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ABSTRACT: In the present paper, we use data mining methods to address two challenges in the sharing and integration of data from electrophysiological (ERP) studies of human brain function. The first challenge, ERP metric matching, is to identify correspondences among distinct summary features ("metrics") in ERP datasets from different research labs. The second challenge, ERP pattern matching, is to align the ERP patterns or "components" in these datasets. We address both challenges within a unified framework. The utility of this framework is illustrated in a series of experiments using ERP datasets that are designed to simulate heterogeneities from three sources: (a) different groups of subjects with distinct simulated patterns of brain activity, (b) different measurement methods, i.e, alternative spatial and temporal metrics, and (c) different patterns, reflecting the use of alternative pattern analysis techniques. Unlike real ERP data, the simulated data are derived from known source patterns, providing a gold standard for evaluation of the proposed matching methods. Using this approach, we demonstrate that the proposed method outperforms well-known existing methods, because it utilizes cluster-based structure and thus achieves finer-grained representation of the multidimensional (spatial and temporal) attributes of ERP data.
Neurocomputing 09/2012; 92:156-169. · 1.58 Impact Factor
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ABSTRACT: In this paper, we present a data mining approach to address challenges in the matching of heterogeneous datasets. In particular, we propose solutions to two problems that arise in integrating information from different results of scientific research. The first problem, attribute matching, involves discovery of correspondences among distinct numeric features (attributes) that are used to characterize datasets that have been collected and analyzed in different research labs. The second problem, cluster matching, involves discovery of matchings between patterns (clusters) across datasets. We treat both of these problems together as a multi-objective optimization problem. A multi-objective metaheuristics algorithm is described to find the optimal solution and compared with the genetic algorithm. The utility of this approach is demonstrated in a series of experiments using synthetic and realistic datasets that are designed to simulate heterogeneous data from different sources.
Journal on data semantics. 08/2012; 1(2):133-145.
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ABSTRACT: Identification and characterization of the important roles microRNAs (miRNAs) perform in human cancer is an increasingly active research area. Unfortunately, prediction of miRNA target genes remains a challenging task to cancer researchers. Current processes are time-consuming, error-prone, and subject to biologists' limited prior knowledge. Therefore, we propose a domain-specific knowledge base built upon Ontology for MicroRNA Targets (OMIT) to facilitate knowledge acquisition in miRNA target gene prediction. We describe the ontology design, semantic annotation and data integration, and user-friendly interface and conclude that the OMIT system can assist biologists in unraveling the important roles of miRNAs in human cancer. Thus, it will help clinicians make sound decisions when treating cancer patients.
Pharmaceutical Research 08/2011; 28(12):3101-4. · 4.09 Impact Factor
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ABSTRACT: Hemiparesis is the most common impairment after stroke, and the initial severity of hemiparesis had been the strongest predictor of neuromotor functional recovery level. However, the intervention response of stroke survivors does not always correlate with their initial level of impairment, which implies the existence of other factors that may significantly affect stroke survivors' recovery process. It is critical to consider these factors in a principled, comprehensive way so that physical rehabilitation (PR) researchers may predict which stroke survivors will respond best to therapy and, as a result, to determine if a particular type of therapy is a more optimal match. Currently, such prediction is primarily a manual process and remains a challenging task to PR researchers and clinicians. Based upon a domain-specific ontology, NeuMORE, we propose a computing framework that aims to facilitate knowledge acquisition from existing sources via semantics-enhanced data mining (SEDM) techniques. It will assist PR researchers and clinicians in better predicting stroke survivors' neuromotor functional recovery level, and will help physical therapists customize most effective intervention therapy plans for individual stroke survivors.
Bioinformatics and Biomedicine Workshops (BIBMW), 2010 IEEE International Conference on; 01/2011
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[show abstract]
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ABSTRACT: Hemiparesis is the most common impairment after stroke, the leading cause of adult disability in the United States. The initial severity of hemiparesis had been the strongest predictor of neuromotor functional recovery level. However, the intervention response of stroke survivors does not always correlate with their initial level of impairment. This implies the existence of other factors that may significantly affect stroke survivors' recovery process. In order to design targeting intervention therapy strategies, it is critical to consider these factors in a principled, comprehensive way so that physical rehabilitation (PR) researchers may predict which stroke survivors will respond best to therapy and subsequently, determine if a particular type of therapy is a more optimal match. Currently, such prediction is primarily a manual process and remains a challenging task to PR researchers and clinicians. We propose a computing framework based upon a domain-specific ontology. This framework aims to facilitate knowledge acquisition from existing sources via semantics-enhanced data mining (SEDM) techniques. As a result, it will assist PR researchers and clinicians in better predicting stroke survivors' neuromotor functional recovery level, and will help physical therapists customize most effective intervention therapy plans for individual stroke survivors.
Bioinformatics and Biomedicine Workshops (BIBMW), 2010 IEEE International Conference on; 01/2011
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Database and Expert Systems Applications - 22nd International Conference, DEXA 2011, Toulouse, France, August 29 - September 2, 2011, Proceedings, Part II; 01/2011
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11th IEEE International Conference on Data Mining, ICDM 2011, Vancouver, BC, Canada, December 11-14, 2011; 01/2011
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On the Move to Meaningful Internet Systems: OTM 2011 - Confederated International Conferences: CoopIS, DOA-SVI, and ODBASE 2011, Hersonissos, Crete, Greece, October 17-21, 2011, Proceedings, Part II; 01/2011
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Christopher Townsend,
Jingshan Huang,
Dejing Dou,
Shivraj Dalvi,
Patrick J. Hayes,
Lei He,
Wen-chang Lin, Haishan Liu,
Robert Rudnick,
Hardik Shah,
Hao Sun,
Xiaowei Wang,
Ming Tan
[show abstract]
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ABSTRACT: The identification and characterization of important roles microRNAs (miRNAs) played in human cancer is an increasingly active
area in medical informatics. In particular, the prediction of miRNA target genes remains a challenging task to cancer researchers.
