and proteins. Especially, grammatical inference methods are expected to find some grammatical structures hidden in biological sequences. In this article, we give an overview of a series of our grammatical approaches to biological sequence analyses and related researches and focus on learning stochastic grammars from biological sequences and predicting their functions based on learned stochastic grammars.

Algorithms↗
PubMed2005

Learning deterministic finite automata with a smart state labeling evolutionary algorithm.

Learning a Deterministic Finite Automaton (DFA) from a training set of labeled strings is a hard task that has been much studied within the machine learning community. It is equivalent to learning a regular language by example and has applications in language modeling. In this paper, we describe a novel evolutionary method for learning DFA that evolves only the transition matrix and uses a simple deterministic procedure to optimally assign state labels. We compare its performance with the Evidence Driven State Merging (EDSM) algorithm, one of the most powerful known DFA learning algorithms. We present results on random DFA induction problems of varying target size and training set density. We also studythe effects of noisy training data on the evolutionary approach and on EDSM. On noise-free data, we find that our evolutionary method outperforms EDSM on small sparse data sets. In the case of noisy training data, we find that our evolutionary method consistently outperforms EDSM, as well as other significant methods submitted to two recent competitions.

Algorithms↗
PubMed2005

Parsing with probabilistic strictly locally testable tree languages.

Probabilistic k-testable models (usually known as k-gram models in the case of strings) can be easily identified from samples and allow for smoothing techniques to deal with unseen events during pattern classification. In this paper, we introduce the family of stochastic k-testable tree languages and describe how these models can approximate any stochastic rational tree language. The model is applied to the task of learning a probabilistic k-testable model from a sample of parsed sentences. In particular, a parser for a natural language grammar that incorporates smoothing is shown.

Algorithms↗