http://idea.library.drexel.edu/handle/1860/726 http://idea.library.drexel.edu/retrieve/4809 http://idea.library.drexel.edu/handle/1860/726 Search the Channel search http://idea.library.drexel.edu/simple-search http://idea.library.drexel.edu/handle/1860/2756 Title: Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks <br/> <br/>Authors: Hu, Xiaohua; Li, Guangren; Yoo, Illhoi; Zhang, Xiaodan; Hu, Xuheng <br/> <br/>Abstract: The problem of mining undiscovered public knowledge from biomedical literature was exemplified by Swanson’s pioneering work on Raynaud disease/fish-oil discovery in 1986. Since then, there have been many approaches to mine undiscovered public knowledge from biomedical literature. This paper presents a semantic-based approach for mining undiscovered public knowledge from bio-medical literature. The method takes advantages of the biomedical ontologies, MeSH and UMLS, as the source of semantic knowledge. A prototype system Biomedical Semantic-based Knowledge Discovery System (Bio-SbKDS) is designed to uncover novel hypothe-sis/connections hidden in the biomedical literature. Using the semantic types and semantic relations of the bio-medical concepts, Bio-SbKDS can identify the relevant concepts collected from Medline and generate the novel hypothesis between these concepts. Bio-SbKDS suc-cessfully replicates Dr. Swanson’s two famous discover-ies: Raynaud disease/fish oil and migraine/magnesium. Compared with previous approaches, our method searches much less articles, generates much less but more relevant novel hypotheses, requires much less human in-tervention in the discovery procedure. http://idea.library.drexel.edu/handle/1860/2754 Title: A comparative study of ontology based term similarity measures on PubMed document clustering <br/> <br/>Authors: Zhang, Xiaodan; Jing, Liping; Hu, Xiaohua; Ng, Michael; Zhou, Xiaohua <br/> <br/>Abstract: Recent research shows that ontology as background knowledge can improve document clustering quality with its concept hierarchy knowledge. Previous studies take term semantic similarity as an important measure to incorporate domain knowledge into clustering process such as clustering initialization and term re-weighting. However, not many studies have been focused on how different types of term similarity measures affect the clustering performance for a certain domain. In this paper, we conduct a comparative study on how different semantic similarity measures of term including path based similarity measure, information content based similarity measure and feature based similarity measure affect document clustering. We evaluate term re-weighting as an important method to integrate domain ontology to clustering process. Meanwhile, we apply k-means clustering on one real-world text dataset, our own corpus generated from PubMed. Experiment results on 8 different semantic measures have shown that: (1) there is no a certain type of similarity measures that significantly outperforms the others; (2) Several similarity measures have rather more stable performance than the others; (3) term re-weighting has positive effects on medical document clustering, but might not be significant when documents are short of terms. http://idea.library.drexel.edu/handle/1860/2745 Title: A study on content and management style of corporate blogs <br/> <br/>Authors: Ma, Shanshan; Zhang, Qiping <br/> <br/>Abstract: Corporate blogs are used by companies to talk with customers. We did a study into 262 blog entries in 9 corporate blogs. The study revealed three corporate blog content types; three corporate blog management styles, and relatively shorter blog length and lower update frequency. http://idea.library.drexel.edu/handle/1860/2740 Title: Utilization of global ranking information in GraphBased biomedical literature clustering <br/> <br/>Authors: Zhang, Xiaodan; Hu, Xiaohua; Xia, Jiali; Zhou, Xiaohua; Achananuparp, Palakorn <br/> <br/>Abstract: In this paper, we explore how global ranking method in conjunction with local density method help identify meaningful term clusters from ontology enriched graph representation of biomedical literature corpus. One big problem with document clustering is how to discount the effects of class-unspecific general terms and strengthen the effects of class-specific core terms. We claim that running global ranking method on a well constructed term graph can identify class-specific core terms. In detail, PageRank and HITS are applied on a direct abstract-title graph to target class specific core terms. Then k dense terms clusters (graph) are identified from these terms. Finally, a document is assigned to the closest term graph. A series of experiments are conducted on a document corpus collected from PubMed. Experimental results show that our approach is very effective to identify class-specific core terms and thus help document clustering. http://idea.library.drexel.edu/handle/1860/2736 Title: The female-friendly public library: gender differences in adolescents’ uses and perceptions of U.S. public libraries <br/> <br/>Authors: Agosto, Denise E.; Paone, Kimberly L.; Ipock, Gretchen S. <br/> <br/>Abstract: This article reports the results of a written survey of ninety-seven female and male adolescents, ages fourteen through seventeen, at two U.S. public libraries. In addition to exploring gender-related variance in the reasons for which teenagers use public libraries, the survey investigated how frequently the respondents needed information relating to twelve major topic areas and how useful they considered public libraries in helping them to find information relating to these topics. For the most part, the results indicated no significant gender difference in the respondents’ reasons for using libraries or in their frequency of information needs. The only major gender difference was the girls’ tendency to rate libraries as more useful in helping them to meet their personal information needs, making public libraries “female-friendly spaces” for adolescent girls. The authors conclude with suggestions for helping both female and male adolescents realize the full potential of public libraries and public library