Current efforts have focused on manual knowledge acquisition from existing miRNA databases, which is time-consuming, error-prone,
and subject to biologists’ limited prior knowledge. Therefore, an effective knowledge acquisition has been inhibited. We propose
a computing framework based on the Ontology for MicroRNA Target Prediction (OMIT), the very first ontology in miRNA domain. With such formal knowledge representation, it is thus possible to facilitate knowledge discovery
and sharing from existing sources. Consequently, the framework aims to assist biologists in unraveling important roles of
miRNAs in human cancer, and thus to help clinicians in making sound decisions when treating cancer patients.
10/2010: pages 1160-1167;
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ABSTRACT: In this paper, we present a method for identifying correspondences, or mappings, between alternative features of brainwave activity in event-related potentials (ERP) data. The goal is to simulate mapping across results from heterogeneous methods that might be used in different neuroscience research labs. The input to the mapping consists of two ERP datasets whose spatiotemporal characteristics are captured by alternative sets of features, that is, summary spatial and temporal measures capturing distinct neural patterns that are linked to concepts in a set of ERP ontologies, called NEMO (Neural ElectroMagnetic Ontologies) [3, 6]. The feature value vector of each summary metric is transformed into a point-sequence curve, and clustering is performed to extract similar subsequences (clusters) representing the neural patterns that can then be aligned across datasets. Finally, the similarity between measures is derived by calculating the similarity between corresponding point-sequence curves. Experiment results showed that the proposed approach is robust and has achieved significant improvement on precision than previous algorithms.
Lecture Notes in Computer Science 05/2010; 6119:43-54.
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On the Move to Meaningful Internet Systems, OTM 2010 - Confederated International Conferences: CoopIS, IS, DOA and ODBASE, Hersonissos, Crete, Greece, October 25-29, 2010, Proceedings, Part II; 01/2010
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Christopher Townsend,
Jingshan Huang,
Dejing Dou,
Shivraj Dalvi,
Patrick J Hayes,
Lei He,
Wen-Chang Lin, Haishan Liu,
Robert Rudnick,
Hardik Shah,
Hao Sun,
Xiaowei Wang,
Ming Tan,
Academia Sinica,
Taipei Taiwan
[show abstract]
[hide abstract]
ABSTRACT: The identification and characterization of important roles microRNAs (miRNAs) played in human cancer is an increasingly active area in medical informatics. In particular, the prediction of miRNA tar-get genes remains a challenging task to cancer researchers. Current efforts have focused on manual knowledge acquisition from existing miRNA databases, which is time-consuming, error-prone, and subject to biolo-gists' limited prior knowledge. Therefore, an effective knowledge acqui-sition has been inhibited. We propose a computing framework based on the Ontology for MicroRNA Target Prediction (OMIT), the very first ontology in miRNA domain. With such formal knowledge representation, it is thus possible to facilitate knowledge discovery and sharing from ex-isting sources. Consequently, the framework aims to assist biologists in unraveling important roles of miRNAs in human cancer, and thus to help clinicians in making sound decisions when treating cancer patients.
LNCS. 01/2010; 6427:1162-1169.
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Advances in Artificial Intelligence, 20th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2007, Montreal, Canada, May 28-30, 2007, Proceedings; 01/2007
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ABSTRACT: Development of internet and Web have resulted in many distributed information resources which in general are structurally and seman-tically heterogeneous even in the same domain. However, hetero-geneity itself has not been studied in a formal way so that the rep-resentation of different kinds of heterogeneities can be generically processed by other programs automatically. Most descriptions and categorization schemes of heterogeneities were given in languages specific to different research groups. We believe that efforts in-vested in a thorough research of heterogeneity can ultimately bene-fit both data integration and data mining communities. In this paper we give a brief survey of various ways to categorize heterogene-ity in the literature, and then performed a case study on detecting a specific class of heterogeneity in the setting of Semantic Web ontologies–the one that can be discovered by only data-driven ap-proaches. Finally we propose an automatic ontology matching sys-tem that can detect this heterogeneity by using redescription min-ing techniques. We also believe that automatic ontology matching process is a helpful step in tasks of mining multiple information sources in the heterogeneous scenario.
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ABSTRACT: We describe a first-generation ontology for
representation and integration of event-related brain potentials (ERPs). The ontology is designed following OBO “best practices” and is augmented with tools to perform ontology-based labeling and annotation of ERP data, and a database that enables semantically based reasoning over these data. Because certain high-level concepts in the ERP domain are illdefined, we have developed methods to support coordinated updates to each of these three components. This approach consists of “top-down” (knowledge-driven) design and implementation, followed by “bottom-up” (data-driven) validation and refinement. Our goal is to build an ERP ontology that is logically valid, empirically sound, robust in application, and transparent to users. This ontology will be used to support sharing and meta-analysis of EEG and MEG data collected within our Neural Electromagnetic Ontologies (NEMO) project.
Nature Precedings.