services. http://idea.library.drexel.edu/handle/1860/2733 Title: Information technology incorporating emotion in dialogues <br/> <br/>Authors: Fowler, Caleb; Weber, Rosina O. <br/> <br/>Abstract: Human computer communication is sometimes difficult due to lack of emotion and diversification. At times when many research projects target the development of technology to support people without adult level cognitive abilities, repetitive, and emotionless communication may challenge any chances of success with the new technology. In this paper, we examine a case-based method that relies on the listener’s emotional context to recommend a communication strategy that is both diverse and embedded with relevant motivational aspects. http://idea.library.drexel.edu/handle/1860/2729 Title: Integrated approach to detect inconspicuous contents <br/> <br/>Authors: Weber, Rosina O.; Waldstein, Ilya; Deshpande, Amit; Proctor, Jason M. <br/> <br/>Abstract: This paper describes an integrated approach for detecting inconspicuous contents in text. Inconspicuous contents can be an opinion or goal that may be disguised in some way to mislead automated methods but keeps a clear message for humans (e.g., terrorist sites). Our methodology hypothesizes that patterns that convey inconspicuous contents can be extracted, represented, generalized, and matched in unknown text. The proposed approach is meant to complement data-intensive methods (e.g. clustering). Data-intensive methods are fast but are susceptible to variations in frequency, do not discern meaning, and require a large corpus for training. Our approach relies on manual engineering for natural language interpretation and pattern extraction using no more than ten examples, but is sufficiently fast to complement a real-time application. http://idea.library.drexel.edu/handle/1860/2699 Title: A novel approach for mining and fuzzy simulation of subnetworks from large biomolecular networks <br/> <br/>Authors: Hu, Xiaohua; Sokhansanj, Bahrad; Wu, Daniel; Tang, Yuchun <br/> <br/>Abstract: Understanding the biomolecular network implementing cellular function goes beyond the old dogma of “one gene: one function”; only through comprehensive system understanding can we predict the impact of genetic variation in the population, design effective disease therapeutics, and evaluate the potential side-effects of therapies. In this paper, we present a novel method to model the regulatory system that executes a cellular function, which can be represented as a biomolecular network. Our method consists of three steps. First, the biomolecular network is derived using data-mining approaches to extend the initial conceptual biomolecular network from the literature search, etc. Secondly, once the whole biomolecular network structure is complete, a novel scale-free network clustering approach is applied to obtain various subnetworks. Lastly, fuzzy rule based models are generated for the subnetworks and simulations are run to predict their behavior in the cellular context. The modeling results represent hypotheses that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. If computational results do not match experimental or previously published results, then new hypotheses are formed and they feed back into the data-mining and analyzing step to refine the biomolecuar network for the next iteration. This is repeated until a good match between modeling and data is obtained. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the subnetwork clustering, which are both essential to make the solution tractable. Experimental results on human gene interaction networks and gene expression time series data for the human cell cycle indicate that our approach is promising for subnetwork mining and simulation from large biomolecular networks, as it produces a better convergence between continuous modeling and experiments. http://idea.library.drexel.edu/handle/1860/2695 Title: A hybrid intelligent system to diagnose and indicate solutions to financial problems <br/> <br/>Authors: Pacheco, Roberto; Martins, Alejandro; Weber, Rosina O.; Barcia, Ricardo M.; Khator, Suresh <br/> <br/>Abstract: Monitoring and adjusting financial health problems play central role in the firm’s performance. Usually, small firms face difficulties in these tasks for lacking human resources and incapacity to afford a consultant. By analyzing the nature of the diagnosis and the solution of financial problems one can identify two different reasoning: during the diagnosis phase, the process is basically intuitive due to complex relations between financial ratios and problems; in the solution phase, the analyst follows a deductive approach searching for the causes and adjustments to the identified problem. Based on these aspects and on the motivation of providing a computational system to aid small firms, we built a Hybrid Intelligent System to diagnose (through Neural Network) and indicate solutions (through Expert System) to financial problems. In this paper we discuss the nature of the problems and present the system. http://idea.library.drexel.edu/handle/1860/2694 Title: Case-based reasoning approach to reuse of experiential knowledge in software measurement programs <br/> <br/>Authors: Gresse von Wangenheim, Christiane; Ramos, Alexandre Moraes; Althoff, Klaus-Dieter; Barcia, Ricardo M.; Weber, Rosina O.; Martins, Alejandro <br/> <br/>Abstract: For the successful application of innovative software engineering technologies in industry, the technologies have to evolve incrementally based on continuous feedback from practice. Experiences about their practical application have to be systematically collected and stored in corporate memories and reused in future software projects. This promotes the sharing of experiences across individuals and projects, the formulation of best practices and facilitates the successful application of tailored technologies in practice. This paper presents a case-based reasoning approach for capturing and reusing experiential knowledge on software measurement programs in industry. A representation structure for experiential measurement knowledge is described in detail and knowledge retrieval and acquisition techniques are presented.